Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement

  1. Catalin Mitelut  Is a corresponding author
  2. Yongxu Zhang
  3. Yuki Sekino
  4. Jamie D Boyd
  5. Federico Bollanos
  6. Nicholas V Swindale
  7. Greg Silasi
  8. Shreya Saxena
  9. Timothy H Murphy  Is a corresponding author
  1. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Canada
  2. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Canada
  3. Department of Ophthalmology and Visual Sciences, University of British Columbia, Canada
  4. Biozentrum, Centre for Molecular Life Sciences, University of Basel, Switzerland
  5. Department of Engineering, University of Florida, United States
  6. Department of Cellular and Molecular Medicine, University of Ottawa, Canada

Abstract

Volition – the sense of control or agency over one’s voluntary actions – is widely recognized as the basis of both human subjective experience and natural behavior in nonhuman animals. Several human studies have found peaks in neural activity preceding voluntary actions, for example the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. Others propose that random processes underlie and explain pre-movement neural activity. Here, we seek to address these issues by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. Implementing a self-initiated water-rewarded lever-pull paradigm in mice while recording widefield [Ca++] neural activity we find that cortical activity changes in variance seconds prior to movement and that upcoming lever pulls could be predicted between 3 and 5 s (or more in some cases) prior to movement. We found inhibition of motor cortex starting at approximately 5 s prior to lever pulls and activation of motor cortex starting at approximately 2 s prior to a random unrewarded left limb movement. We show that mice, like humans, are biased toward commencing self-initiated actions during specific phases of neural activity but that the pre-movement neural code changes over time in some mice and is widely distributed as behavior prediction improved when using all vs. single cortical areas. These findings support the presence of structured multi-second neural dynamics preceding self-initiated action beyond that expected from random processes. Our results also suggest that neural mechanisms underlying self-initiated action could be preserved between mice and humans.

Editor's evaluation

This study is a valuable work that advances our knowledge of the neural correlates of voluntary action through a wide range of methods. The evidence supporting their conclusion is convincing, and the results will be of interest to a large class of neuroscientists interested in the neural mechanisms underlying self-initiated actions.

https://doi.org/10.7554/eLife.76506.sa0

Introduction

Over the past several decades studies of volitional, that is free and voluntary, action in humans using self-initiated (i.e., uncued) behaviors such as flexing a finger or pressing a button have shown that prior to movement there is a gradual increase in scalp electroencephalography (EEG) signal over pre- and supplementary motor area (pre-SMA and SMA, respectively; Ball et al., 1999; Cunnington et al., 2002). This increase in activity is known as the ‘readiness potential’ (RP; Kornhuber and Deecke, 1964; Deecke et al., 1976; Deecke and Kornhuber, 1978; Libet et al., 1983; Shibasaki and Hallett, 2006) and has received increasing attention with some interpretations that it is evidence that voluntary decisions might be made prior to awareness with several studies replicating and extending the original work (Haggard and Eimer, 1999; Schlegel et al., 2013; Sirigu et al., 2004; Alexander et al., 2016). Additionally, single neuron physiology studies have also shown a significant increase (or decrease) in the firing rate of single neurons in SMA and pre-SMA (as well as anterior cingulate cortex) prior to movement (Fried et al., 2011). In parallel, human functional magnetic resonance imaging (fMRI) studies have shown that upcoming behaviors could be decoded up to several seconds prior to movement (Soon et al., 2008; Soon et al., 2013; Bode et al., 2011; Colas and Hsieh, 2014). The role of pre-movement neural activity in voluntary behavior is the subject of active debates on human decision making including free will (Jahanshahi and Hallett, 2003; Lang, 2003; Shibasaki and Hallett, 2006; Haggard, 2008; Klemm, 2010; Custers and Aarts, 2010; Schurger et al., 2012; Deecke, 2012; Guggisberg and Mottaz, 2013; Bode et al., 2014; Maoz et al., 2015; Lavazza, 2016; Schurger et al., 2016). These debates on the neural genesis of voluntary action are further complicated as other studies have shown volitional actions are more likely to occur during certain phases of breathing (e.g., exhalation; Park et al., 2020), or phases in cumulative neural activity (i.e., the crest in slow cortical potential – SCP; Schmidt et al., 2016) which have no immediately obvious connections to volitional intent, awareness, neural noise, or external stimuli or cues.

Although some have called for better-designed self-initiated behavior studies (e.g., Mudrik et al., 2020), it remains challenging to implement them in humans especially in cue-free paradigms. First, neuroanatomically precise high-temporal and spatial precision recordings from many cortical areas are rare in humans (though some limited studies exist, e.g., Fried et al., 2011). Second, obtaining statistically sufficient numbers of trials (i.e., tracking behaviors for days or weeks; see Bode et al., 2014 for a discussion) of higher-value salient actions (e.g., important decisions that are made naturally outside of laboratory environments) is not yet possible in humans. Additionally, human laboratory protocols for volitional studies (e.g., the subject being told to act freely in a study) may result in instructed – rather than free behavior – and there are concerns about whether human subjects can act randomly (Lages et al., 2013) or otherwise carry out balanced behaviors (e.g., randomly pressing left vs. right button) in voluntary behavior paradigms (Bode et al., 2014). An alternative approach to studying self-initiated action is to characterize the neural correlates of voluntary action and removing the requirement for reporting intent or awareness altogether (similar to some human studies, e.g., Soon et al., 2008; Bode et al., 2011). This avoids some of the challenges of human paradigms and makes it possible to implement nonhuman animal models where more ethologically valuable actions could be available (i.e., food or water seeking behaviors) and higher-resolution intracranial neural recordings can be made during hundreds or thousands of trials. There is evidence to support this direction as several nonhuman studies have identified structure – or increases – in pre-movement neural activity in nonhuman primates (Romo and Schultz, 1986; Romo and Schultz, 1990; Coe et al., 2002; Lee and Assad, 2003; Maimon and Assad, 2006; Ding and Hikosaka, 2006), rodents (Hyland, 1998; Isomura et al., 2013; Murakami et al., 2014), crayfish (Kagaya and Takahata, 2010), and zebrafish (Lin et al., 2020). However, none of these studies were designed to – nor report – the neural structure of either voluntary or self-initiated actions and they do not evaluate the predictive relationship between pre-movement cortical neural activity and self-initiated behaviors.

Here, we report results obtained from a self-initiated behavior paradigm targeting the decoding of future body movement and rewarded actions from neural activity in mice. Using a self-initiated task and widefield [Ca++] cortical imaging (Silasi et al., 2016; Vanni and Murphy, 2014), we tracked both water-rewarded lever-pull behavior of water deprived mice and spontaneous body movements. We gathered hundreds to thousands of self-initiated actions over months of recordings and collected neural activity from several cortical areas. We find that both self-initiated water-rewarded lever pulls and spontaneous body movements could be decoded above chance a few to several seconds prior to movement initiation from neural activity. We show the self-initiated movement neural code is distributed across multiple cortical areas and additionally replicate and extend several findings from human studies. Our study is in line with accounts of pre-movement neural activity having temporal and spatial structure beyond that present in random neural dynamics and supports a causal role between pre-movement neural activity and action that is on the scale of several seconds prior to action.

Results

Several studies of voluntary actions, such as finger or wrist movements, in humans have identified an increase in scalp EEG signal over SMA and pre-SMA – known as the RP – occurring 0.5–1.5 s prior to movement and in some cases awareness of movement (Figure 1a, b; Kornhuber and Deecke, 1964; Kornhuber and Deecke, 1965; Libet et al., 1983; Ball et al., 1999; Cunnington et al., 2002).

Figure 1 with 7 supplements see all
Tracking and decoding self-initiated behaviors from widefield neural activity in mice .

Detecting and decoding upcoming self-initiated mouse behaviors via widefield calcium activity. (a) Human voluntary behavior studies using scalp electroencephalography (EEG) target motor cortex and related areas. (b) Human voluntary wrist flexion studies recording EEG from motor areas (as in (a)) reveal a change in neural activity 0.5–1.5 s prior to behavior initiation (t = 0 s) or even awareness (t = W) in some studies (note: the y-axis of the plot is inverted so the readiness potential [RP] is negative). (c) Mice learn to voluntarily self-initiate a lever pull to receive water reward while widefield [Ca++] activity is captured at 30 Hz. (d) Allen Brain Atlas and locaNMF decomposition of neural activity into neuroanatomical areas (see also Methods). (e) The average motor cortex widefield calcium (neural) activity (solid blue line) becomes increasingly stereotyped prior to a self-initiated lever pull (t = 0 s; data shown are from 21 s locked out trials; dashed line represents Hilbert transform of oscillatory signal). (f) Human studies seeking to decode voluntary choice relying on functional magnetic resonance imaging (fMRI) during voluntary behaviors. (g) Decoding accuracy for left- vs. right-hand voluntary button presses (solid purple line) is a few percent above chance several seconds prior to movement initiation and peaks at approximately 10% above chance at movement time. (h) Two examples of decoding accuracy for rewarded lever pull vs. random states in mice (solid blue lines; shading is the standard deviation of 10-fold cross-validation accuracy) showing increases seconds prior to movement and peaking >30% above chance at movement time (data shown for two examples of long earliest decoding times (EDTs) obtained from mice M2 and M3 using 15 s locked out low-pass filtered trials; see Main text). (b) has been adapted from Box 1 in Haggard, 2008. (g) Has been adapted from Figure 2 in Soon et al., 2008.

Self-initiated movements in mice are preceded by stereotyped neural activity changes several seconds prior to movement

We developed an analogous self-initiated behavior paradigm in six mice (M1–M6) to characterize pre-movement neural activity while recording widefield [Ca++] activity from cortex (Figure 1c, d, Figure 1—figure supplements 1 and 2; see also Methods; see also Table 1). Mice were headfixed and trained to perform a self-initiated lever pull to receive a water reward without sensory cues or stimuli. Four of six mice learned the lever lockout period of 3 s (Figure 1—figure supplement 2 shows peaks at 3 s in the inter-lever-pull intervals in mice M3–M6) and four of six mice learned to pull increasingly more often toward the end of each ~20-min session (Figure 1—figure supplement 4, mice). Mice tended to decrease their body movements prior to a lever pull and we did not find evidence of stereotyped behaviors prior to lever pull (Figure 1—figure supplement 5; see also Methods on detecting stereotyped movements). Similar to the RP in humans, self-initiated behaviors in mice are preceded by an increasingly stereotyped average widefield [Ca++] signal up to 5 s or earlier in several areas including motor and limb cortex (Figure 1e, Figure 1—figure supplement 3). This common dynamical pattern was observed in all sessions and animals but not when considering random segments of neural activity (Figure 1—figure supplement 3).

Table 1
Lever-pull statistics.
Animal ID# of behavior sessions*# of video sessions*Median # rewarded pulls/sessionMedian # unrewarded pulls/session
M169301614
M242123414
M3421142135
M4461033111
M5421138116
M6109704535
  1. *

    Total numbers reported include also rejected sessions due to insufficient trials (see also Methods).

Self-initiated movements in mice can be decoded seconds prior to action from preceding neural activity

In addition to the human RP, several human fMRI studies have shown that voluntary behavior could also be decoded up to several seconds prior to movement, usually a few percent above chance (Figure 1f, g; Soon et al., 2008; Soon et al., 2013; Bode et al., 2011). To compare with human studies, we trained support vector machines (SVMs) using trials within each session to decode upcoming rewarded lever pulls (Figure 1h) or spontaneous limb movements (Figure 1i, j). We decoded, that is classified, (1) neural activity preceding a self-initiated action (e.g., rewarded lever pull) vs. (2) neural activity representing random periods of behavior similar to two class voluntary choice decoding carried out in humans (e.g., Soon et al., 2008; note: we defined random activity as continuous segments of neural data that were centered at least 3 s outside of lever-pull times; see Methods). Within each session we used sliding windows of 1 s of neural activity as input to the SVMs. Additionally, we trained markerless pose estimation methods to track the spontaneous limb movements of mice (see Methods). To disambiguate the effects of multiple sequential lever pulls, we considered only lever pulls that were preceded by at least 15 s of no-lever-pull activity (see further results below and Methods on lever lockout analysis). Upcoming rewarded lever pulls could be decoded several seconds prior to movement with decoding accuracy curves improving closer to the lever-pull time (examples in Figure 1h). Similarly, spontaneous limb movements were also decodable above chance a few seconds prior to movement (examples in Figure 1i, j).

In sum, neural activity preceding self-initiated lever pulls in mice is preceded by multi-second stereotyped changes in widefield [Ca++] cortical activity similar to the human RP. Similar to human fMRI results, upcoming lever pulls or spontaneous limb movements could be decoded from preceding neural activity with similar decoding accuracy previously reported in humans (e.g., Soon et al., 2008; Bode et al., 2011). Our findings suggest that, like humans, mice may also engage pre-movement neural dynamics spanning several seconds prior to self-initiated action.

We sought to systematically evaluate decoding accuracy for upcoming behaviors across mice, by evaluating multiple cortical anatomical areas and for different movements (e.g., water-rewarded lever pulls or spontaneous limb movements; Figure 2).

Figure 2 with 4 supplements see all
A cortex-wide distributed multi-second neural code underlies self-initiated actions .

Decoding self-initiated lever pulls using cortical neural activity. (a) Decoding self-initiated water-rewarded lever pulls using a minimum 3-s lockout window and a minimum lever angle threshold (see also Methods). (b) Support vector machine (SVM) decoding accuracy curves of two different sessions (mouse M4) reveal increased decoding accuracy near lever-pull time and an earliest decoding time (EDT) of several seconds (curves represent average accuracy and shaded colored regions represent standard deviation over 10-fold cross-validation; top colored bars represent p values of Student t-test with a Benjamini–Hockberg correction for multiple hypotheses; see Methods for more details; note sessions shown were atypical and were selected to illustrate decoding curves for early EDTs when decoding from all trials, that is without locking out previous lever pulls). (c) EDTs from all sessions (mouse M6) show a strong correlation between EDT and the number of trials within a session (lighter colors indicate earlier sessions in the experiment). (d) Same as (b) but for an example from concatenated, that is multi-session, analysis. (e) EDTs for concatenated sessions (M6) also show a correlation between EDT and the number of trials present in the session and the EDT (lighter shading representing earlier sessions in training). (f) EDT distributions across all mice for single session trials (blue) vs. multi-session trials (red) reveals a significant lengthening (i.e., further from lever-pull time) in EDTs for multi-sessions across all animals (cyan box plots show 25th percentile, median, and 75th percentile; comparisons between single vs. concatenated sessions were carried out using two-sample KS test with asterisks indicating: *<0.05; **<0.01; ***<0.001; ****<0.0001; *****<0.00001). (g) Same as (f) but for 15-s lockout trials concatenated across multiple sessions. (h) Recomputed EDTs for filtered calcium traces. (i) Examples of decoding accuracy curves for 15-s lockout trials using filtered vs. non-filtered neural time series (see also Methods).

Decoding future rewarded lever pulls seconds prior to movement

We next focused on decoding water-rewarded lever pulls: that is, lever pulls that reached a minimum lever angle and were not preceded by a previous lever pull for at least 3 s (Figure 2a; Figure 2—figure supplement 1; see also Figure 4 and Methods). Going back in time from t = 0 s, we defined the earliest decoding time (EDT) as the last point in time at which the SVM accuracy was statistically higher than chance (Figure 2b, 10-fold cross-validation pval <0.05, Student 1 sample t-test corrected for multiple hypotheses using the Benjamini–Hochberg method; see Methods). The decoding accuracy was better than chance seconds prior to movement and gradually increased closer to the lever pull (i.e., t = 0 s). EDTs ranged from 0 s (i.e., lever pull was not predicted) to more than 13 s in some sessions (see Figure 2c for example EDTs). We also found a correlation between the number of trials within a session and EDT suggesting that EDTs could in principle be even lower (i.e., earlier decoding in time) than reported in our study (Figure 2c linear fit; note each EDT was computed from a single session; see also Methods). To evaluate this correlation, trials from sequential sessions were concatenated to obtain at least 200 cumulative trials resulting in improvement in EDT (Figure 2d, e). Pooled sessions EDTs were lower for all mice (Figure 2f – left panel; pvals <0.01 for all animals; single session averages in seconds: M1: −2.91; M2: −2.87; M3: −4.75; M4: −3.55; M5: −4.32; M6: −6.91; concatenated averages in seconds: M1: −4.84; M2: −5.47; M3: −7.58; M4: −5.76; M5: −6.99; M6: −6.92).

Given the strong dependence of EDT on the # of trials, we sought to re-evaluate EDTs using only trials that were not preceded by another lever pull (either rewarded or non-rewarded). We find that oscillations observed in the neural data are likely enforced by repetitive and stereotyped recent lever pulls and that EDT analysis requires exclusion of trial that occur too soon after a previous lever pull (here we chose a lockout of 15 s; note: this approach significantly decreased the number of trials available for analysis as mice only rarely went without pulling the lever for 15 s; we thus pooled trials from across sessions into a minimum of 50 to a maximum of 200 trial hybrid sessions; see Methods). We found that after lockout the neural data had a single negative (i.e., inhibitory) phase preceding self-initiated rewarded lever pulls that comenced ~5 s prior (similar to Figure 1e; see Figure 2—figure supplement 2). We additionally found that EDTs decoded from lockout trials were shorter (Figure 2g: average EDT in seconds: mouse 1 (M1): −1.93; M2: −3.14; M3: −2.27; M4: −1.87; M5: −1.64; M6: −2.49). However, low-pass filtering the neural time series (at 0.3 Hz) (as a type of feature engineering based on power analysis results in Figure 7) resulted in EDTs more similar to the initial results (Figure 2; average EDTs of causal filtered neural data in seconds: M1: −3.5; M2: −4.85; M3: −6.95; M4: −4.31; M5: −3.0; M6: 3.7). The improvement in EDT was qualitatively observable in decoding accuracy curves (Figure 2i) and was present even for non-lock out trials (see Figure 2—figure supplement 4; see also Methods).

The initial loss of EDT (without the filtering step) suggests that sequential lever pulls might have a causal role in lengthening EDTs by generating stereotyped neural time series which represents preceding – not just the current – rewarded lever pulls. However, we also found that pooling trials from sessions far apart in time (days or weeks) as required by the lockout method also shortened EDT values (i.e., closer to 0 s; Figure 2—figure supplement 3). This suggests that higher data variance (due to learning, [Ca] bleaching, implant degradation, etc.) might also have a causal role in shortening EDTs. Overall, these findings show that self-initiated water-rewarded lever pulls in mice can have neural correlates that are present up to several seconds prior to lever pull and can be decoded several seconds prior to level pulls, but that such analysis must appropriately take into account previous behaviors and the effects of data variance over longitudinal studies.

Preparation of upcoming lever pulls is widely distributed across the cortex

We next evaluated decoding of upcoming lever pulls using individual cortical areas rather than the entire dorsal cortex (Figure 2i). Anatomically informed components were obtained using LocaNMF (Saxena et al., 2020; see Methods) and EDTs were computed for bilateral activity from: retrosplenial, somatosensory-barrel, somatosensory-limb, visual, and motor cortex. Somatosensory-limb cortex was generally the most informative of upcoming lever pulls (i.e., lowest mean EDTs across all mice) followed by motor cortex; visual cortex-based decoding had the highest EDTs (i.e., closest to lever-pull time t = 0 s). More importantly, using all regions for decoding yielded lower EDTs than using somatosensory-limb cortex alone (two sample KS test comparing limb-cortex vs. all neural regions EDTS; D-statistic for all mice: 1.0; p values: M1: 0.0; M2: 5.55e−16; M3: 2.90e−22; M4: 2.77e−19; M5:4.52e−21; M6: 2.22e−16). These findings are consistent with a single neuron study in humans that showed pooling neurons yielded increased decoding accuracy of upcoming voluntary action when compared to single neuron decoding alone (Fried et al., 2011) but our results yield earlier decoding times than previously shown in humans.

EDTs of non-locked out paw movements and licking events are similar to those for lever pulls

Most human studies on self-initiated voluntary behavior employ simple behaviors such as the flexing of a finger or pressing of a button with a specific hand (e.g., Libet et al., 1983; Soon et al., 2008). Accordingly, we also sought to determine whether mouse spontaneous paw movements (not just those related to water-rewarded lever pulls) could be decoded from preceding neural activity (see Methods for description of body movement tracking methods). Briefly, we defined a self-initiated body movement as the time when the body part increased its velocity to more than 1× the standard deviation of all movement within the session (we also implemented a 3-s non-movement lockout period as in human studies and as in the preceding section). SVMs were trained as for rewarded lever-pull times but using the body movement initiation time (i.e., t = 0). As for self-initiated lever pulls, a strong correlation was present between the number of trials within a session and the EDT suggesting that with higher number of body movements (e.g., longer sessions) EDTs could be even lower (Figure 2, figure supplement 4). Across all animals, upcoming body movements could be predicted above chance a few seconds prior to movement in the vast majority of sessions and in some cases more than 10 s prior to movement (Figure 2l). Importantly, with a few exceptions, licking or paw movements EDT distributions were not statistically different from lever pull time ETDs, however, due to the high correlation between paw movements and licking to lever-pull times – we do not view them as completely independent analyses. Importantly, as we show below (see Figure 8) when considering only body movements that are isolated from lever pulls (i.e., not preceded by a lever pull in the previous 15 s, or following 5 s) and also not preceded by other body movements for at least 5 s, we find that [Ca] averages show an increase in motor cortex (and other areas) and that EDTs are near 0 s as the signal is too noisy to enable decoding (see also Methods and Discussion).

In sum, upcoming self-initiated behaviors in mice can be decoded above chance several seconds prior to movement for all animals with a strong dependence of the EDT on the number of trials present in each session. While sequential stereotyped pulls have the effect of lengthening EDTs, longitudinal changes in the neural recordings have the effect of shortening EDTs. Single anatomical area analysis revealed that somatosensory-limb cortex contained the most information about upcoming movements but that decoding information was distributed across multiple regions of cortex.

Studies of human voluntary behavior have shown that SCPs, that is slowly changing voltages measured usually via EEG <1 Hz, might be involved in modulating voluntary behavior (Jo et al., 2013; Schmidt et al., 2016). In particular, voluntary behavior was found more likely to occur (on the order of ~10%) when the SCP phase over motor areas was near the crest. Additionally, there is some evidence to support that the SCP is related to awareness or consciousness and may play a causal role in internal state driven action (He and Raichle, 2009; Northoff, 2017). Given these findings we sought to determine whether self-initiated lever pulls in mice co-occurred with specific phases of widefield [Ca++] activity (Figure 3).

Figure 3 with 2 supplements see all
Self-initiated lever pulls occur during narrowly distributed slow-oscillation phases.

Self-initiated behaviors occur during specific phases of slow-oscillations. (a) Top: single-trial neural activity (gray curves) from 33 trials in a single session (M4) for somatosensory-upper left limb cortex contain oscillations that become increasingly stereotyped closer to lever-pull time (t = 0 s; thick black curve is session average; inset shows anatomical area selected); Bottom: single-trial sinusoidal fits (thin pink curves) to neural activity (in Top) and phases (scatter dots on the t = 0 s line; thick pink curve is session average). (b) Polar plot of results in (a) showing the distribution of sinusoidal fit phases at t = 0 s. (c) Same as (b) but for random periods of neural activity (i.e., not locked to any behavior). (d) Same as (b) but for all sessions in mouse M4. (e) Same as (d) but for random periods of neural activity across all sessions in mouse M4. (f) Probability of voluntary action in humans during various phases of the slow cortical potential (SCP). (g) Same as (f) but widefield [Ca++] from the motor cortex in mice M1 and M6. (h) Phase distributions across all mice, sessions, and areas. (i) SCP phase distributions for random segments of neural activity for all areas in mouse M4. (j) Single-trial pairwise correlation between all cortical areas (e.g., limb vs. motor, limb vs. retrosplenial, etc.). (f) Has been adapted from Figure 2 from Schmidt et al., 2016. (pval * same as in Figure 2—figure supplement 3).

© 2016, Elsevier. Figre 3f is reproduced from Figure 2 from Schmidt et al., 2016, with permission from Elsevier (copyright year 2016, copyright holder Elsevier). It is not covered by the CC-BY 4.0 licence and further reproduction of this panel would need permission from the copyright holder

Lever pulls occur during narrowly distributed phases of neural activity

As shown above (see Figure 1, Figure 1—figure supplement 3) within single cortical areas, the neural dynamics preceding rewarded lever pulls become increasingly stereotyped closer to t = 0 s (Figure 3a – top for an example of left forelimb neural activity). Fitting sinusoids to the last 5-s period prior to movement in each trial yielded sinusoidal fits with very similar phases at t = 0 s (Figure 3a – bottom). Neither of these stereotyped dynamics were present in random segments on neural activity (Figure 3—figure supplement 1). The phase distribution for a single session and cortical area was narrowly distributed with most phases falling in a <90° wide window in many sessions (and <45° for some sessions) (see Figure 3b for example distribution from phases in Figure 3a; see also Figure 3—figure supplement 2 for examples from all cortical areas). In contrast, random segments of neural activity had widely distributed phases (Figure 3c, Figure 3—figure supplement 2). Computing the t = 0 s phases for all trials across all sessions also revealed narrowly distributed phases (Figure 3d) in contrast to random segments of neural activity which yielded an approximately uniform distribution (Figure 3e). Similar human studies where the most likely SCP phase during voluntary action was the crest phase with ~30% probability followed by the rising phase (Figure 3f for an adapted example), phases in mice were also biased to these two locations (i.e., crest or rising phase). In contrast to human results, we found an even higher bias as some mice had more than a 50% probability of initiating action in the crest phase – while others preferred the rising phase (see Figure 3g for two examples). The phases for all mice, sessions, and trials showed that virtually all mice and cortical areas had narrowly distributed phases (Figure 3h) significantly different than random segments of neural activity (Figure 3i; Rayleigh test for uniformity <<1E−5 for all animals and areas). The diversity of phase preferences was present not only between mice but also within mice as the phase bias could be significantly different between cortical areas (e.g., Figure 3h, mouse M1 limb vs. motor cortex phase differences). Lastly, the median inter-area phase correlation varied with some mice having highly correlated phases across areas whereas others having mostly low correlations (Figure 3j; see also Methods).

In sum, the phases of neural activity at lever-pull initiation from all areas were significantly stereotyped, consistent with human findings (Jo et al., 2013; Schmidt et al., 2016). In contrast to human findings we found a higher bias of phases: that is, behaviors were even more likely to occur during a specific phase in mice than in humans. These findings support the presence of biases in neuroanatomical dynamics preceding self-initiated behavior preparation while confirming inter-animal differences in both anatomy and dynamics observed in other findings (see also Figures 4 and 5).

Changes in decoding and neural dynamics over weeks of task performance.

Tracking changes in decoding performance and spontaneous neural dynamics over time. (a) The number of rewarded lever pulls per session in all mice over the duration of the experiment. (b) Support vector machine (SVM) decoding accuracy curves from M1 and M2. (c) Earliest decoding times (EDTs) (black dots) for all mice (using 15-s lockout trials only black lines: linear fits; inset: Pearson correlation coefficient; note: the EDTs were computed on non-lockout data). (d) Convex hull of neural activity preceding lever pulls from 30 trials from a single session in mouse M3. The colored scatter points represent neural activity at various frames in the −1 to 0 s period and the polygons represent the convex hull of neural activity for all data (black), t = 0 s (blue) and t = −1 s to t = 0 s (red; see also legend for details). (e) Same as (d) but for 1 s segments ranging from −10 s to t = 0 s. (f) Convex hull volume (red line; shading represents area under the ratio curve [AUC]) of pre-pulls vs. random segments (black line; shading represents 10-fold sampling standard deviation; see also Methods). (g) AUC (scatter points) of ratio curves (as in (f)) for all animals and sessions. (h) Convex hull of 1 s of neural activity preceding body movements (red and blue polygons), lever pull (magenta polygons), and all neural data (black polygons) at different longitudinal time points in the experiment for mouse M6. Brown shaded regions represent the overlap between the right paw movement initiations and lever pull. (i) Ratio of convex hull of lever pull to all neural activity (magenta scatter points), linear fit (black line), and Pearson correlation value (inset). (j) Intersection of right paw and lever-pull initiation space (brown scatter points), linear fit (black lines), and Pearson correlation (inset).

Single-trial variance changes seconds prior to self-initiated lever pulls.

Internal-state evaluations begin several seconds prior to self-initiated lever pulls. (a) Average session neural activity from cortical areas (colored curves) and average over all sessions (red curve) from mouse M3. (b) Variance of data in (a) and the earliest variance decrease time (EVDT; black dots represent EVDT of all session averages, i.e., red curves in (a)). (c) EVDTs for all animals and sessions.

We next sought to determine whether learning or mere longitudinal performance of the task changed the decoding accuracy or cortical neural dynamics during voluntary behavior initiations (Figure 4).

Longitudinal cortical network dynamics support shortening of EDTs

Considering the number of rewarded lever pulls per session, we found that between the first and last days of the experiment three of the mice (M2, M3, and M6) increased the number of rewarded lever pulls while one additional mouse (M5) also had a positive trend (with pval of 0.13); one of the mice (M1) decreased its number of pulls per day and the remaining mouse did not have statistically significant changes (Figure 4a; Pearson correlation values provided in figure insets; see Methods). We labeled the four mice with either a strong or a trend in positive correlation over time as the ‘performer’ group (M2, M3, M5, and M6) and the remaining mice (M1 and M2) as ‘non-performers’ as they did not increase their pulls over time. Given the potential confounds identified in Figure 2 between sequential lever pulls and decoding time, further analysis in this section focused primarily on 15-s lockout trials only (see Figure 2). We found that SVM decoding accuracy curves over the weeks or months of behavior revealed potential trends over time (Figure 4b for examples from two mice). In particular, we found that EDTs shortened (i.e., were closer to the lever-pull time) over time in two mice (M1 and M6); a similar trend was present in another mouse (M5; pval 0.06); while the remaining three mice did not show statistically significant trends. Although only statistically significant in two mice, shortening of EDT decoding time trends may be explained by automaticity findings in other studies: that is, that following learning and repetitive behavior performance, the control of behavior is transferred from cortical structures (which we had access to during widefield [Ca] imaging) to subcortical structures (that we could not access in our paradigm; another explanation could be that implants slowly degraded) (see e.g. Ashby et al., 2010; see also Discussion).

Lever-pull neural activity space increases its cortical representation over time in some mice

We implemented a convex-hull-based analysis to capture how the neural activity space prior to lever pulls changed over weeks or months of performance (Figure 4d–g; see Methods). For each session, the neural activity convex hull at lever-pull time (i.e., t = 0 s) was defined as the hyper-volume that enclosed the t = 0 s neural activity vectors relative to all the neural activity vectors in the session (note: as convex hull analysis is sensitive to outliers a 10% K-nearest-neighbor triage was implemented prior to evaluation; see Methods). The convex hull could be visualized in two dimensions using principal component analysis (PCA) as the area enclosing the t = 0 s neural activity vectors for lever pulls in that session (Figure 4d – blue dots; see also Methods). Both the convex hull of the t = 0 s and t = −1 s period prior to lever pull occupied a small subspace within the entire neural space of all the activity in the session (Figure 4d – colored dots; see Methods). Going backwards in time, the convex hull space gradually increased further away from t = 0 s (Figure 4e). This suggests that neural activity looks more like spontaneous (i.e., random) activity further in time from lever pulls and becomes more stereotyped toward t = 0 s. The ratio of the pre-pull convex hull to the hull of the entire session was smaller than the hull computed from random trigger times (Figure 4f).

Lastly, we evaluated the area under the ratio curve (AUC) longitudinally to evaluate whether there are systematic changes in the neural activity convex hull over weeks of behavior performance (Figure 4g). We carried out this analysis using only 15-s lockout data grouped in sessions of up to 200 trials (similar to carried out above to exclude any possible trends arising from increased intra-session lever pulls or effects of sequential lever pulls; see Methods). We found that, as in the EDT longitudinal trends, two mice (M1 and M6) that had decreasing EDTs (i.e., poorer decoding over time) also had an increased similarity (i.e., increased AUC values) between lever-pull dynamics and random neural states. Considering only these two mice (as they were the only statistically significant results), one explanation may be that cortical dynamics may return to pre-lever-pull learning patterns and look increasingly the same as random neural states (occurring near or far from behaviors) because subcortical circuits increasingly facilitate and ‘take over’ self-initiated behavior preparation during automaticity processes.

Neural activity space of right paw movements and lever pulls change systematically over time in some mice

Given the findings above we sought to further evaluate systematic changes between lever-pull and random neural activity (Figure 4h–j). We recomputed convex hulls for the 1-s period prior to lever pull, 1-s period prior to left and right paw movements and found that the size of the lever-pull convex hulls and its overlap with the rest of the behaviors changed over time (Figure 4h). While five of the six mice had increasing convex hulls, the trends were statistically significant in only two of the mice, which again, were mice M1 and M6 (although mouse M3 also showed a similar trend, p value: 0.14) (Figure 4i). Interestingly, mouse M4 showed a decrease in overlap of lever dynamics with all dynamics. These mixed results suggest that different mouse-specific mesoscale neural representations may be involved during learning and performing of a task.

We also found mixed results with respect to the intersection between the right paw convex hull, that is the paw used to pull the lever, and the lever-pull convex hulls: the overlap decreased with time in two of the mice (M4 and M6) and showed a similar trend in another mouse (M3; pval 0.09); and it increased with time in mouse M1. These results suggest that in some mice (M4, M6, and possibly M3) lever-pull neural dynamics increasingly specialize or differentiate from non-lever-pull right paw movements neural dynamics (despite the right paw being used for the lever-pull task). In contrast, in one mouse the similarity between right paw movements and lever pulls increased (e.g., mouse M1), but this could be explained by this mouse being a behavior outlier as the only mouse with decreased number of rewarded lever pulls over time.

In sum, EDTs shortened longitudinally in some mice suggesting neural dynamics underlying self-initiated behavior might be transferred from cortical to subcortical circuits decreasing the power of cortical-based decoding methods. The convex hull of the neural activity prior to self-initiated lever pulls also increased over time in some mice with a potential explanation that cortical dynamics return to pre-learning similarity over time. Lastly, right paw and lever dynamics appeared to become increasingly dissimilar in a few mice, with one mouse showing the opposite trend. These findings suggest that learning or mere longitudinal performance of a task restructures the neural dynamics underlying self-initiated action but that the effects could be subject specific, drawing attention to the need for intra-animal analyses (rather than cohort) in future studies.

Over the past few decades, one of the most robust findings in stimulus cued decision making studies has been that stimulus onset decreases neural variability in a wide range of paradigms (see Churchland et al., 2010 for a summary). This decrease in variance, also evaluated as the fano factor (i.e., the ratio of variance to mean of neural activity) has been interpreted to suggest that incoming information (i.e., stimuli) ‘stabilizes’ the state of cortical activity (e.g., decreases the variance of membrane potential fluctuations, spiking variance, or correlated spiking variability) and potentially supports the accumulation of internal memory evidence (Ratcliff, 1978; Ratcliff and McKoon, 2008). Here, we sought to determine whether neural activity preceding self-initiated lever pulls exhibited a change in variance prior to the decision to pull, potentially reflecting the commencement of evaluation of ‘internal-state’ evidence and/or the preparation of a skilled action (Figure 5).

Single-trial variance decreases seconds prior to self-initiated action

As shown above for single trials (Figure 3a), the average trial activity within a session becomes increasingly stereotyped near t = 0 s. This stereotypy can be observed in all major cortical areas even across weeks or months of behavior (Figure 5a). Interestingly, the variance was also stereotyped with a significant change (decrease in five mice; increase in one mouse: M2) several seconds prior to the movement-related areas (e.g., motor, somatosensory, and retrosplenial) but less so in areas not directly related to movement preparation (e.g., visual cortex; Figure 5b). We defined the earliest variance decrease time (EVDT; see Methods) for each session as the time at which the variance decreased (or increased for mouse M2) by two times the standard deviation from a random period of time (Figure 5b – black dots; see Methods). Computing the EVDTs for all sessions and mice revealed that in most areas and animals the neural activity variance began to decrease a few to several seconds prior to lever-pull initiation. While these measurements were noisy (see Methods), in mice with significant numbers of detected EVDTs (i.e., mice M3, M4, M5, and M6) the average EVDT for all areas was around −3 s or earlier. Such decreases in variance seconds prior to behavior initiation may represent the times at which internal state evaluations and motor preparation commences (see also Discussion; we note that this analysis was not possible using lockout trials due to significantly higher intra-session variance caused by pooling data from across sessions days and weeks apart; given EDTs ranging from approximately −3 to −7 s for lockout data we anticipate that given sufficient numbers of 15-s lockout trials we would obtain similar or shorter EVDT times as in non-lockout data).

In sum, we found that in most mice and sessions, variance across all areas (excluding visual cortex) began to change several seconds prior to lever-pull time. These results are consistent with and support our prior findings and could be interpreted to suggest that internal-state driven behaviors are underpinned by neural processes similar to those observed in stimulus driven decision making studies (and as observed in Murakami et al., 2014 in neural activity preceding a self-paced task).

An outstanding question in voluntary behavior neuroethology is how confounds, such as random body movements occurring in the period prior to a targeted action, affect behavior preparation and – in our paradigm – the decoding of future behaviors. For example, in animals that perseverate and pull the lever frequently it is not known whether decoding methods leverage dynamics from multiple lever pulls or just the lever pull occurring at t = 0 s. Accordingly, we sought to evaluate the effect of preceding lever pulls and/or body movements on the decoding of future lever pulls.

Rewarded lever pulls form a small portion of all self-initiated actions

Across the duration of the study mice performed between 1454 (M1) and 6999 rewarded lever pulls (M6) (Figure 6a). The ratio of self-initiated rewarded and non-rewarded lever pulls, licking events, and left and right paw spontaneous movements for all sessions revealed that lever pulls constituted only a small portion of behaviors (Figure 6b). Across all animals the proportion of rewarded lever pulls compared to all other movements ranged from 0.03 (M1) to 0.07 (M2) (Figure 6c). Even though only selected body parts were tracked (i.e., paws and tongue), these results suggest the vast majority of the time mice are engaging in carrying out many other spontaneous behaviors as reported in other studies (e.g., Musall et al., 2019).

Evaluation of pre-ever pull movements and behavior lockout on earliest decoding times (EDTs).

Tracking and evaluating the effects of previous self-initiated movements on the decoding of rewarded lever pulls. (a) Total number of recording hours and number of lever pulls for each mouse (note: each recording session was approx. 22 min long). (b) Percentage of rewarded lever pulls, non-rewarded lever pulls, and left paw, right paw, and licking movements performed by mouse M1 across all sessions. (c) Proportion of rewarded lever pulls relative to all other body movements for all animals. (d) The number of rewarded lever pulls as a function of ‘locking out’ previous lever pulls, licking events or left paw movements (note: locking out means excluding any rewarded lever-pull trial that was preceded by a movement in the previous n-seconds; see also Main text and Methods). (e) Same as (d) but for all animals and sessions (shaded region indicates lockout conditions under which less than 100 trials were present across the entire study and decoding was not carried out). (f) EDTs for rewarded lever pulls conditioned on licking event locking out periods of 0–3 s (for clarity, the 0-s time point excluded any rewarded lever pull that occurred precisely at the same time as a licking event, i.e. to the resolution of our 15FPS video). (g) Same as (f) but conditioned on excluding previous lever pulls (3-s time point excluded any rewarded lever pulls that occurred exactly 3 s after a previous rewarded or unrewarded lever pull). (h) Mean EDTs from (g) as a function of lever-pull lockout period. (i) Same as (g) but following low-pass filtering of the locaNMF components. (j) Same as (h) but for data in panel (g).

Removing pre-action confounds by implementing a post hoc lockout period

To evaluate the effect of intervening body movements in decoding future actions, we evaluated decoding upcoming rewarded lever pulls or spontaneous body movements preceded by periods of quiescence of varying durations (Figure 6d–h). All trials across all sessions were pooled (similar to the concatenated analysis in Figure 2) and decoding was carried out after ‘locking-out’ previous (1) lever pulls (i.e., rewarded and non-rewarded), (2) licking events, or (3) left paw movements (note: we selected left paw movements as the right paw was used to pull the lever and excluding such movements from analysis would remove most of the rewarded lever-pull trials). The number of rewarded lever pulls preceded by periods of non-body movements or lever pulls decreased approximately exponentially with increasing lockout duration (Figure 6d). This decrease in available trials for analysis was present in all mice (Figure 6e). Importantly, locking out licking and lever paw movements beyond 3 s yielded insufficient numbers of trials for decoding analysis (see shaded region Figure 6e).

Lever-pull EDTs are not affected by prior licking events

EDTs were recomputed by locking out (i.e., removing) lever pulls that were preceded by licking events in the previous 0, 1, 2, and 3 s (note: 0-s bin removed lever-pull trials that occurred exactly with a licking event based on our video recording resolution of 15FPS). We found that for all animals, the intra-animal EDT distributions were not statistically different from each other (Figure 6f).

Evaluating lever-pull EDTs vs. lever-pull lockout duration

We also recomputed EDTs for each animal and session after enforcing periods of 3–15 s of lockout (in increments of 3 s) (Figure 6g). EDTs for two mice (M1 and M2) stayed the same, while for four mice they shortened with increasing lever-pull lockout duration (M3–M6) (Figure 6g). This trend was confirmed by examining the mean EDT at each lockout time point which showed in nearly all mice (excluding M2) a strong trend for the mean EDT to shorten with increasing lockout (Figure 6h). In interpreting these results it is important to note that there were substantially fewer consecutive or same-session lever-pull trials when implementing increasingly longer lockouts. Thus, surviving trials used for analysis came from sessions that were increasingly further apart (e.g., multiple days or even a week). Pooling trials from separate days or weeks provides an additional source of noise due to changes in animal behavior, [Ca++] indicator properties, and longitudinal network changes observed in our cohorts (see Figure 4). As evidence for this, we found that subsampling the number of non-lockout trials to match the number of trials following 15-s lockout had the effect of shortening most EDTs (Figure 2—figure supplement 3).

We also recomputed locked-out EDTs for each animal (as in (g)) but following low-pass filtering the neural time series (filter set to 0.3 Hz; see Methods) as described above (Figure 6i). We found that average EDTs detected were longer than using non-filtered data (Figure 6j; e.g., 15-s lockout data means: M1: −3.50; M2: −4.85; M3: −6.95; M4: −5.01; M5: −3.0; M6: −3.70).

Taken all these factors into account, the results suggest that while EDTs shortened when using only lockout trials, the cause of the increase is due to: (1) removing stereotyped sequential lever pulls which could artificially bias the neural signal; and (2) increased variance in the longitudinal data caused by [Ca++] state changes, systematic neural network restructuring due to longitudinal performance and other unknown factors (see also Discussion).

In sum, we find that despite carrying out thousands of rewarded lever pulls, such pulls constituted a small percentage of overall spontaneous body movements (likely much less if we consider other body movements we did not track). Even when pooling all trials from each animal (resulting in thousands of trials), when locking out previous licking events or left paw movements, exponentially fewer rewarded lever pulls were available with increasing locking out period. EDTs computed when excluding previous lever pulls for a period of up to 15 s prior shortened EDTs in most animals, and average EDTs for band-passed neural data ranged from approximately −3 to −7 s. We conclude that it is critical to exclude sequential lever pulls to the computation of EDT as well as track and control for longitudinal changes in neural dynamics caused by learning or implant-related causes.

While the role of SCP in modulating voluntary behavior is suggested in some human studies (Schmidt et al., 2016), less is known about the specific frequencies involved in self-initiated action especially in mice. We thus sought to further characterize the frequency, power, and longitudinal characteristics of slow oscillations in neural activity occurring prior to lever pulls (Figure 7).

Slow oscillations dominate pre-self-initiated behavior neural dynamics.

Slow wave oscillations underlie self-initiated behavior in mice. (a) Examples of single session averages (dark continuous curves) from two time points and random segments (dark dashed curves) from V1-left and somatosensory-upper limb left reveal the presence of oscillations. (b) Power spectra of all session averages (colored curves) and average across all sessions (black curves) from mouse M4 in four cortical areas (dashed vertical lines indicate peak of average). (c) Peak frequency power of each session trial average for retrosplenial, motor, and limb cortex (colored scatter points). (d) Same as plot in (c) but for peak power for all animals and sessions. (e) Same peak frequency analysis as in (c) but for single lever-pull trials (instead of session averages). (f) Same peak power analysis as in (d) for single trials.

Limb and motor cortex oscillations have the highest power during pre-movement neural activity

We first evaluated the power of neural activity in a session (i.e., average of neural data from all trials from −15 to 0 s) and observed that high amplitude oscillations were present in some areas (e.g., limb cortex) but were much weaker in other areas (e.g., visual cortex) (Figure 7a). This is consistent with our self-initiated behavior task as no sensory stimuli or cues were used. This difference was consistent across all sessions with limb cortex oscillations being up to 10 times larger than those in visual cortex (Figure 7b examples from mouse M4).

Frequency and power in the average pre-movement neural activity

We evaluated the peak frequency of session averages (i.e., we computed the power-spectrum-density of the lever-pull trial average for each session; ses also Methods). Across all mice the vast majority of session averages had power peaks falling between 0.2 and 0.6 Hz (Figure 7c). This suggests that slow oscillations dominated the pre-movement neural activity consistent with our findings that self-initiated action preparation unfolds on time scales of several seconds (and consistent with the time course of our [Ca++] indicator). Turning to longitudinal trends, few statistically significant trends were present with only three mice showing correlations of peak frequency and time (Figure 7c; mouse M1: strong increase in peak frequencies in limb and motor cortex; mouse M2: strong decrease in peak frequencies in limb and motor cortex; and mouse M5 had an increase in peak frequency in retrosplenial cortex). The peak frequency power also exhibited differences between animals and also longitudinally (Figure 7d). For example, mouse M1 showed significant drops in power in limb, motor, and retrosplenial cortex, while other mice showed increases in power in limb or motor cortex (M2: limb; M3: motor; M4: motor; M6: limb). In sum, longitudinal changes in peak frequencies and power were minor and differed across animals.

Frequency power in single-trial pre-movement neural activity

We carried out a similar analysis as above but on an individual trial basis (Figure 7e, f). With respect to peak frequencies, distributions of frequencies from ~0.1 to ~0.5 Hz were observed, similar to session averages. In contrast with session averages, single-trial analysis showed more statistically significant trends (p values <0.05) in most animals and areas considered (retrosplenial, limb, and motor cortex): three animals had mid to strong-level increases in peak frequency (in all areas) with time (M1, M4, and M5); one animal (M6) had slight increases in frequency power in retrosplenial and limb cortex; and one animal has mid to strong decreases in peak frequency over time (M2) (mouse M3 had a −0.01 Pearson correlation value with time in limb cortex). Peak power trends were less common, with only two mice showing strong correlations in the three areas longitudinally (M1 decreases in power over time; M4 increases in power over time) with the remaining mice having changes only in a single area (mouse M2 showed strong decrease in power in limb cortex; and mice M3 and M6 showed a slight increase in motor cortex power over time).

In sum, during self-initiated behavior preparation power in both session averages and individual trials was strongest in the 0.1–0.7Hz. Some animals showed systematic changes in peak frequency suggesting that learning and/or longitudinal performance may change the underlying oscillatory structure of neural activity of self-initiated behavior preparation. These findings suggest a complex picture with different mice potentially engaging different learning mechanisms and areas that should be considered when evaluating in-session and longitudinal performance and decoding upcoming behaviors.

Given the results in Figures 17, namely, that prior to rewarded lever pulls there are systematic changes in cortex, for example inhibition of motor activity, on the order of several seconds prior to movement we sought to relate our results to human EEG studies of volitional action. In human volitional studies the motor cortex EEG signal preceding spontaneous body movements (e.g., flicking a wrist, pressing a button) generally shows an increase in activity commencing between −2 and −1 s prior to the body movement. To compare our results to human studies we evaluated the [Ca] activity preceding isolated random left forelimb movements, that is movements that were not related to lever pulls nor preceded by other movements for several seconds.

Identifying left paw movements isolated from other movements

We first computed the locations of all lever pulls relative to every left paw movement in windows of 20 s with 15 s of pre-paw movement and 5 s of post-paw movement (Figure 8a). Across all video recorded sessions we identified between 13,757 and 124,397 left paw movement bouts in individual mice (i.e., times where the left paw moved; see Methods; number of movements in all mice: M1: 54016; M2: 16241; M3: 18186; M4: 20487; M5: 13757; M6: 124397). We then ranked every left paw movement bout by the longest period of non-lever-pull activity, that is we ranked paw movement bouts by the amount of lever pulls occurring in the previous 15 s or following 5 s (Figure 8a). We next extracted the trials with a complete lever-pull lockout (i.e., no lever pulls 15 s before or 5 s after) and added the locations of left and right paw movements and licking events (Figure 8b). We then realigned the surviving bouts by the longest period of body movement quiescence, that is we reranked the remaining paw movement again by quiescence (Figure 8c). Finally, we looked for left paw movements that were not preceded by any movements for at least 5 s (Figure 8d). This approach revealed between 96 and 557 left paw movements that had at least 5 s of no preceding body movements and were completely isolated from lever pulls (number of movements per mouse: M1: 416, M2: 96, M3: 133, M4: 167, M5: 177, M6: 557).

The widefield [Ca] activity correlates of self-initiated limb movement vs. goal-oriented actions.

The widefield [Ca] activity correlates of random vs. goal-oriented self-initiated actions.

(a) Raster plot showing the location of all lever pulls in mouse M1 across all sessions (blue rasters) relative to spontaneous left paw movements (red line at t = 0 s) ordered by the duration of pre-paw-movement lever-pull quiescence. (b) The completely lever-locked left forelimb movements from (a) with added left and right forelimb movements and licking events (red rasters). (c) Same as (b) but ordered by the duration of quiescence prior to the left forelimb movement. (d) The bottom events from (c) showing the location of left and right forelimb movements and licking events (red rasters). (e) The average neural dynamics in motor cortex (blue), forelimb cortex (red), and retrosplenial cortex (green). (f) The average forelimb cortex activity (color shading represents standard error) for each mouse during periods as in (d) and (e). (g) The average neural activity (color shading represents standard error) from all mice averages as in (f) for motor, forelimb and retrosplenial cortex. (h) Same as (g) but for water-rewarded lever-pull activity. (Note: gray shading in all plots represents 3× standard deviation of the average neural signal between −30 and −5 s prior to movement or lever pull; see also Methods.)

Neural activity increases 1–2 s prior to left-limb movement

We next computed the average neural dynamic for the right hemisphere motor, upper-limb, and retrosplenial cortex activity and found that both motor and upper-limb cortex exhibited a slow rising time course commencing at approximately −2 s prior to paw movement, with the upper-limb cortex signal being larger than motor cortex (Figure 8e for example from mouse M1). We computed the upper-limb cortex average signal for all animals and found that in four mice (M1, M3, M4, and M5), this signal showed a significant increase prior to movement (i.e., rising above 3× the standard deviation of the preceding neural activity) commencing approximately −2 to −1 s relative to paw movement time (Figure 8f). Mouse M2 had an average [Ca] signal which was noisy, likely due to the lower number of trials. Mouse M6 also had a noisy signal that showed an increase above 3× std prior to movement, but not as pronounced as the other four mice. We note that mouse M6 had more than 5 months of recording and the pooled trials likely reflected significantly more variance due to pooling bouts from longitudinal data (see Figure 2—figure supplement 3 and Methods for a detailed discussion on the challenges of pooling widefield [Ca] data across long periods of time). We also sought to decode upcoming behavior-locked out paw movements but found that SVM-based methods did no better than chance (not shown). This was likely due to higher dynamics occurring during non-goal-oriented paw movements, but we also could not rule out the effect of pooling only a few hundred trials from tens of thousands of trials across many months of recording (see also Discussion).

Computing the average neural signals in motor, upper-limb, and retrosplenial cortex from the means in all mice we found that both motor and upper-limb cortex showed a significant increase in the average neural signal beginning at approximately 2 s prior to paw movement (Figure 8g). In contrast, the average neural signal in these areas prior to water-rewarded (locked-out) lever pulls showed a decrease commencing as early as −5 s prior to lever pull (Figure 8h).

In sum, we find that prior to non-lever pull related and isolated left paw movements, the [Ca] neural activity in mouse motor cortex begins to increase at approximately −2 to −1 s prior to paw movement – consistent with human EEG studies on the RP dynamics occurring prior to spontaneous finger or hand movement. In contrast, self-initiated water-rewarded lever pulls contain an inhibitory signal that starts earlier, at approximately −5 to −4 s prior to lever pull and contains stereotyped neural patterns that can be decoded to predict upcoming level pull timing (see Figures 1, 2,, 6).

Discussion

Since the 1960s, several human neuroscience studies seeking to identify the neural correlates and genesis of self-initiated, voluntary action have found that increases in neural activity in SMA and pre-SMA precede both voluntary movement and even awareness of the intent to act (Kornhuber and Deecke, 1965; Libet et al., 1983). Most studies found only small differences (~150 ms) between the intent to act and voluntary action initiation; however, these findings remain controversial and determining the precise arrival of subjective intent and the effect of reporting it is a complex topic (see e.g. Wegner, 2002; Dijksterhuis et al., 2006; Tusche et al., 2010; Sinnott-Armstrong and Nadel, 2010; Dijksterhuis and Aarts, 2010). Removing the reporting of intent from voluntary behavior paradigms and focusing solely on the relationship between neural activity and self-initiated action avoids some of the controversies and focuses the debate on the study of objective variables (e.g., timing of behavior initiation, neural activity in specific areas) – and enables the use nonhuman animal models for self-initiated and voluntary action research.

Self-initiated actions in mice are prepared seconds prior to movement and are biased to occur during specific phases of slow oscillations

Using a self-initiated behavior paradigm in mice to relate pre-movement neural activity to the initiation of behavior enabled us to collect a high number of behavior trials across weeks and months of recording and with higher neuroanatomical resolution recordings than EEG and higher temporal resolution than fMRI. Self-initiated behaviors in mice were preceded by a decrease in widefield [Ca] neural activity starting 3–5 s prior to behavior time, similar to (but longer) than the EEG RP signals in humans (Figure 1, see also Kornhuber and Deecke, 1965; Libet et al., 1983). We further found that decoding of upcoming behavior approximately 3–7 s prior to movement was possible, a finding consistent with findings using fMRI in humans (Soon et al., 2008; Soon et al., 2013; Bode et al., 2011; see Figures 1 and 2). Self-initiated behaviors were even more biased toward specific phases of neural activity than in humans (Figure 3). This suggests that in mice, oscillation phase could be even more determinative of action initiation timing than in humans. Overall, these results link human voluntary action studies with rodent self-initiated behavior studies and suggest mice as an adequate model for studying the neural correlates of self-initiated action.

Behavior preparation signals are distributed across the cortex

While the vast majority of voluntary action studies in humans focused on SMA (and pre-SMA), we found that decoding future behaviors using motor (or limb cortex) was inferior to decoding using all cortical activity (Figure 2). This suggests that pre-voluntary movement neural signals are widely distributed across the dorsal cortex but also that more temporally and spatially precise neural recordings may be required for a complete characterization of pre-movement neural activity in humans (and nonhumans). Coupled with a neural recording modality that more precisely reports local neural activity (i.e., widefield [Ca++] cortex) our findings suggest that voluntary action studies in humans may benefit from subcranial neural signals from multiple areas.

The role of motor cortex inhibition prior to lever pulls

Our paradigm shows that, on average, motor cortex is inhibited beginning approximately −5 s prior to lever pulls. In some mice and sessions, we found EDTs as low as −7 s which suggest the inhibitory (and other dynamics) commence even earlier. In parallel, we found that mice decreased, but not stopped altogether, random body movements during the period of −5 to 0 s. While our task did not require mice to cease body movements prior to lever pulls, it is possible that preparation of future pulls requires some mesoscale inhibition of motor cortex which necessarily leads to decreases in body movements.This hypothesis of delays in behavior on the order of seconds has some support from studies which show human subjects are more likely to initiate actions during exhalation periods which are generally 3 s in duration (Park et al., 2020), although mice breath faster than humans. Another interpretation is that mice consciously inhibit motor cortex in preparation of an action toward a water reward. (We use the term consciously, as we view intentional inhibition as achievable via unconscious systems.) For example, for every lever-pull mice consciously withhold pulling the lever prior to movement as they learn to ‘count’ several seconds prior to carrying out an attempt at the lever pull. In our opinion, this ‘counting’ might occur consciously or subconsciously and it would be challenging to establish in humans let alone in mice where we lack subjective report. In addition, such a strategy might lead to higher variances in the duration of inhibitory bouts – yet the neural dynamics of 15-s lever-pull lockout trials were sufficiently stereotyped to yield EDTs on the order of multiple seconds. Additionally, one mouse (M1) in our study did not internalize appear to learn the 3-s lockout rule and did not persevere at lever pulls at approximately 3-s intervals, yet had decodable EDTs. However, we cannot rule this possibility out with our dataset and paradigm. Such an interpretation could also call into question whether volitional actions toward a highly salient goal can be studied at all in the lab under self-initiated or self-paced paradigms. That is, it would raise the question of whether the study of volitional action toward high-value (rather than low-value) goals can only be carried out in natural settings where the opportunity for high-value decisions arises naturally from internal states rather than as part of an instructed paradigm.

A simpler explanation for our results is that the inhibitory dynamics are an integral part of preparation of a future action toward a salient goal and that they are present prior to action toward a salient goal in other paradigms. The duration and extent of such dynamics may be different than our paradigm where we required a 3-s lockout, but could be detected in both rodents and humans acting volitionally toward a highly salient goal.

Comparison of our study with cued action studies

In our experience, mice following external cues such as a light or sound triggers initiate their behaviors immediately and decoding the timing of future behavior on the scale of seconds is not possible. However, it is possible that in cue-triggered experiments – expert mice exhibit motor cortex inhibitory signals while they are waiting for a cue (e.g., see Schurger et al., 2012). However, such neural states would be conditioned on the eventual arrival of an external cue toward a future action and would not constitute self-initiated volitional action. Another possible paradigm would be to train mice to withhold action following a cue for several seconds prior to movement. In such a paradigm the neural correlates of action inhibition would be the focus rather than preparation of future action. Lastly, optogenetic approaches to activate or inhibit motor and forelimb cortex of mice after they have acquired the task could also be implemented, especially if mice learn a self-initiated – rather than cued task (the latter being more often the case). Overall, we view these and other similar paradigms as useful complementary studies to our work and suggest them for future studies.

Longitudinal performance of a self-initiated task restructures the neural dynamics underlying action and spontaneous neural activity

It has been suggested that noise-driven stochastic models (i.e., leaky stochastic accumulators) can explain the RP as a result of averaging backwards in time over multiple stochastically determined behavior initiations (Schurger et al., 2012). In contrast, a recent human study aimed at testing part of this hypothesis found that the RP amplitude increased with learning suggesting that the RP represents planning and learning rather than stochastic structures alone (Travers et al., 2021). Consistent with the later study, we also found that decoding times for upcoming water-rewarded lever pulls in some mice changed, in particular they became shorter (Figure 4). We also found structural changes in the neural dynamics in some mice as both the lever-pull neural space and its overlap with the left paw changed systematically with time. These results suggest that learning, or merely longitudinal performance, of a high-value behavior (e.g., water seeking in water deprived mice) increases stereotypy even in self-initiated actions. We suggest that automaticity-like processes could be involved with movement preparation becoming less dependent on cortex and more dependent on subcortical structures. These large-scale cortical changes in brain activity have been observed in humans, for example, while learning a brain–machine interface task (e.g., Wander et al., 2013) but aside from a very recent EEG study (Travers et al., 2021) are not well described. The mesoscale mechanisms for such changes may involve increased representation of lever-pull preparation dynamics in the overall spontaneous dynamics (e.g., occurring more frequently) or increase differentiation between lever-pull preparatory neural activity and other behaviors. These results add to the evidence that self-initiated actions are supported by learned neural-dynamical structures and that those can change on longer time scales.

Inter-animal variability

We found inter-animal differences in several results: different decoding times, anatomical areas involved as well as longitudinal dynamics (e.g., differences in longitudinal decoding trends Figure 4; or preferred phase angles at behavior time different Figure 3; different trends in frequency peak and power Figure 7). These results suggest that neural dynamics and strategies for initiating a self-initiated action could be specific to individual subjects and that pooling over multiple subjects may remove novel or important differences. In other words, single subject analysis may be critical to further advancing the debate on the dynamics of neural activity underlying self-initiated behaviors in both humans and nonhumans.

Internal state evidence accumulation commences seconds prior to movement

We found that both lever-pull decoding accuracy increases nearly monotonically with approaching action (e.g., Figure 4b) and that intra-session variance decreases several seconds prior to movement within limb, motor, and retrosplenial cortex. We suggest these findings constitute further ‘indirect evidence that evidence-accumulation’ (Bode et al., 2014) is occurring even in the absence of explicit stimuli – likely based on evaluations of internal states and models. This supports the hypothesis that internal-state driven self-initiated actions could be potentially modeled by commonly used perceptual and cognitive decision making models (Gold and Shadlen, 2007; Heekeren et al., 2008; Murakami et al., 2014; Murakami et al., 2017).

The effect of confounding movements on the decoding of future rewarded action

Our study does not directly address the effects of stimuli or other perturbations on the neural dynamics preceding self-initiated decisions, for example, as in some human choice paradigms that consider decision choice in the presence of novel information (e.g., Maoz et al., 2019). Additionally, there is evidence that micro-movements can contribute to ongoing neural activity between behavior bouts (Stringer et al., 2019; Musall et al., 2019). While it is possible that in preparing for a self-initiated rewarded behavior mice undergo a series of physical movements, we did not see any evidence for this in our recordings. In particular, we found a tendency for mice to decrease their body movements in the 3–5 s prior to self-initiated action and we did not find evidence for sequences of paw or licking movements. We suggest future studies focusing on the micro-movements underlying self-initiated action could address this issue using high-frame rate and high-resolution video recordings.

Limitations

Our study focused on characterizing neural dynamics and timing of an ethologically valuable movement rather than identifying intent or awareness of upcoming movements. As such, we do not directly address the role of subjective ‘intent’ as in some human EEG studies (e.g., Libet et al., 1983) or the role of reasons or deliberation on decision making. (Note: as mentioned above, the effect of reporting intent and the use of reasons or deliberation in voluntary actions are the subject of ongoing debates; see, for example, Dijksterhuis et al., 2006; Dijksterhuis and Aarts, 2010; Vierkant et al., 2019; Wegner, 2002.) Our study was also not aimed at disambiguating between the timing of intent awareness and movement. Uncued voluntary action studies in humans generally find the difference between the timing of subjective intent and movement to be small (e.g., 150 ms; Libet et al., 1983) or even negligible, orders of magnitude smaller than the EDTs in our results. Second, our decoding times showed a strong dependence on the number of trials suggesting that additional trials would change (most likely improve) our decoding results (though we note that this effect may merely reflect sequences of lever pulls). Although it is challenging to keep animals motivated across many trials within a single ~20-min session, decreasing reward size might have increased the number of self-initiated lever-pull behaviors and reduced the dependence we observed. Third, we sought to remove pre-movement confounds from our results by ‘locking out’ previous lever pulls or body movements (Figure 6). A more direct approach where animals are specifically trained to remain quiescent prior to an action may yield more trials and easier to interpret results. However, it is practically challenging to train mice to withhold behaviors for significant periods of time (e.g., >>3 s) while also performing a task for a valuable reward. Fourth, we found strong correlations between lever pulls and body movements in all mice (not shown). However, we did not take into account the temporal location of lever-pull activity when decoding the body movement (Figure 2); for example, we decoded upcoming left paw movement initiations without accounting for – or removing – lever-pull initiations co-occurring with such paw movements. It is obvious that many of the spontaneous paw movements also coincided with lever pulls and thus the EDTs for paw movements were not an independent measure from the EDTs of rewarded lever pulls. However, we chose to remain agnostic and not separate body movement initiations into those coinciding with lever pulls and those that occurred many seconds away from rewarded lever pulls (this also had the effect of preserving a higher number of body-movement trials for decoding). Despite not separating the data, we did find a significant difference in lever pull and right paw dynamics longitudinally (Figure 4j) suggesting that further separation may have only increased this difference. We acknowledge that it would have been interesting to divide the behaviors and carry out separate analysis, and leave this direction for future projects.

Conclusion

Over the past few decades, rodent models of sensory systems and decision making have become increasingly common (e.g., visual evidence accumulation; Najafi and Churchland, 2018; Odoemene et al., 2018; Aguillon-Rodriguez et al., 2021). The findings presented here suggest that mice could also be an appropriate model for neuroscience investigations into self-initiated action. While characterizing the dynamics underlying self-initiated behavior in rodents may advance our understanding of self-initiated action in nonhumans as well, it could also advance our understanding of developmental and psychiatric disorders that have behavioral symptoms such as avolition in depression (e.g., lack of will to move; Brakowski et al., 2017) and behavior repetition observed in obsessive–compulsive disorders (Lysaker et al., 2018). Our findings suggest that the neural mechanisms underlying self-initiated action preparation and performance could be preserved in part, or in whole, between humans and mice and that studies of self-initiated action in humans would benefit from mouse models and the vast libraries of behavior, genetic, and neural recording methodologies available.

Materials and methods

Mice

Mouse protocols were approved by the University of British Columbia Animal Care Committee and followed the Canadian Council on Animal Care and Use guidelines (protocols A13-0336 and A14-0266). Six GCaMP6 transgenic male mice (Ai93 and Ai94; Madisen et al., 2015) were used. For the study the mice names were defined as M1–M6 and had the following genotypes: M1–M5: Ai94; M6: Ai93.

Lever-pull task

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Mice were kept on a restricted water schedule as previously described (see Silasi et al., 2016). Briefly, mice were implanted with a head post and head fixed with their bodies partially resting in a 28-mm diameter Plexiglass tube. A 1-cm cutout from the right side of the tube floor accommodated a monitored lever that was positioned at the same height as the tube. At the start of each session a water spout was set near the mouth of the mice, ensuring the mice could obtain dispensed water drops by licking. In order to receive a water reward mice were required to pull the lever past a threshold and then hold the lever without pulling it to the maximum value. Correct lever pulls – ‘rewarded lever pulls’ – were tracked in real time to provide water reward. Following a reward a lockout period of 3 s was implemented during which mice could pull the lever but would not be rewarded irrespective of performance. This task required learning the minimum threshold, the duration of the hold and the refractory period of the lockout. We note that the duration of the hold was gradually increased by 0.1-s increments in mice that learned to perform the task well. We selected 3 s as longer lockout values limited the consistent acquisition of the lever-pull task. We recorded widefield calcium activity across each recording session (1330 s, i.e., ~22 min long) over many days (42–109 days, average = 58.3 ± 24.6 standard deviation). Mice had longitudinal trends with some increasing the # of lever pulls over time and others decreasing. (See Figure 4a; M1–M6: Pearson correlations: −0.25, 0.39, 0.65, −0.37, 0.18, 0.68; p values: 0.048, 0.020, 1.095e−5, 0.288, 6.320e−15; note: because mice were not habituated we discarded the first week of training in this computation to better capture longitudinal trends rather than habituation idiosyncrasies.)

Widefield calcium imaging

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Widefield calcium imaging was carried out as described previously (Xiao et al., 2017; Silasi et al., 2016). Briefly, mice with a chronically implanted transcranial window were head fixed under a macroscope. Images were captured at 30 Hz with 8 × 8 pixel binning, producing a resolution of 68 µm/pixel (Vanni and Murphy, 2014). To visualize the cortex, the surface of the brain was illuminated with green light (but not during image acquisition). Calcium indicators were excited with blue-light-emitting diodes (Luxeon, 470 nm) with bandpass filters (467–499 nm). Emission fluorescence was filtered using a 510–550 nm bandpass filter or collected in a multi-band mode as described below. For single wavelength green epifluorescence, we collected 12-bit images at varying time resolution (33 ms; i.e., 30 Hz) using XCAP imaging software. In order to reduce file size and minimize the power of excitation light used, we typically bin camera pixels (8 × 8) thus producing a resolution of 68 µm/pixel. Hemodynamic correction was not available as we only used single wavelength excitation. Based on previous experiments using similar imaging conditions (Vanni and Murphy, 2014; Silasi et al., 2016; Xiao et al., 2017) in control GFP expressing mice we would not expect significant contributions from hemodynamic signals under the conditions we employed. These imaging parameters have been used previously for voltage-sensitive dye imaging (Mohajerani et al., 2013) as well as anesthetized GCaMP3 imaging of spontaneous activity in mouse cortex (Vanni and Murphy, 2014) and awake GCaMP6 imaging in mouse cortex with chronic window (Silasi et al., 2016).

Behavioral recordings

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Behavior was recorded using a Windows OS camera at 15 frames per second. Video recordings were saved in the native.wmv format and converted at the same resolution to .mp4 format for post-processing steps. Each video recording session lasted approximately 22 min and contained ~20,000 frames.

ΔF/F0 computation

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ΔF/F0 computation was carried either via bandpass filtering (0.1–6.0 Hz) or as previously described (Xiao et al., 2017). Briefly, F0 was computed as the average pixel activity in the window preceding the analysis window. For example, for analyses of neural activity within a ±3 s window following a behavior, F0 was computed based on the previous 3 s of neural activity, that is the −6 to −3 s window. We found no statistical differences in our results between using sliding window ΔF/F0 or bandpass filtering and for our analysis we relied only on bandpass filtered data.

Registration to Allen Institute dorsal cortex map

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We used a 2D projection of the Allen Institute dorsal cortex atlas, similar to Musall et al., 2019, Saxena et al., 2020, and Couto et al., 2021 to agnostically identify ROIs without the need for stimulus driven or other neuroanatomical markers. We rigidly aligned the widefield data to a 2D projection of the Allen Common Coordinate Framework v3 (CCF) (Oh et al., 2014) as in Musall et al., 2019, Saxena et al., 2020, and Couto et al., 2021, using four anatomical landmarks: the left, center, and right points where anterior cortex meets the olfactory bulbs and the medial point at the base of retrosplenial cortex. The ROIs identified for each animal and session were individually inspected to qualitatively match expected activation of somatosensory cortex during lever-pull trials in each session.

Analysis

All analysis was carried out using custom python code developed as part of an electrophysiology and optical physiology toolkit available online (https://github.com/catubc/widefield; Mitelut, 2022; copy archived at swh:1:rev:726ecd42f035f17af9cc7e4f274b3b55a3ef6908). Methods for computing event triggered analysis for widefield imaging have been previously published (Xiao et al., 2017) and are also available online (https://github.com/catubc/sta_maps; Mitelut, 2017).

Unsupervised behavior annotation and body movement computation

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Seven features were identified for tracking: center of left paw, center of right paw, the underside of the jaw, the tip of the nose, the underside of the right ear, the tongue, and the midpoint of the lever. DeepLabCcut (DLC v. 2.1.8; Mathis et al., 2018) was used to label these features in 60 frames per video for three videos in each animal. The DLC predictions were inspected and smoothing was applied to correct missing or error frames (using a 30 frame window sliding mean or Savitsky-Golay 31 frame filter using third degree polynomial; note: mouse M2 did not have good tongue tracking and this feature was excluded from analysis). Body movement initiations were computed as the first time point at which the velocity was larger than three times the standard deviation of velocity over all periods. We then excluded movement initiations which were preceded by another initiation (of the same body part) in the previous 3 s of time.

Principal component analysis

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PCA was applied to neural activity time series to decrease the dimensionality and denoise the data. For each session we first converted the filtered [Ca++] neural activity from pre lever neural recordings from time −15 to +15 s into a series of [n_frames, width_pixels * height_pixels]. These data were then run through principle complement analysis linear dimensionality reduction using the python sklearn package to obtain a pca model (available here). We next selected the number of principal components required to reconstruct the data to ≥95% variance explained precision. Lastly, we applied the PCA model (i.e., denoised) to both the lever-pull neural data and control data.

SVM classification – decoding single sessions

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We used SVM classification to decode neural activity preceding an action vs. random periods of time using methods similar to those used in humans with fMRI data (Soon et al., 2008). Briefly, for each session and each rewarded lever pull or body movement initiation we extracted segments of neural activity 30 s long centered on the time of the action (i.e., −15 to +15 s following the action). Controls were selected similarly but the time of the action (i.e., t = 0 s) was randomized to fall anywhere in the session except a ±3 s window around an action. For clarity, controls could contain neural activity from rewarded lever pulls or body initiations; we found this to be a more conservative method than to manually select only non-movement periods as controls. We next denoised both the behavior data and the control data using PCA (see description above). We then built SVM classifiers using as input 1-s-wide windows (30 frames @ 30 FPS) of data from both the behavior (i.e., class #1) and the random controls (i.e., class #2). The input to each SVM classifier was a 2D array [n_trials, n_frames * n_PCs]. For example, in a session where >95% of the data dimensionality was captured by 10 PCs, the input the the SVM classifier was: [n_trials, 30*10] = [n_trials, 300]. We similarly computed the control array and the SVM classifier were trained on two classes (i.e., lever pull vs. control). We tested additional sized windows (i.e., single frame = 30 ms, or 150 frames = 5 s) but did not see significant improvements. We used sigmoidal SVM kernels as they showed a slight improvement in SVM accuracy over linear kernels (see https://scikit-learn.org/). We carried out 10-fold cross-validation using a split of 0.9:0.1 train:validate. The output of the SVM classification for a 1 s window was assigned to the value of the last time point in the stack (e.g., the accuracy computed from decoding the −15 to −14 s time window was assigned to the −14 s time bin). We carried out this SVM classification for each time point in the −15 to +15 s window. For clarity, 870 SVM classifiers (−15 to +14 s = 29 s * 30 fps = 870 frames) were trained for each validation point (i.e., 10-fold cross-validation).

SVM classification – decoding concatenated sessions

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We additionally trained SVM classifiers on concatenated sessions to increase the number of trials available. Sessions were concatenated across sequential behavior days to reach a minimum of 200 trials. Intra-session PCA was first applied to each session to denoise the data locally using a minimum of 95% reconstruction accuracy as above. The denoised time series were then concatenated and fit to a PCA model (available here) using randomly sampled (3%) of the data from the concatenated stack. The multi-session PCA matrix was then used to denoise the individual sessions and we kept a fixed 20 principal components to reconstruct the concatenated datasets. The remaining steps (SVM training and decoding) were carried out as for the single session approach described above.

SVM classification – decoding locked out concatenated sessions

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We trained SVM classifiers on trials that were locked out (i.e., not preceded by a previous rewarded or unrewarded lever pull) by several seconds (see Figure 6). Sessions were generated by pooling a minimum of 50 to a maximum of 200 locked-out trials. We chose these values to be qualitatively similar to the analysis done for non-lockout trials in which we used single session trials (ranging from 30 to >100 trials per session) and concatenated trials (which contained trials from several sequential sessions reaching a minimum of 200 trials). We used a 50 trial-sliding window to select trials for each concatenated session. For example, for mouse M1, for a lockout of 15 s, a total of 503 trials survived out of all rewarded lever-pull trials. We pooled these 503 trials in groups of 200 using 50 trial windows resulting in 10 groups of (overlapping) data to be processed (i.e., 0–200, 50–250 … 450–503). This method is essentially similar to the concatenated datasets where for each session we added trials from subsequent sessions until we reached at least 200 trials (i.e., that method also yielded overlapping trials across time). While this method does yield overlapping trial sessions, our goal was to show EDTs for such synthetic sessions. Using non-overlapping data (i.e., the sliding window was set to 200 trials) yields similar results.

Computation of EDT

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We sought to use a method that detected the first time point in the cross-validated SVM accuracy curves that was above chance at a statistically significant level (i.e., Student t-test p value <0.05). We denoted this time as the EDT for each session. We obtained the EDT for each session using several steps. First, for each session, we computed the 10-fold cross-validated accuracy curves using 30-time step (i.e., 1 s) windows as described above but we additionally filtered the accuracy curves using a 30-time step moving average to further decrease the effects of noise on the prediction curves. Next, we obtained the significance at each time point by computing a one-sample t-test between the SVM cross-validation accuracy values (i.e., 10 values) and a population mean of 0.5 (i.e., chance) using the python scipy stats package. We next applied a Benjamini–Hochberg correction for multiple tests using the python statsmodel package. Finally, starting at t = 0 s, we moved backwards in time until we found the last time point that was statistically significant (i.e., p value <0.05 as computed above). This last step had the effect of imposing a constraint which required all decoding accuracy distribution times following the EDT to be statistically significant – thus excluding random stochastic fluctuations in the accuracy curves which could result in very low EDTs that are not reasonable or meaningful. The effects of this last constraint could be observed in Figure 2g, h where the EDT is higher (i.e., closer to t = 0 s) than other time points that are statistically significant (see top colored bars for statistical significance and note that there are some isolated times that show statistical significance). We denote ‘shortened’ EDT time to indicate that the EDT time to the lever-pull time (i.e., t = 0 s) decreased (i.e., decoding become poorer) and ‘lengthened’ EDT time to indicate that the EDT time to lever-pull time increased (i.e., decoding was better).

Statistical tests for single ROI vs. all neural areas

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Two-sample KS tests were carried out between EDTs obtained by using limb cortex neural activity vs. EDTs obtained using all areas. This analysis was done on non-lever lockout data (i.e., all rewarded lever trials) as it is a cross-feature analysis not a cross-session analysis and we were interested in the relative effects between areas not absolute EDT times. This approach enabled us to obtain a significantly higher number of sessions (as opposed to using locked out data only).

LocaNMF

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LocaNMF was applied to the data from each widefield session as in Saxena et al., 2020. Briefly, we applied semi non-negative matrix factorization (sNMF) to the denoised data, while encouraging localization in the spatial components to the Atlas regions. This results in spatial components that are aligned to the different Atlas regions, thus allowing us to further analyze the corresponding temporal activity in each region. The locaNMF parameters used were as follows: maxrank = 1; min_pixels = 200; loc_thresh = 75; r2_thresh = 0.96; nonnegative_temporal = False; maxiter_hals = 20; maxiter_lambda = 150; lambda_step = 2.25; lambda_init = 1e−1.

Power spectra

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LlocaNMF temporal component spectra were computed using the python scipy signal package.

Low band-pass filtering of time series

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We used both causal and non-causal filters to evaluate the effects of filtering on EDTs. For non-causal filtering we used the scipy.signal.filtfilt(), and for causal filtering we used scipy.signal.lfilter() and corrected by shifting the filtered time series by an amount of fps x 1/filter_frequency = 30 fps × 1/0.3 Hz = 100 time steps. Both filtered time series were qualitatively similar in shape, amplitude, and phase.

Convex-hull analysis

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For each session, the neural activity ‘convex hull’ at lever-pull time (i.e., t = 0 s) was defined as the hyper-volume that enclosed the t = 0 s neural activity vectors. As convex hull analysis is sensitive to outliers a 10% K-nearest-neighbor triage was implemented prior to evaluation. The convex hull of lever-pull (i.e., t = 0 s) neural activity can be visualized in two dimensions by carrying out PCA on all the neural activity from the session and then computing the convex contour enclosing the t = 0 s neural activity vectors (see Figure 4d – blue dots). For computing the ratios of the pre-pull convex hull to the random neural data we chose random periods of time uniformly from the session. The normalized area under the ratio was computed by first computing the convex hull of neural dynamics from 10 to 0 s prior to a pull, normalizing at every computation by the total area of the convex hull for all neural activity occurring in the session. We then computed the area under this curve and divided it by a similar computed curve by this time starting at randomized times (i.e random times at least 3 s away from a rewarded lever pull).

Pre-movement region-based ROI phase computation

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We computed the phase of a neural activity of single trial for each region’s temporal components ROI by fitting a sine function to the period of −5 to 0 s prior to lever pull. We next computed the phase of each trial as the intersection of the sine fit with the t = 0 s line.

Sinusoidal fits to single-trial data

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Sinusoids were fit using scipy curve fit function to single-trial neural data from each area based on the last 5 s preceding the lever pull (i.e., −5 to 0 s).

Earliest variance decrease time

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We defined the EVDT for each session as the time at which the variance in a 1-s sliding window decreased by 2× the standard deviation of the variance computed in the window −30 to −15 s prior to the lever pull. We found the requirement for all variance values in a 1-s sliding window to fall below the threshold as necessary to deal with noise or fluctuations in variance. We also required the EVDT to fall between −6 and 0.5 s prior to the pull or it was discarded from analysis resulting in many sessions being discarded from analysis, especially for mice M1 and M2. More robust methods for detecting the variance decrease are likely possible, but were not explored. We also note that in Mouse M2, the variance change prior to movement was positive (i.e., variance increased) and thus we used the absolute of the difference to compute the EVDT (instead of only considering decreases). We note that as the time courses could be quite noisy, this method is sensitive to thresholds being set, for example 2 vs. 4× std can yield somewhat different distributions. We also note that we used a Savitsky-Golay filter (31 samples) to furter smooth the data in order to get consistent results from visual cortex data.

Detecting stereotyped movements

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We sought to detect the presence of stereotyped movement patterns (e.g., left paw followed by right paw followed by licking) using PCA applied to binarized behavior time series (not shown in results). We first obtained DeepLabCut time series locations for the left paw, right paw, and tongue as a 2D vector [n_time_points, n_dimensions], where n_time_points: number of imaging frames in a session (usually 1300 s × 15 fps) and n_dimesions: x and y video coordinates (in pixels). We next generated a Boolean array with zeros representing no movement and ones representing body movements. We then extracted segments of 20 s long around each lever pull (i.e., from −10 to +0 s relative to lever pull) resulting in arrays of dimension [10 × 15 fps, 2] = [150, 2]. We then flattened and stacked the arrays (trial wise) and computed PCA on the resulting data (i.e., input into pca [n_trials, n_time_points x dimension]). In an additional step, we also concatenated across body parts resulting in single-trial dimensions of [n_time_steps, n_dimensions x n_body_parts]. Neither approach (single body part of concatenated body parts) yielded multiple discernable clusters in the first two PCs.

Quiescence period analysis

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Analysis in Figure 1, Figure 1—figure supplements 6 and 7 and in Figure 8 required the computation of bouts of activity around a lever pull or limb movement. Movements (of limb or lever) were detected as explained above and binned in segments of 0.250 s. Given the lower temporal resolution of our videos, when computing lockouts we allowed for the first or last bin of a lockout period to contain movements (e.g., the first bin in a 15-s lockout period or the very last bin prior to movement, i.e., the −0.250 s bin).

Processing and analysis of data for Figure 1, Figure 1—figure supplement 7

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Pooling data across weeks or months of longitudinal recordings adds substantial noise and other confounds to our datasets (see Figure 2—figure supplement 3, and Main manuscript for Figure 6 for a detailed explanation). For the analysis here we pooled lever lockout trials (as in Figure 6) but were further limited to only those sessions where video was available. This reduced our overall trials by approximately 50% (or more) in all mice compared to Figure 6 analysis. The trials were further split into three groups representing least to most body movements. Additionally, trials pooled into these subgroups were ordered by movement amount – as opposed to ordered by day of acquisition time. This also has the effect of increasing the variance of data means due to time between individual trials in the subgroup. Given these issues, our analysis in this panel was limited to characterizing [Ca] time courses only (i.e., it was not possible to also carry out EDT decoding on trials contained in these reduced datasets – as the time series were too noisy).

Data availability

Code for generating all figures is provided here: https://github.com/catubc/elife_self_init_paper, (copy archived at swh:1:rev:2a6d97d1afdc611e827d952dfa3c7d2fecb4ec33). Datasets are provided on Dryad under the information below: Mitelut, Catalin (2022), Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement, Dryad, Dataset, https://doi.org/10.5061/dryad.ttdz08m0z.

The following data sets were generated
    1. Mitelut C
    (2022) Dryad Digital Repository
    Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement.
    https://doi.org/10.5061/dryad.ttdz08m0z

References

    1. Coe B
    2. Tomihara K
    3. Matsuzawa M
    4. Hikosaka O
    (2002)
    Visual and anticipatory bias in three cortical eye fields of the monkey during an adaptive decision-making task
    The Journal of Neuroscience 22:5081–5090.
  1. Book
    1. Jahanshahi M
    2. Hallett M
    (2003) The bereitschaftspotential: what does it measure and where does it come from
    In: Jahanshahi M, Hallett M, editors. The Bereitschaftspotential. Springer. pp. 1–17.
    https://doi.org/10.1007/978-1-4615-0189-3_1
    1. Kornhuber HH
    2. Deecke L
    (1964)
    Hirnpotentialänderungen beim menschen vor und nach willkürbewegungen,dargestellt mit magnetband-speicherung und Rückwärtsanalyse”
    Pflügers Arch 281:52.
  2. Book
    1. Lang W
    (2003) Surface recordings of the bereitschaftspotential in normals
    In: Jahanshahi M, Hallett M, editors. The Bereitschaftspotential. Springer. pp. 19–34.
    https://doi.org/10.1007/978-1-4615-0189-3_2
  3. Book
    1. Maoz U
    2. Mudrik L
    3. Rivlin R
    4. Ross I
    5. Mamelak A
    6. Yaffe G
    (2015) On reporting the onset of the intention to move
    In: Mele AR, editors. Surrounding Free Will: Philosophy, Psychology, Neuroscience. Oxford University Press. pp. 184–202.
    https://doi.org/10.1093/acprof:oso/9780199333950.001.0001
    1. Romo R
    2. Schultz W
    (1986)
    Discharge activity of dopamine cells in monkey midbrain: comparison of changes related to triggered and spontaneous movements
    Soc Neurosci Abstr 12:207.

Decision letter

  1. Gordon J Berman
    Reviewing Editor; Emory University, United States
  2. Christian Büchel
    Senior Editor; University Medical Center Hamburg-Eppendorf, Germany

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Christian Büchel as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) There was confusion amongst the reviewers as to the precise definitions of what the authors called "voluntary" movements. Providing a more precise definition (and the appropriate controls, as described below) will be important.

2) One of the goals of the authors was to study the neural mechanisms underlying voluntary movements. While they acknowledge (in the discussion) that they do not have evidence that actions are "intentional", they make the assumption that mice do "form the intent to act near the lever pull time". To back up this assumption, the authors should at least present some evidence that the action of interest (i.e., the rewarded lever-pull) is not just a random jerky movement that happens to be rewarded once in a while. In fact, mice seemed to pull the lever very frequently and impulsively (the majority of inter-pull intervals were way below 3 s in Supplementary Figure 1.2) even for the last sessions of the training. Therefore, it is not readily apparent that mice apply any control to their lever-pull actions. Providing evidence that the action is goal-directed is important if the goal of the paper is to study neural signatures of the intention to act. A somewhat compelling analysis could be to compare rewarded lever-pulls with "spontaneous" movements, provided that these two types of movement can be convincingly characterized as goal-directed vs. incidental. In contrast, throughout the manuscript, the neural activity aligned to rewarded lever-pull events (which are assumed to be "voluntary" actions) is compared to the neural activity aligned to random times during the task (whether or not it involved movements), which may not be the most convincing control.

3) The learning trajectory of mice is also not well characterized (e.g. changes in inter-pull intervals are not quantified, nor the relative increase in rewarded actions across training sessions, etc.). Yet, several claims in the paper are directly based on the fact that mice have learned to pull the lever after 3 s interval to receive water rewards (which relates to point 1). In particular, one important assumption in the paper is that as mice learn, the lever-pull movements become more stereotyped, but this has not been shown explicitly. It would be helpful, for example, to see how analog traces of lever-pulling change throughout the learning stages and how the variance of the movement across trials decreases in late sessions.

4) The central claim of the paper is that rewarded lever-pulls can be predicted from pre-movement neural activity several seconds (even up to 10 s) prior to the action. However, obvious motor confounds and other alternative explanations have not been convincingly ruled out. In fact, the action of lever pulling may require a series of complex movements (like changing posture, extending the forelimb, reaching the lever, grabbing the lever, etc.). The authors themselves mentioned that they found strong correlations between lever pulls and body movements in all mice, but the data is not used nor shown in the paper. The motor commands preceding but related to lever-pull could unfold at least a few hundreds of milliseconds prior to the detection of lever-pull in the task, and thus be reflected in the neural activity that is predictive of the lever pull. Moreover, if this series of movements is highly stereotyped, and in turn leads to stereotyped neural activity (like the slow oscillations observed before the lever-pulls), it could explain why the detection of lever-pulling actions always occurs at a given phase of the neural oscillation. Such observations that stereotyped movements occur way before the lever-pull detection could partially rule out the fully "cognitive" explanation proposed in the paper, but would concur with recent findings that showed that ramping neural activity can be, for the most part, explained by movement-related activity (Musall et al., 2019).

5) Toward the end of the result section (Figure 6), the authors briefly begin to address the issue about whether pre-movement activity can really be considered movement free. Here, "lockouts", i.e. periods where other movements (like licking, or previous lever-pulls) did not occur, were introduced in the analysis. The lockouts altered the earliest-decoding-time (EDT) of the lever-pull (in some mice EDT was even divided by half: from -4 s to -2 s). However, the effects of "micro-movements" like facial movements or changes in body posture may not be taken into account with the lockout approach. Such micro-movements have been shown to explain a large variance of the neural activity (see Stringer et al. 2019 and Musall et al. 2019). Therefore, to fully control for movement confounds, the effect of high dimensional/micro-movements extracted from video recordings should be removed from the neural activity. These analyses could yield a much shorter EDT (e.g., -0.15 s), more consistent with previous reports.

6) SVM decoding accuracy in Figures 1h, 2b, 2d, etc. shows in some cases that the earliest decoding time can precede a lever pull by 10-15 seconds. But how are we to know that SVM predictions in a given 1-second window are predicting an upcoming movement 10-15 seconds in the future, versus a separate pulling event that occurs within the SVM window on some subset of trials in a session? The increase in SVM accuracy as the movement approaches may be (as in A) due to changes in probability and temporal coupling in pulling events. The data are aligned to lever pull n, hence maximizing SVM accuracy at that time point. It is unclear whether the authors can address such concerns with the existing data. One analysis that would be telling would be something akin to GLM. For example, in Figure 1e, are the oscillations due to multiple movements occurring in succession? The authors could fit a Gaussian to the last peak (from about -2 sec to 2 sec) in Figure 1e. Then the authors can take this Gaussian and convolve it with each lever pull event, and then add all these together into a composite trace that represents what neural activity should look like if it is only determined by lever pull events that follow some Gaussian distribution. Figure 1e could be reconstructed from this idealized timeseries as if it were the actual raw data. Should this control also show oscillations with increasing amplitude, this suggests that the neural activity in Figures 1e (and the SVM analyses by extension) is due to multiple lever pulls – not volitional activity that predicts an upcoming lever pull. Similarly, as a control, the authors should also re-plot neural activity in Figures 1e, 5a, etc., using the longer post-hoc lockouts employed in Figure 6. That is, the authors should re-plot activity but exclude all trials where a previous lever pull occurred within x seconds, where x is 6, 9, 12, etc. as in Figure 6g.

7) Another related concern seems to be the convex hull analysis in Figures 4d-g. For example, in Figure 4e it is shown that the convex hull decreases in size as the movement (t=0 sec) approaches. The authors conclude that neural activity looks more random prior to the lever pull. However, given the concerns above, it seems this "randomness" could actually be due to other lever pulls (and other spontaneous behaviors) that are not exactly temporally aligned, and not certain to occur in each sample. Thus, we would also ask the authors to repeat this analysis using longer post-hoc lockouts to prevent contamination from earlier lever pulls.

8) It is excellent that the authors considered the issue described in Point (1), in their control analysis in Figure 6. But the reviewers disagree with their conclusions. Figures 6g and 6h clearly show that EDT drops considerably as the lever lockout window increases. For example, the 9, 12, and 15 second lever lockout periods appear to mostly eliminate all sessions with EDTs greater than 5 seconds. This is a drastic reduction in the time horizon (10-15 seconds) argued by the authors given their earlier results.

The authors might argue that these results are mixed, given that they were not statistically significant in M1 and M2 (Figure 6g). But this is misleading. In M1, there are only 2 or so EDTs greater than 5 seconds in the 3 second lockout condition. There are none in the 6 second lockout condition. Thus, in this mouse, the lack of statistical significance is simply because there are little to no EDTs to eliminate by gradually increasing the lever lockout period. This analysis is not useful in a mouse that exhibited little to no EDTs in the 10-15 second range argued by the authors. For M2, it is surprising that no statistically significant effect was detected given that all EDTs greater than 5 seconds are eliminated in the 12 and 15 second lockout conditions. There is a clear trend here despite the statistical results, which calls into question the statistical technique used to assess the relationships in Figure 6g. Could the authors report their statistical technique? As far as we can tell this is not described in the paper. Furthermore, is there a way the authors could analyze outlying mice behavior (M1 and M2) to understand why their results differ? M1 in particular may be very telling, because they have very few "large" EDTs to begin with as shown in Figure 6g? Can this be attributed to some behavior that M1 did (or did not do) as compared with other mice?

9) Overall, it is surprising that the observed clear and large reductions in EDTs in Figures 6g and 6h are dismissed or downplayed by the authors at several points. For example, the authors state "Taking all these factors into account, the results suggest that while EDTs increased slightly in some animals, the cause of the increase could not be disambiguated between [Ca++] state changes, animal behavior, systematic neural network restructuring due to longitudinal performance…". The title of this section reads "Lever pull EDTs are similar or slightly higher with increasing lever-pull lockout duration". The Discussion again states that "Self-caused movement confounds have only minor effects on the decoding of future rewarded action." First, the increase in EDTs is substantial (for example, in Figure 6h, the mean EDT across mice seems to increase from about -4.5 to about -2.5 sec, almost a 50% change) and calls into question the primary results. And second, while we can appreciate that many factors can worsen EDT detection as the lockout period increases (e.g., fewer sessions that occurred further apart), it is not sufficient to explain away the clear and concerning trends by listing several limitations. Especially when it seems that there may be ways to test this. For example, the authors could downsample lever pull events in the 3 sec and 6 sec lockout groups, etc., in order to match the sample size across the 5 conditions, and then repeat their analysis. Additionally, if their concern is that sessions are further apart in the longer lockout conditions, they could resample trials in the smaller lockout conditions in such a way to match the "timing" of samples across the 5 conditions, and then repeat their analysis.

Reviewer #1 (Recommendations for the authors):

Recommendations for the author

1. Our main concern with the authors' study is the lack of quiescence prior to lever pulling events. The inter-lever-pull distributions in Supplementary Figure 1.2 show that many pulls will occur within the 15-20 second window prior to a given lever pulling event. This is problematic. Suppose we analyze pull n. How are we to know that neural activity prior to lever pull n relates to that pull, when lever pulls n-3, n-2, n-1, etc., occur within the analysis window?

It seems very possible that several key results may be altered by this confound, as outlined below:

A. The elongated and oscillatory neural activity profiles in Figure 1e, Figure 3a, Figure 5a, and Figure 7a seem to us to reflect a sequence of lever pulling movements. For example, in Figure 1e, there are oscillations in Ca++ activity, with 4-5 peaks. The authors' attribute the change in peak amplitude to an increase in stereotypy as the movement approaches. An alternate hypothesis here is that the change in amplitude is related to the probability and alignment of successive pulling events. For example, there are two large peaks because two pulling events are likely to occur together. The smaller earlier peaks reflect that a third, fourth, and fifth pulling event can occur, but with a smaller likelihood and a less temporally precise coupling to pull n.

B. SVM decoding accuracy in Figures 1h, 2b, 2d, etc. shows in some cases that the earliest decoding time can precede a lever pull by 10-15 seconds. But again, how are we to know that SVM predictions in a given 1-second window are predicting an upcoming movement 10-15 seconds in the future, versus a separate pulling event that occurs within the SVM window on some subset of trials in a session? The increase in SVM accuracy as the movement approaches may be (as in A) due to changes in probability and temporal coupling in pulling events. The data are aligned to lever pull n, hence maximizing SVM accuracy at that time point.

It is unclear whether the authors can address such concerns with the existing data. One analysis that would be telling would be something akin to GLM. For example, in Figure 1e, are the oscillations due to multiple movements occurring in succession? The authors could fit a Gaussian to the last peak (from about -2 sec to 2 sec) in Figure 1e. Then the authors can take this Gaussian and convolve it with each lever pull event, and then add all these together into a composite trace that represents what neural activity should look like if it is only determined by lever pull events that follow some Gaussian distribution. Figure 1e could be reconstructed from this idealized timeseries as if it were the actual raw data. Should this control also show oscillations with increasing amplitude, this suggests that the neural activity in Figures 1e (and the SVM analyses by extension) is due to multiple lever pulls – not volitional activity that predicts an upcoming lever pull.

Similarly, as a control, the authors should also re-plot neural activity in Figures 1e, 5a, etc., using the longer post-hoc lockouts employed in Figure 6. That is, the authors should re-plot activity but exclude all trials where a previous lever pull occurred within x seconds, where x is 6, 9, 12, etc. as in Figure 6g.

Finally, another related concern seems to be the convex hull analysis in Figures 4d-g. For example, in Figure 4e it is shown that the convex hull decreases in size as the movement (t=0 sec) approaches. The authors conclude that neural activity looks more random prior to the lever pull. However, given the concerns above, it seems this "randomness" could actually be due to other lever pulls (and other spontaneous behaviors) that are not exactly temporally aligned, and not certain to occur in each sample. Thus, we would also ask the authors to repeat this analysis using longer post-hoc lockouts to prevent contamination from earlier lever pulls.

2. It is excellent that the authors considered the issue described in Point (1), in their control analysis in Figure 6. But we disagree with their conclusions. Figures 6g and 6h clearly show that EDT drops considerably as the lever lockout window increases. For example, the 9, 12, and 15 second lever lockout periods appear to mostly eliminate all sessions with EDTs greater than 5 seconds. This is a drastic reduction in the time horizon (10-15 seconds) argued by the authors given their earlier results.

The authors might argue that these results are mixed, given that they were not statistically significant in M1 and M2 (Figure 6g). But this is misleading. In M1, there are only 2 or so EDTs greater than 5 seconds in the 3 second lockout condition. There are none in the 6 second lockout condition. Thus, in this mouse, the lack of statistical significance is simply because there are little to no EDTs to eliminate by gradually increasing the lever lockout period. This analysis is not useful in a mouse that exhibited little to no EDTs in the 10-15 second range argued by the authors. For M2, it is surprising that no statistically significant effect was detected given that all EDTs greater than 5 seconds are eliminated in the 12 and 15 second lockout conditions. There is a clear trend here despite the statistical results, which calls into question the statistical technique used to assess the relationships in Figure 6g. Could the authors report their statistical technique? As far as we can tell this is not described in the paper. Furthermore, is there a way the authors could analyze outlying mice behavior (M1 and M2) to understand why their results differ? M1 in particular may be very telling, because they have very few "large" EDTs to begin with as shown in Figure 6g? Can this be attributed to some behavior that M1 did (or did not do) as compared with other mice?

Overall, it is surprising to us that the observed clear and large reductions in EDTs in Figures 6g and 6h are dismissed or downplayed by the authors at several points. For example, the authors state "Taking all these factors into account, the results suggest that while EDTs increased slightly in some animals, the cause of the increase could not be disambiguated between [Ca++] state changes, animal behavior, systematic neural network restructuring due to longitudinal performance…". The title of this section reads "Lever pull EDTs are similar or slightly higher with increasing lever-pull lockout duration". The Discussion again states that "Self-caused movement confounds have only minor effects on the decoding of future rewarded action."

First, the increase in EDTs is substantial (for example, in Figure 6h, the mean EDT across mice seems to increase from about -4.5 to about -2.5 sec, almost a 50% change) and calls into question the primary results. And second, while we can appreciate that many factors can worsen EDT detection as the lockout period increases (e.g., fewer sessions that occurred further apart), it is not sufficient to explain away the clear and concerning trends by listing several limitations. Especially when it seems that there may be ways to test this. For example, the authors could downsample lever pull events in the 3 sec and 6 sec lockout groups, etc., in order to match the sample size across the 5 conditions, and then repeat their analysis. Additionally, if their concern is that sessions are further apart in the longer lockout conditions, they could resample trials in the smaller lockout conditions in such a way to match the "timing" of samples across the 5 conditions, and then repeat their analysis.

3. There is another point to check here, related to these matters. If we understand correctly, the authors used a post-hoc lockout of 3 seconds in their primary analyses (e.g., Figures 2b and 2d). Other lockout periods are tested in the controls in Figure 6. We are curious what constitutes a lever pull in these post-hoc lockout periods? In other words, there is a threshold beyond which the lever needs to be pulled to be rewarded (e.g., the 15{degree sign} angle illustrated in Figure 2a). When the authors exclude lever pulls that were preceded by another lever pull in the lockout window, was it required that the preceding lever pull also exceed the 15{degree sign} threshold? Or was another threshold used? On pg. 27 it says that rewarded and non-rewarded lever pulls were counted as lever pulls in the lockout period. What is meant by unrewarded here: unrewarded because the lever pulls did not meet the minimum threshold, the minimum hold duration for the pull, not hitting the maximum value (as described in Methods)?

The concern we are getting at is that if the minimum 15{degree sign} threshold was used to determine lever pulls in the lockout window, the authors may be not counting smaller (and/or less purposeful) unrewarded lever pulls in the lockout window. Should these pulls also modulate cortical activity, this would pose a problem with regards to Points 1 and 2 above. That is, it seems to us that mice should be as quiescent as possible (excepting perhaps licking / grooming) during the lockout window, to be certain that EDT estimates are not inflated by smaller lever pulls (actions) that may be missed in the lockout period.

4. A response to these concerns would greatly benefit from deeper behavioral analyses. The authors should consider whether pre-pull behaviors are associated with EDTs. We have several suggestions on this point:

Might the authors be able to use a classifier on their videos, to see whether pre-pull behaviors (e.g. subthreshold pulls, paw movements unassociated with the task, licking, grooming, random body motion, rewarded pulls, etc.) can predict future behavior? In other words, the authors could analyze whether classifier output is related to EDT. For example, when an EDT is -5 sec in a given session, are there animal behaviors occurring at -5 sec which might be predictive of the upcoming pull?

The authors have a heterogeneous mouse population. Mouse M1 seems to have a smaller time horizon in their EDTs (as noted in Point 2 above) in Figure 6g. The authors should compare this mouse's behavior to other mice. For example, in the pre-pull period, does Mouse 1 engage in more/less behaviors that might explain why their EDTs differ from other mice.

Are past behaviors predictive of future lever pulls? That is, do mice perform movement sequences of lever pulls (both rewarded/unrewarded)? Do the periodicity of movement sequences relate to EDTs? When sequences are more common in a session, does this appear related to EDT? Do actions such as licking, grooming, body motion, etc. predict upcoming lever presses? If so, does the relative timing between actions relate to EDTs on a given session?

Providing video data in example sessions across the EDT spectrum (e.g. some sessions where EDT was -10 sec, and others where it was -5 sec), could be helpful to the reader in interpreting changes in animal behavior/state that might occur near the EDT.

Reviewer #3 (Recommendations for the authors):

• The authors need to make a stronger case regarding the novelty of their study as compared to Murakami et al., 2014, 2017 and da Silva et al., 2018.

• By definition, voluntary action should be reason-responsive. Do the authors have any data to show lever-pull actions are goal-directed?

• Panels a,b,f in Figure 1 do not seem to contain any new information and could simply be mentioned in the text.

• Please report the exact p-values.

• I am not an SVM expert but is it surprising to see decoding accuracy increasing with number of trials? What is the significance of this finding? This also refers to decoding data from multiple cortical regions rather than a specific region. Isn't it expected to see better decoding accuracy with more data?

• There are multiple interesting findings throughout the paper, but the authors failed to explain their significance. For example: (A) they found a higher bias of phases: i.e., behaviours were more likely to occur during a specific phase in mice than in humans. (B) There was a high diversity of phase preference between animals. (C) EDT improved longitudinally. (D) Limb and motor cortex oscillations had most power during pre-movement neural activity [but each animal showed a different pattern]. These are interesting findings but what are their significance and how do they contribute to addressing the research question?

• The statistics are reported in quite an unusual way. Sometimes it is just described qualitatively without any inference test. Sometimes it is reported separately for each animal and sometime across the group. It is quite hard to make any conclusion about the findings when animals show different and in some cases opposite behaviour. Maybe the authors could perform mixed-effect modelling with by-subject random slope and intercept to investigate whether any of the findings is consistent across the animals and significant at the population level.

• The authors conclude that "Overall, these results link human voluntary action studies with rodent self-initiated behavior studies and suggest mice as an adequate model for studying the neural correlates of self-initiated action". This conclusion is not justified without a control condition contrasting self-initiated with externally-triggered action.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement" for further consideration by eLife. Your revised article has been evaluated by Christian Büchel (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below. In particular, the reviewers would like the authors to respond to the following comments, providing edits to the manuscript as needed (please see the full reviews for further elaboration).

1) It seems the most likely interpretation is that EDTs relate to complete, or at least partial, pauses in behaviors (see Reviewer #1's recommendations for details).

2) In Figure 3, where the authors discuss their finding that movement initiation tends to coincide with peaks in a slow neural oscillation, this interpretation seems potentially problematic, as this oscillation could more simply be a decrease in neural activity during the pause in motor behaviors, and then the subsequent increase during the active motor period (so basically, an ON-OFF-ON transition in neural activity that is correlated to distinct motor events rather than some latent oscillation). An analysis that compares the duration of "quiescent periods" (nicely highlighted in Figure 1-Supp. 5c,d) to EDT is necessary to understand how to interpret these results (see Reviewer #1's recommendations for details).

3) The revised results are not as strong as in the original manuscript. More rigorous analysis of the EDTs revealed that the presence of sequential lever pulls had artificially lengthened the EDT, as anticipated by the reviewers. Additionally, the authors now acknowledge that some of the results are not as reliable as previously reported (i.e., large variability across animals in neural activity phases and longitudinal changes in neural dynamics), weakening the main conclusions of the paper.

4) The reviewers would like the authors to comment on the claim that they have identified a specific neural signature of self-initiated voluntary action. The authors show that pre-movement neural activity in mice contains structures that are not present in random neural activity. This observation is well supported by the data. However, to claim that this structure – neural dynamics becoming increasingly stereotyped prior to movement – is specific to self-initiated actions one needs to show that pre-self-initiated movement (uncued) neural activity in mice contains structures that are not present in externally-triggered movement (cued) neural activity. This comparison could also rule out other possible explanations such as motor confounds associated with the lever pulls or other related micro-movements. Comparison with random time during the task is not sufficient to make any conclusion regarding the self-initiated nature of the behavior.

Reviewer #1 (Recommendations for the authors):

I commend the authors on the care they have taken to address reviewer concerns. The added attention to 15-second lockout data is very useful. I think the focus on the 15-second lockout periods largely addresses the issue that sequences (or even random periods of movement) bias EDT estimates. Figure b (rebuttal Page 3) aligns with the updated EDTs in Figure g on Page 4. That is, neural activity changes around 3-4 seconds prior to the lever pull event in Figure b (Page 3), and EDTs seem largely capped at 3-4 seconds in Figure g (Page 4). In addition, there is evidence that mice are sensitive to the 3-second experimental lockout, given the drop in movement probability as shown in Figure c (Page 6). Thus, there does appear to be a 'cognitive decision' associated with obtaining the reward.

But the clear analyses presented by the authors, highlight a new issue that has potential implications for the way they interpret the data. An interpretation that seems consistent with the analyses detailed on Pages 2-6, is that changes in neural activity related to EDTs are less so about pulling the lever, and more so about suppressing actions. For example, in Figure 2-Supplement 2, we see a gradual decline in activity in many brain regions. It appears that the onset of this decline aligns with EDT estimates via SVM. This decline appears to align with the movement data shown in Figure 1-Supp. 5c and 5d, which shows that overall movements start to decline about 3-5 seconds prior to the lever press event. And again, EDTs in the 15-second lockout datasets, appear to saturate around 3-5 seconds. And neural variability decreases in that window (Figure 5). Thus, it seems to be that this decline in spontaneous behaviors is related to the decline in neural activity, and the EDT estimates obtained by SVM. On the other hand, neural activity exhibits a sharp spike 500 ms prior to lever press (Figure 2-Supplement 2) which peaks at the same time as a sudden burst of all sorts of movements (not just lever presses) as seen in Figure 1-Supplement 5.

It seems the most likely interpretation is that EDTs relate to complete, or at least partial, pauses in behaviors. The mouse has learned to suppress movement, and these movement suppressions are needed to acquire the reward. These movement suppressions begin at about 3-5 seconds because this is indeed the experimentally imposed lockout period. In other words, mouse behavior seems to represent a sequence of actions: pause for 3 seconds, then move.

At the end of the day, this does still relate to volition. Without a cue, the mouse chooses to start a sequence, pause then move. But the idea that mice have learned a sequence, pause then move, does not seem to align with many critical points the authors make: e.g., "structured multi-second neural dynamics preceding self-initiated action" as claimed for example in the abstract, or the paper's title. Rather, if the mice are performing a sequence, pause then move, then the initial changes in neural activity (which are detected in EDT) are not about lever press, but about the pause that starts the sequence. In other words, it seems if the mice have a state change from movement to quiescence (and then lever press), then the EDTs should be detected not relative to the lever press but the movement quiescence that begins the "sequence".

Another related issue might be the interpretation in Figure 3, where the authors discuss their finding that movement initiation tends to coincide with peaks in a slow neural oscillation. This interpretation seems potentially problematic, as this oscillation could more simply be a decrease in neural activity during the pause in motor behaviors, and then the subsequent increase during the active motor period (so basically, an ON-OFF-ON transition in neural activity that is correlated to distinct motor events rather than some latent oscillation).

Thus, while overall the mice do clearly show that they are able to volitionally control themselves for at least 3-5 seconds to obtain the reward (i.e., stop moving), this should not be taken to mean that the neural activity during this period is necessarily important to the upcoming lever press. Stated very succinctly, it is problematic to assign EDT-related neural activity to the lever press, when there is a large coincident change in the behavioral state of the animal that precedes the lever press (the complete or partial suppression of behavior). It seems more analysis on this quiescence is very important to the paper's interpretation of their results.

Data that are needed on this point would be an analysis that compares the duration of "quiescent periods" (nicely highlighted in Figure 1-Supp. 5c,d) to EDT. For example, I anticipate that occasions where the animal pauses body movements for a longer period prior to the lever press will relate to longer EDTs. On this note, it would be very helpful for the authors to provide an analogue of Figure 1-Supplement 5 (Panels c and d) for the 15-second lockout data specifically, and for all their mice as opposed to solely mouse M1. Do 15-second lockout data also show the 3-5 second quiescent periods prior to lever pulls? Another suggestion on this point: the error bars in Figure 1-Supp. 5d are large: both in the "pause" prior to time point 0, and the peak in activity at time point 0. This implies some heterogeneity in the extent to which animals pause, and the action they take after a pause. The authors could take advantage of this. If the authors binned trials based on (a) complete pause, (b) partial pause, and (c) no pause, do these groups show different neural activity patterns in the -5 to 0-sec range in Figure 2-Supp. 2 (a similar binning analysis could be done based on the length of the pause.)? If so, again, these analyses would be a reason to suspect that much of the neural signal change detected in the EDT has to do with a pause in activity prior to lever press, as opposed to the lever press itself. Trial-to-trial analyses could also be conducted on this issue, relating the pause duration (or perhaps pause magnitude, e.g., the extent to which behaviors stop prior to lever press) relates to neural activity on that trial (to this end, GLMs may be useful to parse overall activity into component behaviors).

Reviewer #2 (Recommendations for the authors):

This is the second review of the manuscript "Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement".

Overall, the authors have addressed in detail the concerns raised by the previous round of reviews.

On the one hand:

1) The authors have appropriately toned down claims about "voluntary" movements.

2) The authors have also performed new analyses to address the concerns about sequences of stereotyped movements but did not address the concerns of micro-movements due to a lack of proper video data. This limitation was acknowledged in the manuscript and I have no additional concerns in this respect.

On the other hand:

3) The revised results are not as strong as in the original manuscript. More rigorous analysis of the EDTs revealed that the presence of sequential lever pulls had artificially lengthened the EDT, as anticipated by the reviewers. Additionally, the authors now acknowledge that some of the results are not as reliable as previously reported (i.e., large variability across animals in neural activity phases and longitudinal changes in neural dynamics), weakening the main conclusions of the paper.

4) There are a few typos in the manuscripts, for instance, on page 11, line 224: double "also".

Reviewer #3 (Recommendations for the authors):

The authors have done a great job addressing the comments. The new analyses and figures have significantly improved the readability and quality of the manuscript.

I am, however, still not completely convinced by the authors' claim that they have identified a specific neural signature of self-initiated voluntary action. The authors show that pre-movement neural activity in mice contains structures that are not present in random neural activity. This observation is well supported by the data. However, to claim that this structure – neural dynamics becoming increasingly stereotyped prior to movement – is specific to self-initiated actions one needs to show that pre-self-initiated movement (uncued) neural activity in mice contains structures that are not present in externally-triggered movement (cued) neural activity. This comparison could also rule out other possible explanations such as motor confounds associated with the lever pulls or other related micro-movements. Comparison with random time during the task is not sufficient to make any conclusion regarding the self-initiated nature of the behavior.

https://doi.org/10.7554/eLife.76506.sa1

Author response

Essential revisions:

1) There was confusion amongst the reviewers as to the precise definitions of what the authors called "voluntary" movements. Providing a more precise definition (and the appropriate controls, as described below) will be important.

We agree with the reviewer that the term “voluntary” was not adequately defined. In fact, given the comments of the reviewers we prefer to use the term “self-initiated” as it better describes our paradigm. Thus, when referring to mouse behaviors studied in our paradigm we replaced the word ‘voluntary’ with ‘self-initiated’.

2) One of the goals of the authors was to study the neural mechanisms underlying voluntary movements. While they acknowledge (in the discussion) that they do not have evidence that actions are "intentional", they make the assumption that mice do "form the intent to act near the lever pull time".

We thank the reviewers for their comments and agree that we need to clarify the focus of study. We do not intend to solve the issue of intent in human or nonhuman animals in our study and we hope that the revisions made to the main manuscript make it increasingly clear of the findings and limitations of our work. We have now removed most all mentions of the term “intent” when referring to results from our study and changed the language to refer only to “self-initiated” behaviors in mice.

To back up this assumption, the authors should at least present some evidence that the action of interest (i.e., the rewarded lever-pull) is not just a random jerky movement that happens to be rewarded once in a while. In fact, mice seemed to pull the lever very frequently and impulsively (the majority of inter-pull intervals were way below 3 s in Supplementary Figure 1.2) even for the last sessions of the training. Therefore, it is not readily apparent that mice apply any control to their lever-pull actions. Providing evidence that the action is goal-directed is important if the goal of the paper is to study neural signatures of the intention to act.

We view habitual or innate behavior (or even “jerky movements”) as those relating to spontaneous grooming or periodic whisking, and as such could amount to instinctual, non-task-oriented behavior.

In contrast, our task is of sufficient complexity to elicit goal-directed behavior:

(a) The mice must seek out a lever in space (it is not touching their body and their right paw is not attached to it). That is, the task is not a natural or trivial one as mice must grab and grip the lever (i.e. it is not attached to their paws) and pull it in a specific direction. Rewards cannot be generated by random body movements or jerks of the paws or limbs of the mice as the lever is located to the side of the mice (see Figure 1—figure supplement 1) and does not prevent or block random limb movements.

(b) The mice must pull the lever and hold it for at least 0.1 sec (and we note many lever pulls failed this requirement).

(c) The mice must also learn to wait a minimum of 3 sec before attempting a new lever pull in order to get a water reward. Mice appear to have learned the 3sec reward lockout condition. The peaks in inter-lever-pull histograms (Figure 1—figure supplement 2), also shows that four of six mice had peaks in the inter-lever-pull histogram at ~ 3 seconds indicating that mice internalized, or “learned”, the lockout period in their behaviors and carried out many lever pulls approximately 3 secs after a previous lever pull.

(d) Moreover, we now cite previous work by the lab from Silasi et al., 2018 (paragraph from the main text of that study is included below), where mice were able to employ advanced forms of the lever pulling task described in our manuscript here during home cage training: e.g. increasing the hold duration and decreasing the goal range. As evidence that the mice were learning after training, in that study we relaxed and then escalated task difficulty (a second time) and mice were able to reach criterion significantly faster, indicating learned behaviour.

“Previously, we have evaluated the extent to which the lever pulling task reflects learned behaviour. This was assessed by finding that mice were able to adjust to increasing task difficulty: mice were able to hold the lever longer, or over a narrow rewarded goal range. These actions reflected learning and not random behavior since when criteria were relaxed and then reinstated mice progressed more quickly when trained a second time.” (Silasi et al. 2018).

(e) We also find evidence of increased lever-pull activity within each session. In response to the reviewers’ comment, we have also generated Figure 1—figure supplement 4 which shows every rewarded lever pull across all animals and sessions. This figure shows that the average number of lever pulls across each session in 4 mice (M3-M6) increases across time. That is, within each ~20minute session, these 4 mice perform increasingly more water rewarded lever pulls across the session (on average). This supports the view that the mice are aware of being in a paradigm where they can receive water rewards and are seeking to perform a behavior that results in water.

Taken together, these results support that mice learned the association between 3-sec lockout lever pulls and water rewards and engaged in lever pulls as a learned goal-directed behavior.

A somewhat compelling analysis could be to compare rewarded lever-pulls with "spontaneous" movements, provided that these two types of movement can be convincingly characterized as goal-directed vs. incidental. In contrast, throughout the manuscript, the neural activity aligned to rewarded lever-pull events (which are assumed to be "voluntary" actions) is compared to the neural activity aligned to random times during the task (whether or not it involved movements), which may not be the most convincing control.

One of our study’s findings is that upcoming self-initiated behaviors (implicitly covering “goal-oriented” and “non-goal-oriented behaviors”) do not arise from random processes, but that they have non-random neural correlates and they can be decoded prior to movement. From our original manuscript abstract:

“Several human studies have found peaks in neural activity preceding voluntary actions, e.g. the readiness potential (RP), and some have shown upcoming actions could be decoded even before awareness. Others propose that random processes underlie and explain pre-movement neural activity. Here we seek to address these issues by evaluating whether pre-movement neural activity in mice contains structure beyond that present in random neural activity. [….] Our findings support the presence of structured multi-second neural dynamics preceding voluntary action beyond that expected from random processes.”

In support of our conclusion, we compared neural activity from both self-initiated lever-pulls and other body movements to random periods of time. This paradigm replicates the human studies on this subject: our study’s paradigm is not new (or intended to be new) as both “self-initiated” and “spontaneous” behaviors have been studied in this field (e.g. the original Kornhuber and Deckee studies in the 1960s; Libet et al. 1983). Our analysis is nearly identical to that of human fMRI studies (see Soon et al. 2008, 2011; Bode et al. 2013). There is nothing inherently controversial or new in our paradigm nor in the method we implemented for computing EDTs.

Additionally, we point out that we did not design our experimental paradigm to enable decoding of self-initiated rewarded lever pulls from other body movements such as paw movements. In our study, limb movement initiations often occurred near simultaneously to the lever pulls and could not be disambiguated easily, thus making the reviewers’ suggestion difficult to implement. To show this, we generated Author response image 1 showing high correlation peaks between body movements and lever pulls:

Author response image 1
Cross-correlation between lever pull and body movement initiations.

(a) Visualizating body movement times (colored lines = boolean values) and lever pull locations (red dots) in a ~50sec window shows that many body movements occur in groups, are preceded by seconds of non-movement and often temporally coincide with lever pulls (example data was taken from mouse M1). (b) Zoomed in section from (a). (c) The cross-correlation between lever pull initiation time and the left paw, right paw and licking bout initiations (for session data in (a)). (d) The average (normalized) cross correlation (continuous lines) and standard deviation (shading) for all sessions in mouse M1 between lever pull and left paw, right and tongue movement invitations. (note: we obtained similar results for mice M2-M6).

In sum, panels a-b show that lever pulls generally occur after quiescence periods, i.e. there is a decrease in body movements and licking activity in the few seconds prior to a lever pull. Panel c shows no strong peaks in body movements preceding the lever pull on the scale multiple seconds. Panel d shows that in the period of approx. -5 to approx 0 seconds prior to a lever pull there is a decrease in correlated activity between body movements and licking.

These results suggest that: (i) many body movements coincide with the lever pull time (making them more difficult to disambiguate); and (ii) that mice tend to decrease their overall body movements a few seconds prior to lever pull. Taken together, these results support that neural signals in the preceding few seconds prior to lever pulls likely represent ongoing neural dynamics of behavior preparation rather than performance (see below also for further analysis on this point).

We have made changes to the manuscript to address the reviewers’ comments on this particular issue to be more consistent with the paradigm and findings. For example, we added this paragraph to

“We developed an analogous self-initiated behavior paradigm in six mice (M1-M6) to characterize pre-movement neural activity while recording widefield [Ca++] activity from cortex (Figure 1c, d, Figure 1—figure supplements 1,2; see also Methods). Mice were head fixed and trained to perform a self-initiated lever-pull to receive a water reward without sensory cues or stimuli. Four of six mice learned the lever lockout period of 3 sec (Sup Figure 1.2 shows peaks at 3-sec in the inter-lever-pull intervals in mice M3-M6) and four of six mice learned to pull increasingly more often towards the end of each ~20 minute session (Figure 1—figure supplement 4, mice ). Mice tended to decrease their body movements prior to a lever pull and we did not find evidence of stereotyped behaviors prior to lever pull (Figure 1—figure supplement 5; see also Methods section on detecting stereotyped movements).”

3) The learning trajectory of mice is also not well characterized (e.g. changes in inter-pull intervals are not quantified, nor the relative increase in rewarded actions across training sessions, etc.). Yet, several claims in the paper are directly based on the fact that mice have learned to pull the lever after 3 s interval to receive water rewards (which relates to point 1).

We agree with the reviewers' concerns regarding the lack of characterization of task learning in our original manuscript. We have addressed this in Essential Revision Point 2 (see comments above). There, we pointed to the design of the task including the presence of lockouts, duration of lever hold and systematic increase in lever pulls across a session. We also pointed to a recent paper in our lab using a similar lever pull paradigm with increasingly challenging parameters that mice also performed well on (Silasi et al. 2018). While lever-pulls were central to our study, learned advancement of lever difficulty was not required and we focused on the cortical activity patterns around each action.

In particular, one important assumption in the paper is that as mice learn, the lever-pull movements become more stereotyped, but this has not been shown explicitly. It would be helpful, for example, to see how analog traces of lever-pulling change throughout the learning stages and how the variance of the movement across trials decreases in late sessions.

We regret this confusion potentially caused by our manuscript’s original language (e.g. Figure 4), but our claim with respect to longitudinal performance is primarily that neural activity becomes more stereotyped – not paw trajectories.

While it is not central to our study we agree with the reviewer that it is possible that trajectories of the right paw could become more stereotyped over time (e.g. see Kawai et al. 2015). Although not directly affecting our main findings, we have generated Author response image 2 to evaluate any stereotyped structure in the movements of the right paw in mouse M6 (which had by far the most sessions and recording hrs: 70 sessions and > 13 hours of video recordings).

Author response image 2
Evaluation of systematic longitudinal changes path in right paw movement trajectories.

Panel (a) shows UMAP applied on 1-sec long right paw movement segments from all sessions in mouse M6. For clarity, we took the DeepLabCut detected locations of the right paw, split them into continuous, non-overlapping 1 sec segments (and centered them, i.e. removed any offset as we were interested in relative changes in the 1 sec period), and fed it into UMAP (i.e. each example had dimensions of: [n_time points, n_spatial_locations] = [15, 2] in this panel). Panel (b) is the same as (a) but for 5 sec long movement snippets.

In sum, we do not find clusters of stereotyped activity nor correlations between the UMAP distribution shape and session time.

4) The central claim of the paper is that rewarded lever-pulls can be predicted from pre-movement neural activity several seconds (even up to 10 s) prior to the action. However, obvious motor confounds and other alternative explanations have not been convincingly ruled out. In fact, the action of lever pulling may require a series of complex movements (like changing posture, extending the forelimb, reaching the lever, grabbing the lever, etc.). The authors themselves mentioned that they found strong correlations between lever pulls and body movements in all mice, but the data is not used nor shown in the paper. The motor commands preceding but related to lever-pull could unfold at least a few hundreds of milliseconds prior to the detection of lever-pull in the task, and thus be reflected in the neural activity that is predictive of the lever pull. Moreover, if this series of movements is highly stereotyped, and in turn leads to stereotyped neural activity (like the slow oscillations observed before the lever-pulls), it could explain why the detection of lever-pulling actions always occurs at a given phase of the neural oscillation.

We agree with the reviewers that analyzing the presence and effect of pre-lever pull movements is important to our study’s findings relating to the absolute values of EDTs. We provide the following evidence that such stereotyped series of movements do not exist in the behaviors of our mice:

  1. In sum, this figure shows that in the few seconds prior to lever pulls, mice tend to decrease their average number of body movements, with some sessions (as the example session shown in panel c) having very few behaviors prior to lever pulls. Additionally, we used PCA to search for correlated sequences of activity and were not able to identify any (see Methods for more details on this analysis).

  2. While we agree with the reviewer that motor command preparation of lever pull behaviors likely takes O(100ms) or more and is present in pre-movement neural activity, we did not find evidence that stereotyped behavior sequences lasting a few to several seconds prior to lever pulls were present in our data. Our data suggests that the 3sec lockout has the effect of causing mice to enter into increasingly quiescent states prior to attempting a lever pull for water reward.

Such observations that stereotyped movements occur way before the lever-pull detection could partially rule out the fully "cognitive" explanation proposed in the paper, but would concur with recent findings that showed that ramping neural activity can be, for the most part, explained by movement-related activity (Musall et al., 2019).

As shown above, we do not find evidence for stereotyped movements occurring “way before” lever pulls.

With respect to Musall et al. 2019: that study was more focused on decoding contemporaneous behaviors whereas our study focused on detection of future behaviors. The behavior paradigm in Musall et al. 2019 contains short periods of behavior, e.g. holding levers for 1 sec, processing stimuli for 0.6 sec, a pause of 0.5 sec, a stimulus for 0.6sec, and a lick movement after a 1 sec window. The findings of that study suggest that while performing learned tasks during these different task segments, mice can also engage in non-task related behaviors that have neural correlates which are detectable in widefield [Ca] recordings.

The paradigm we study in this manuscript is different from that studied by Musall et al. 2019 due to its stimulus-free nature where the only constraint was a minimum of 3 sec of task lockout (or 15 seconds of post-hoc lockout). Importantly, while spontaneous behaviors (and their neural correlates) can occur while mice are head fixed – there is no evidence in Musall et al. 2019 that there are sequences of behaviors preceding rewarded behavior.

As additional evidence that mice do not transition between multiple behavioral states during the period of approx -5 sec to – 3sec, we also generated Figure 2—figure supplement 2 which shows that there is only one neural motif occuring during this period.

The panel shows an increase in inhibition across forelimb (red), hindlimb (purple) and motor (blue) areas lasting a few seconds and then a rapid increase in neural activity in the 1-2 seconds prior to movement. There is no evidence for multiple phases or oscillations in this motif. This single phase of inhibitory dynamics is inconsistent with those supporting body movements (which generally require activation of cortex; though the time course of the [ca] reporter may affect the dynamics). This time course is also inconsistent with the presence of multiple sequential movements.

In sum, we did not find evidence for the presence of multiple behavioral motifs as identified in Musall et al. 2019. We find evidence only for a single neural motif which involves distributed inhibition over many areas during a behavior preparation period of several seconds prior to lever pull followed by a return to baseline prior to lever pull and an increase in activity past t=0 sec (shown in other panels, e.g. Figure 1).

5) Toward the end of the result section (Figure 6), the authors briefly begin to address the issue about whether pre-movement activity can really be considered movement free. Here, "lockouts", i.e. periods where other movements (like licking, or previous lever-pulls) did not occur, were introduced in the analysis. The lockouts altered the earliest-decoding-time (EDT) of the lever-pull (in some mice EDT was even divided by half: from -4 s to -2 s). However, the effects of "micro-movements" like facial movements or changes in body posture may not be taken into account with the lockout approach. Such micro-movements have been shown to explain a large variance of the neural activity (see Stringer et al. 2019 and Musall et al. 2019). Therefore, to fully control for movement confounds, the effect of high dimensional/micro-movements extracted from video recordings should be removed from the neural activity. These analyses could yield a much shorter EDT (e.g., -0.15 s), more consistent with previous reports.

We thank the reviewers for this concern regarding micro-movement effects on neural activity. Given our video recording and experimental paradigm, we were not in a position to track or regress out micro-movements including those occurring in facial muscles. It is indeed possible that multiple forms of “micro-movements” could occur during the period prior to a level pull.

However, we do not believe that such micro-movements – even if present in our study – would affect EDTs obtained in our study for reasons outlined above and summarized here.

  1. Neither Musall et al. 2018 nor Stringer et al. 2019 reveal the presence of sequences of body movements (or micro-movements) before rewarded behavior, nor of such body movement sequences lasting a few to several seconds.

  2. We did not find evidence for stereotyped sequences of movements prior to a lever pull (Essential Revision Point 3).

  3. We did not find evidence for the presence of a series of movements in the neural data (Essential Revision Point 4).

  4. The average neural dynamics occurring in the period of approx. -5 to 0 seconds prior to lever pulls is inhibitory and is inconsistent with the presence of multiple movements which generally require increases in neural activity – not inhibition (Essential Revision Point 4).

  5. The average neural dynamics occurring in the period of approx. -5 to 0 seconds prior to lever pull has a single multi-second (inhibitory) phase which is unlikely to support multiple complex movements or micro-movements.

Based on the reviewers’ suggestion, we have now added the following paragraph in the discussion on this topic.

“The effect of confounding movements on the decoding of future rewarded action. Our study does not directly address the effects of stimuli or other perturbations on the neural dynamics preceding self-initiated decisions, for example, as in some human choice paradigms that consider decision choice in the presence of novel information (e.g. Maoz et al. 2019). Additionally, there is evidence that micro-movements can contribute to ongoing neural activity between behavior bouts (Stringer et al. 2019 and Musall et al. 2019). While it is possible that in preparing for a self-initiated rewarded behavior mice undergo a series of physical movements, we did not see any evidence for this in our recordings. In particular, we found a tendency for mice to decrease their body movements in the 3-5 seconds prior to self-initiated action and we did not find evidence for sequences of paw or licking movements. We suggest future studies focusing on the micro-movements underlying self-initiated action could address this issue using high-frame and high-resolution video recordings.”

6) SVM decoding accuracy in Figures 1h, 2b, 2d, etc. shows in some cases that the earliest decoding time can precede a lever pull by 10-15 seconds. But how are we to know that SVM predictions in a given 1-second window are predicting an upcoming movement 10-15 seconds in the future, versus a separate pulling event that occurs within the SVM window on some subset of trials in a session? The increase in SVM accuracy as the movement approaches may be (as in A) due to changes in probability and temporal coupling in pulling events. The data are aligned to lever pull n, hence maximizing SVM accuracy at that time point. It is unclear whether the authors can address such concerns with the existing data. One analysis that would be telling would be something akin to GLM. For example, in Figure 1e, are the oscillations due to multiple movements occurring in succession? The authors could fit a Gaussian to the last peak (from about -2 sec to 2 sec) in Figure 1e. Then the authors can take this Gaussian and convolve it with each lever pull event, and then add all these together into a composite trace that represents what neural activity should look like if it is only determined by lever pull events that follow some Gaussian distribution. Figure 1e could be reconstructed from this idealized timeseries as if it were the actual raw data. Should this control also show oscillations with increasing amplitude, this suggests that the neural activity in Figures 1e (and the SVM analyses by extension) is due to multiple lever pulls – not volitional activity that predicts an upcoming lever pull.

We agree with the reviewer’s concerns that sequential lever pulls could affect the EDT decoding times. Our original manuscript raised this concern as noted in the introduction to Figure 6:

“For example, in animals that perseverate and pull the lever frequently it is not known whether decoding methods leverage dynamics from multiple lever pulls or just the lever pull occurring at t=0sec.”

The issue raised here in Essential Revision Point 6 (whether simulations provide insight into EDTs and oscillations), Essential Revision Point 8 (interpreting the effect of lever-pull lockout) and Essential Revision Point 9 (effect of downsampling non-lever pull lockout data) are all related to the effect of sequential lever pulls on decoding. We largely agree with the reviewers’ concerns and generated additional figures and analyses to address these points (discussed below). Briefly, our findings are that:

  1. We find evidence that sequential lever pulls can generate stereotyped oscillatory-like activity that likely decrease EDTs (Essential Revision Point 6).

  2. While “locking out” previous lever pulls for periods of 15 sec (to remove the effect of sequential level pulls) shortens EDTs averages by 1-2 seconds (as shown in original manuscript Figure 6), filtering the neural dynamics prior to EDT computation results in EDTs that are lengthened by 1-3 seconds. This filtering step results in 15 sec lockout EDTs averages falling between -3 to -7 seconds (see updated manuscript and below). (Essential Revision Point 8).

  3. Subsampling non-lockout data to match lockout data results in average EDTs shortening by 1-2 seconds, similar to locking-out effects. This supports that pooling across longitudinal data decreases the ability to decode behavior (Essential Revision Point 9).

We proceed to address the specific issue in Essential Revision Point 6, i.e. whether simulated data provides insight into the sequential lever pull issue (we leave the analysis for Essential Revision Points 8 and 9 below). Based on the reviewers’ comments we generated the Author response image 3. Consistent with the reviewers suggestions, we find that in some sessions simulated neural data based on lever pull times can generate oscillations similar to those observed in the neural data. Additionally, oscillations in locked-out neural data seem to decrease for some animals.

Author response image 3
Generating synthetic “neural” data by convolving lever pull times and gaussians.

(a) A synthetic neural signal obtained by convolving each rewarded lever pull time within a single session with a gaussian kernel note: we used 1.5sec std gaussian, which gives about a 3.5-4.5sec wide signal; 1 sec or 2 sec std gaussians gave similar results; random data was generated by shifting the times by a random value drawn from U(-25sec,25sec). (b) Average synthetic neural data (blue) centered on the lever pulls and the random control neural signal (red). (c) Four example sessions from different mice showing synthetic data (note: M4 had many trials and shuffling -/+25 secs still yielded a “bump” in the random average; a higher shift value removes this effect). (d) The real (not simulated as in a-c) average of motor cortex activity for no lockout (blue) vs. 21 sec lockout (red; note: we used the 21 sec as the longest window still yielding sufficient data) conditions shows that some animals continue to have small oscillations even when locking out previous lever pulls.

In sum, this figure shows that simulated neural data based on the number and times of level pulls can generate oscillations observed in real neural data (Panels a,b,c). Consistent with the reviewer’s concerns, some of oscillations observed in real neural data might thus not be related to preparation of the decoded lever pull but a previous lever pull. Oscillations are often decreased in real neural data when locking out sequential lever pulls (Panel d: decrease in lockout curve (red) oscillations compared to all trials (blue) in mice M1, M4 and M6). However, we also observe that even after lockout real neural data small oscillations can be present in some animals (e.g. note small red oscillations in Panel (d) M1 and M3). Overall, we agree with the reviewers that multiple lever pulls can have a causal effect on neural activity. We added the following text to the main manuscript (along with several changes as described below):

“We find that oscillations observed in the neural data are likely enforced by repetitive and stereotyped recent lever pulls and that EDT analysis requires exclusion of trials that occur too soon after a previous lever pull (here we chose a lockout of 15 seconds).”

Similarly, as a control, the authors should also re-plot neural activity in Figures 1e, 5a, etc., using the longer post-hoc lockouts employed in Figure 6. That is, the authors should re-plot activity but exclude all trials where a previous lever pull occurred within x seconds, where x is 6, 9, 12, etc. as in Figure 6g.

We agree with the reviewers’ comments and have amended the manuscript as follows:

  1. Figure 1e was replaced with a panel generated from trials where a 21sec lever-pull lockout was enforced (we chose 21sec to reflect the time range of the original panel).

  2. We replaced Figures 1h with decoding curves generated following a 15 sec lockout:

In response to this Essential Revision Point (and others below), we made several additions and changes to our manuscript including the following statements (in multiple sections of the manuscript):

“To disambiguate the effects of multiple sequential lever pulls, we considered only lever pulls that were preceded by at least 15 seconds of no-lever pull activity (see further results below and Methods on lever lockout analysis).”

“Given the strong dependence of EDT on the # of trials, we sought to re-evaluate EDTs using only trials that were not preceded by another lever pull (either rewarded or non rewarded). We find that oscillations observed in the neural data are likely enforced by repetitive and stereotyped recent lever pulls and that EDT analysis requires exclusion of trial that occur too soon after a previous lever pull (here we chose a lockout of 15 seconds; note: this approach significantly decreased the number of trials available for analysis as mice only rarely went without pulling the lever for 15 seconds; we thus pooled trials from across sessions into a minimum of 50 to a maximum of 200 trial hybrid-sessions; see Methods). We found that after lockout the neural data had a single negative (i.e. inhibitory) phase preceding self-initiated rewarded lever pulls that comenced ~5 seconds prior (similar to Figure 1e; see Figure 2—figure supplement 2). We additionally found that EDTs decoded from lockout trials were shorter (Figure 2g: average EDT in seconds: mouse 1 (M1): -1.93; M2: -3.14; M3: -2.27; M4: -1.87; M5: -1.64; M6: -2.49). However, low pass filtering the neural time series (at 0.3hz) (as a type of feature engineering based on power-analysis results in Figure 7 below) resulted in EDTs more similar to the initial results (Figure 2h; average EDTs of causal filtered neural data in seconds: M1: -3.5; M2: -4.85; M3: -6.95; M4: -4.31; M5: -3.0; M6: 3.7). The improvement in EDT was qualitatively observable in decoding accuracy curves (Figure 2i) and was present even for non-lock out trials (see Figure 2—figure supplement 4; see also Methods).

The initial loss of EDT (without the filtering step) suggests that sequential lever pulls might have a causal role in lengthening EDTs by generating stereotyped neural time series which represents preceding – not just the current – rewarded lever pulls. However, we also found that pooling trials from sessions far apart in time (days or weeks) as required by the lockout method also also shortened EDT values (i.e. closer to 0 sec; Figure 2—figure supplement 3). This suggests that higher data variance (due to learning, [Ca] bleaching, implant degradation etc.) might also have a causal role in shortening EDTs. Overall, these findings show that self-initiated water rewarded lever pulls in mice can have neural correlates that are present up to several seconds prior to lever pull and can be decoded several seconds prior to level pulls, but that such analysis must appropriately take into account previous behaviors and the effects of data variance over longitudinal studies.”

“…While sequential stereotyped pulls have the effect of lengthening EDTs, longitudinal changes in the neural recordings have the effect of shortening EDTs.”

“… Thus, surviving trials used for analysis came from sessions that were increasingly further apart (e.g. multiple days or even a week). Pooling trials from separate days or weeks provides an additional source of noise due to changes in animal behavior, [Ca++] indicator properties, and longitudinal network changes observed in our cohorts (see Figure 4). As evidence for this, we found that subsampling the number of non-lockout trials to match the number of trials following 15sec lockout had the effect of shortening most EDTs (Figure 2—figure supplement 3).

We also recomputed locked-out EDTs for each animal (as in (g)) but following low-pass filtering the neural time series (filter set to 0.3Hz; see Methods) as described above (Figure 6i). We found that average EDTs detected were longer than using non-filtered data (Figure 6j; e.g, 15 sec lock out data means: M1: -3.50; M2: -4.85; M3: -6.95; M4: -5.01; M5: -3.0-; M6: -3.70).

Taken all these factors into account, the results suggest that while EDTs increased in some animals when using only lockout trials, the cause of the increase is due to: (i) removing stereotyped sequential lever pulls which could artificially bias the neural signal; and (ii) increased variance in the longitudinal data caused by [Ca++] state changes, systematic neural network restructuring due to longitudinal performance and other unknown factors”.

7) Another related concern seems to be the convex hull analysis in Figures 4d-g. For example, in Figure 4e it is shown that the convex hull decreases in size as the movement (t=0 sec) approaches. The authors conclude that neural activity looks more random prior to the lever pull. However, given the concerns above, it seems this "randomness" could actually be due to other lever pulls (and other spontaneous behaviors) that are not exactly temporally aligned, and not certain to occur in each sample. Thus, we would also ask the authors to repeat this analysis using longer post-hoc lockouts to prevent contamination from earlier lever pulls.

We agree with the reviewers’ concern and updated figures as follows:

  1. Figure 4b was updated to show two examples of decoding accuracy curves from 15 sec lockout (and smoothed) neural time series.

  2. Figure 4c was updated to show the trends from 15 sec lockout, filtered time series.

  3. Figure 4g was updated using 15 sec lockout data.

We updated the text to reflect these changes.

“…We carried out this analysis using only 15 sec lockout data grouped in sessions of up to 200 trials (similar to carried out above to exclude any possible trends arising from increased intra-session lever pulls or effects of sequential lever pulls; see Methods). We found that, as in the EDT longitudinal trends, 2 mice (M1, M6) that had decreasing EDTs (i.e. poorer decoding over time) also had an increased similarity (i.e. increased AUC values) between lever pull dynamics and random neural states. Considering only the statistically significant results, one explanation may be that cortical dynamics may return to natural, i.e. pre-learning, patterns and look increasingly the same as random neural states (occurring near or far from behaviors) as subcortical-cortical circuits increasingly facilitate and “take over” self-initiated behavior preparation during automaticity processes.”

(Note: Figure 4d only shows the last second of neural dynamics prior to a lever pull and was thus not affected by introducing longer lockouts; Figure 4e was generated from a session with few short inter-lever-pull intervals; and Figure 4f is a qualitative sketch demonstrating our method; we noted in the Figure legend these changes and notes).

Additionally, we made several changes to the main manuscript including the following statements:

“Longitudinal cortical network dynamics support shortening of EDTs. Considering the number of rewarded lever pulls per session, we found that between the first and last days of the experiment three of the mice (M2,M3 and M6) increased the number of rewarded lever pulls while one additional mouse (M5) also had a positive trend (with pval of 0.13); one of the mice (M1) decreased its number of pulls per day and the remaining mouse did not have statistically significant changes (Figure 4a; pearson correlation values provided in figure insets; see Methods). We labeled the 4 mice with either a strong or a trend in positive correlation over time as the “performer” group (M2,M3,M5 and M6) and the remaining mice (M1, M2) as “non-performers” as they did not increase their pulls over time. Given the potential confounds identified in Figure 2 between sequential lever pulls and decoding time, further analysis in this section focused primarily on 15 sec lockout trials only (see Figure 2). We found that SVM decoding accuracy curves over the weeks or months of behavior revealed potential trends over time (Figure 4b for examples from 2 mice). In particular, we found that EDTs shortened (i.e. were closer to the lever pull time) over time in 2 mice (M1 and M6); a similar trend was present in another mouse (M5; pval 0.06); while the remaining three mice did not show statistically significant trends. Although only statistically significant in two mice, shortening of EDT decoding time trends may be explained by automaticity findings in other studies: i.e. that following learning and repetitive behavior performance, the control of behavior is transferred from cortical structures (which we had access to during widefield [ca] imaging) to subcortical structures (that we could not access in our paradigm; another explanation could be that implants slowly degraded) (see e.g. Ashby et al. 2010; see also Discussion). ”

“Lastly, we evaluated the area under the ratio curve (AUC) longitudinally to evaluate whether there are systematic changes in the neural activity convex hull over weeks of behavior performance (Figure 4g). We carried out this analysis using only 15 sec lockout data grouped in sessions of up to 200 trials (similar to carried out above to exclude any possible trends arising from increased intra-session lever pulls or effects of sequential lever pulls; see Methods). We found that, as in the EDT longitudinal trends, 2 mice (M1, M6) that had decreasing EDTs (i.e. poorer decoding over time) also had an increased similarity (i.e. increased AUC values) between lever pull dynamics and random neural states. Considering only these two mice (as they were the only statistically significant results), one explanation may be that cortical dynamics may return to pre-lever pull learning patterns and look increasingly the same as random neural states (occurring near or far from behaviors) because subcortical circuits increasingly facilitate and “take over” self-initiated behavior preparation during automaticity processes. ”

“…While 5 of the 6 mice had increasing convex hulls, the trends were statistically significant in only 2 of the mice, which again, were mice M1 and M6 (although mouse M3 also showed a similar trend, p value: 0.14) (Figure 4i). Interestingly, mouse M4 showed a decrease in overlap of lever dynamics with all dynamics. These mixed results suggest that different mouse-specific mesoscale neural representations may be involved during learning and performing of a task.

We also found mixed results with respect to the intersection between the right paw convex hull, i.e. the paw used to pull the lever, and the lever pull convex hulls: the overlap decreased with time in 2 of the mice (M4 and M6) and showed a similar trend in another mouse (M3; pval 0.09); and it increased with time in mouse M1. These results suggest that in some mice (M4, M6 and possibly M3) lever pull neural dynamics increasingly specialize or differentiate from non-lever pull right paw movements neural dynamics (despite the right paw being used for the lever pull task). In contrast, in one mouse the similarity between right paw movements and lever pulls increased (e.g. mouse M1), but this could be explained by this mouse being a behavior outlier as the only mouse with decreased number of rewarded lever pulls over time..

In sum, EDTs shortened longitudinally in some mice suggesting neural dynamics underlying self-initiated behavior might be transferred from cortical to subcortical circuits decreasing the power of cortical-based decoding methods. The convex hull of the neural activity prior to self-initiated lever pulls also increased over time in some mice with a potential explanation that cortical dynamics return to pre-learning similarity over time. Lastly, right paw and lever dynamics appeared to become increasingly dissimilar in a few mice, with one mouse showing the opposite trend. These findings suggest that learning or mere longitudinal performance of a task restructures the neural dynamics underlying self-initiated action but that the effects could be subject specific, drawing attention to the need for intra-animal analyses (rather than cohort) in future studies.“

8) It is excellent that the authors considered the issue described in Point (1), in their control analysis in Figure 6. But the reviewers disagree with their conclusions. Figures 6g and 6h clearly show that EDT drops considerably as the lever lockout window increases. For example, the 9, 12, and 15 second lever lockout periods appear to mostly eliminate all sessions with EDTs greater than 5 seconds. This is a drastic reduction in the time horizon (10-15 seconds) argued by the authors given their earlier results.

The authors might argue that these results are mixed, given that they were not statistically significant in M1 and M2 (Figure 6g). But this is misleading. In M1, there are only 2 or so EDTs greater than 5 seconds in the 3 second lockout condition. There are none in the 6 second lockout condition. Thus, in this mouse, the lack of statistical significance is simply because there are little to no EDTs to eliminate by gradually increasing the lever lockout period. This analysis is not useful in a mouse that exhibited little to no EDTs in the 10-15 second range argued by the authors. For M2, it is surprising that no statistically significant effect was detected given that all EDTs greater than 5 seconds are eliminated in the 12 and 15 second lockout conditions.

We thank the reviewers for their comments on Figure 6. In response to this point we generated 2 additional Supplementary Figures and made two additional panels to Figure 2, as described below. Briefly, our results show that an additional preprocessing step (low pass filtering) results in average EDTs of approx -3 seconds to approx. -7 seconds even for 15 sec lockout data.

Figure 2—figure supplement 4 shows that low-pass filtering (0.3Hz) the neural time series as a type of feature engineering results in EDTs (for non-lockout data) that decrease by a few seconds in most sessions.

We also added an additional panel to Figure 2 to recompute EDTs using low-bandpass filtered neural components (i.e. the same analysis as Sup Figure 2.4 but on 15 sec locked out data).

This figure shows that lowpass filtering the neural time series of locked out trials also yields lower EDTs (we also note that filtering to 0.3Hz makes the signal closer to fMRI time dynamics studied in humans, e.g. Soon et al. 2008).

Following the reviewers comments in Point 8 and the results above, we added a new panel to Figure 6 which shows EDTs following lockout pre- and post-low bandpass filtering.

These results show that although excluding sequential lever pulls decreases decoding power (i.e. shortens EDT times), EDTs obtained from low-pass filtered data can still range between -3sec to -7sec (average EDTs for 15 sec lock out filtered data: M1: -3.50; M2: -4.85; M3: -6.95; M4: -5.01; M5: -3.0; M6: -3.70).

There is a clear trend here despite the statistical results, which calls into question the statistical technique used to assess the relationships in Figure 6g. Could the authors report their statistical technique? As far as we can tell this is not described in the paper.

We apologize for this oversight. For Figure 6 we used the same test as in Figure 2f and g (and can be found in the original manuscript), namely, a 2-sample KS test with asterisks indicating: * <0.05; **<0.01; ***<0.001; ****<0.0001; *****<0.00001. We have also added exact p-values throughout the manuscript in response to this issue.

Furthermore, is there a way the authors could analyze outlying mice behavior (M1 and M2) to understand why their results differ? M1 in particular may be very telling, because they have very few "large" EDTs to begin with as shown in Figure 6g? Can this be attributed to some behavior that M1 did (or did not do) as compared with other mice?

We agree with the reviewers that mouse M1’s behavior was an outlier. Mouse M1 seemed the least “motivated” mouse and had the least # of pulls overall, including a decrease over time in the number of pulls it did in each session and the number of lever pulls it carried out across each individual session (Figure 4a shows decrease in # of rewarded lever pulls over time). Figure 1—figure supplement 2 also suggests that neither M1 or M2 acquired the strong 3-sec peak in the inter-lever-pull interval histogram. It is possible M1 did not require as much water as other mice. It is not possible to know whether mouse M1 – unlike other mice – required less intention or preparation time to pull the lever or experience less stress towards getting the water reward. However, the 15 sec filtered lockout results from Figure 6 show that after filtering, EDT distributions of mouse M1 for 3sec lockout look more similar to other mice. The 15 sec behavior lockout yielded an average EDT of -3.5, which is comparable to other mice.

We have also added text to the main manuscript

“… EDTs computed when excluding previous lever pulls for a period of up to 15 seconds prior shortened EDTs in most animals, and average EDTs for band-passed neural data ranged from approximately -3sec to -7sec. We conclude that it is critical to exclude sequential lever pulls to the computation of EDT as well as track and control for longitudinal changes in neural dynamics caused by learning or implant related causes.”

9) Overall, it is surprising that the observed clear and large reductions in EDTs in Figures 6g and 6h are dismissed or downplayed by the authors at several points. For example, the authors state "Taking all these factors into account, the results suggest that while EDTs increased slightly in some animals, the cause of the increase could not be disambiguated between [Ca++] state changes, animal behavior, systematic neural network restructuring due to longitudinal performance…". The title of this section reads "Lever pull EDTs are similar or slightly higher with increasing lever-pull lockout duration". The Discussion again states that "Self-caused movement confounds have only minor effects on the decoding of future rewarded action." First, the increase in EDTs is substantial (for example, in Figure 6h, the mean EDT across mice seems to increase from about -4.5 to about -2.5 sec, almost a 50% change) and calls into question the primary results.

We agree with the reviewers' concerns and regret the cursory way in which we tackled this issue in the original manuscript. We have generated additional analysis and text (see Essential Revision Points 6 and 8 above).

And second, while we can appreciate that many factors can worsen EDT detection as the lockout period increases (e.g., fewer sessions that occurred further apart), it is not sufficient to explain away the clear and concerning trends by listing several limitations. Especially when it seems that there may be ways to test this. For example, the authors could downsample lever pull events in the 3 sec and 6 sec lockout groups, etc., in order to match the sample size across the 5 conditions, and then repeat their analysis. Additionally, if their concern is that sessions are further apart in the longer lockout conditions, they could resample trials in the smaller lockout conditions in such a way to match the "timing" of samples across the 5 conditions, and then repeat their analysis.

We thank the reviewers for the suggested analysis and have generated Figure 2—figure supplement 3 to address this point. This figure compares EDTs from non-lockout trials (i.e. all trials) vs. lockout trials (15 sec) vs non-lockout trials that are uniformly subsampled to match the # of trials in the 15 sec lockout condition.

Taking the reviewers’ comments and these results into consideration, we modified our main manuscript to indicate that sequential lever pulls tend to decrease EDTs and should be excluded from analysis, although this leads to a fewer number of trials that has the effect of shortening EDTs.

Reviewer #1 (Recommendations for the authors):

Recommendations for the author

1. Our main concern with the authors' study is the lack of quiescence prior to lever pulling events. The inter-lever-pull distributions in Supplementary Figure 1.2 show that many pulls will occur within the 15-20 second window prior to a given lever pulling event. This is problematic. Suppose we analyze pull n. How are we to know that neural activity prior to lever pull n relates to that pull, when lever pulls n-3, n-2, n-1, etc., occur within the analysis window?

We agree with the reviewer’s concern and have addressed these issues in depth in the Essential Revision.

It seems very possible that several key results may be altered by this confound, as outlined below:

A. The elongated and oscillatory neural activity profiles in Figure 1e, Figure 3a, Figure 5a, and Figure 7a seem to us to reflect a sequence of lever pulling movements. For example, in Figure 1e, there are oscillations in Ca++ activity, with 4-5 peaks. The authors' attribute the change in peak amplitude to an increase in stereotypy as the movement approaches. An alternate hypothesis here is that the change in amplitude is related to the probability and alignment of successive pulling events. For example, there are two large peaks because two pulling events are likely to occur together. The smaller earlier peaks reflect that a third, fourth, and fifth pulling event can occur, but with a smaller likelihood and a less temporally precise coupling to pull n.

We agree and this issue has been discussed in the Essential Revision resulting in additional panels and changes to the main text.

B. SVM decoding accuracy in Figures 1h, 2b, 2d, etc. shows in some cases that the earliest decoding time can precede a lever pull by 10-15 seconds. But again, how are we to know that SVM predictions in a given 1-second window are predicting an upcoming movement 10-15 seconds in the future, versus a separate pulling event that occurs within the SVM window on some subset of trials in a session? The increase in SVM accuracy as the movement approaches may be (as in A) due to changes in probability and temporal coupling in pulling events. The data are aligned to lever pull n, hence maximizing SVM accuracy at that time point.

We agree and have adjusted our results and panels accordingly.

It is unclear whether the authors can address such concerns with the existing data. One analysis that would be telling would be something akin to GLM. For example, in Figure 1e, are the oscillations due to multiple movements occurring in succession? The authors could fit a Gaussian to the last peak (from about -2 sec to 2 sec) in Figure 1e. Then the authors can take this Gaussian and convolve it with each lever pull event, and then add all these together into a composite trace that represents what neural activity should look like if it is only determined by lever pull events that follow some Gaussian distribution. Figure 1e could be reconstructed from this idealized timeseries as if it were the actual raw data. Should this control also show oscillations with increasing amplitude, this suggests that the neural activity in Figures 1e (and the SVM analyses by extension) is due to multiple lever pulls – not volitional activity that predicts an upcoming lever pull.

We agree and have adjusted our results and panels accordingly.

Similarly, as a control, the authors should also re-plot neural activity in Figures 1e, 5a, etc., using the longer post-hoc lockouts employed in Figure 6. That is, the authors should re-plot activity but exclude all trials where a previous lever pull occurred within x seconds, where x is 6, 9, 12, etc. as in Figure 6g.

We agree and have adjusted our results and panels accordingly.

Finally, another related concern seems to be the convex hull analysis in Figures 4d-g. For example, in Figure 4e it is shown that the convex hull decreases in size as the movement (t=0 sec) approaches. The authors conclude that neural activity looks more random prior to the lever pull. However, given the concerns above, it seems this "randomness" could actually be due to other lever pulls (and other spontaneous behaviors) that are not exactly temporally aligned, and not certain to occur in each sample. Thus, we would also ask the authors to repeat this analysis using longer post-hoc lockouts to prevent contamination from earlier lever pulls.

We agree and have adjusted our results and panels accordingly.

2. It is excellent that the authors considered the issue described in Point (1), in their control analysis in Figure 6. But we disagree with their conclusions. Figures 6g and 6h clearly show that EDT drops considerably as the lever lockout window increases. For example, the 9, 12, and 15 second lever lockout periods appear to mostly eliminate all sessions with EDTs greater than 5 seconds. This is a drastic reduction in the time horizon (10-15 seconds) argued by the authors given their earlier results.

We agree and have adjusted our results and panels accordingly. We provide explanations and figures showing evidence for our original comments (including that pooling data longitudinally affects EDTs significantly).

The authors might argue that these results are mixed, given that they were not statistically significant in M1 and M2 (Figure 6g). But this is misleading. In M1, there are only 2 or so EDTs greater than 5 seconds in the 3 second lockout condition. There are none in the 6 second lockout condition. Thus, in this mouse, the lack of statistical significance is simply because there are little to no EDTs to eliminate by gradually increasing the lever lockout period. This analysis is not useful in a mouse that exhibited little to no EDTs in the 10-15 second range argued by the authors. For M2, it is surprising that no statistically significant effect was detected given that all EDTs greater than 5 seconds are eliminated in the 12 and 15 second lockout conditions. There is a clear trend here despite the statistical results, which calls into question the statistical technique used to assess the relationships in Figure 6g. Could the authors report their statistical technique? As far as we can tell this is not described in the paper. Furthermore, is there a way the authors could analyze outlying mice behavior (M1 and M2) to understand why their results differ? M1 in particular may be very telling, because they have very few "large" EDTs to begin with as shown in Figure 6g? Can this be attributed to some behavior that M1 did (or did not do) as compared with other mice?

We agree and have adjusted our results and panels accordingly.

Overall, it is surprising to us that the observed clear and large reductions in EDTs in Figures 6g and 6h are dismissed or downplayed by the authors at several points. For example, the authors state "Taking all these factors into account, the results suggest that while EDTs increased slightly in some animals, the cause of the increase could not be disambiguated between [Ca++] state changes, animal behavior, systematic neural network restructuring due to longitudinal performance…". The title of this section reads "Lever pull EDTs are similar or slightly higher with increasing lever-pull lockout duration". The Discussion again states that "Self-caused movement confounds have only minor effects on the decoding of future rewarded action."

We agree and have adjusted our results and panels accordingly.

First, the increase in EDTs is substantial (for example, in Figure 6h, the mean EDT across mice seems to increase from about -4.5 to about -2.5 sec, almost a 50% change) and calls into question the primary results. And second, while we can appreciate that many factors can worsen EDT detection as the lockout period increases (e.g., fewer sessions that occurred further apart), it is not sufficient to explain away the clear and concerning trends by listing several limitations. Especially when it seems that there may be ways to test this. For example, the authors could downsample lever pull events in the 3 sec and 6 sec lockout groups, etc., in order to match the sample size across the 5 conditions, and then repeat their analysis. Additionally, if their concern is that sessions are further apart in the longer lockout conditions, they could resample trials in the smaller lockout conditions in such a way to match the "timing" of samples across the 5 conditions, and then repeat their analysis.

We agree with this suggestion and have carried out this analysis. Subsampling shows a significant shortening of EDTs.

3. There is another point to check here, related to these matters. If we understand correctly, the authors used a post-hoc lockout of 3 seconds in their primary analyses (e.g., Figures 2b and 2d). Other lockout periods are tested in the controls in Figure 6. We are curious what constitutes a lever pull in these post-hoc lockout periods? In other words, there is a threshold beyond which the lever needs to be pulled to be rewarded (e.g., the 15{degree sign} angle illustrated in Figure 2a). When the authors exclude lever pulls that were preceded by another lever pull in the lockout window, was it required that the preceding lever pull also exceed the 15{degree sign} threshold? Or was another threshold used? On pg. 27 it says that rewarded and non-rewarded lever pulls were counted as lever pulls in the lockout period. What is meant by unrewarded here: unrewarded because the lever pulls did not meet the minimum threshold, the minimum hold duration for the pull, not hitting the maximum value (as described in Methods)?

We apologize for the confusion. All unrewarded pulls were locked out regardless of the failure mode.

The concern we are getting at is that if the minimum 15{degree sign} threshold was used to determine lever pulls in the lockout window, the authors may be not counting smaller (and/or less purposeful) unrewarded lever pulls in the lockout window. Should these pulls also modulate cortical activity, this would pose a problem with regards to Points 1 and 2 above. That is, it seems to us that mice should be as quiescent as possible (excepting perhaps licking / grooming) during the lockout window, to be certain that EDT estimates are not inflated by smaller lever pulls (actions) that may be missed in the lockout period.

We agree and the original analysis was indeed as suggested by the authors, namely all lever pulls were excluded for the lockout periods.

4. A response to these concerns would greatly benefit from deeper behavioral analyses. The authors should consider whether pre-pull behaviors are associated with EDTs. We have several suggestions on this point:

Might the authors be able to use a classifier on their videos, to see whether pre-pull behaviors (e.g. subthreshold pulls, paw movements unassociated with the task, licking, grooming, random body motion, rewarded pulls, etc.) can predict future behavior? In other words, the authors could analyze whether classifier output is related to EDT. For example, when an EDT is -5 sec in a given session, are there animal behaviors occurring at -5 sec which might be predictive of the upcoming pull?

We have carried out analysis seeking to quantify the occurrence of different spontaneous behaviors and have found few spontaneous body movements occurring prior to lever pulls and no stereotyped structure (e.g. sequences) in movements.

The authors have a heterogeneous mouse population. Mouse M1 seems to have a smaller time horizon in their EDTs (as noted in Point 2 above) in Figure 6g. The authors should compare this mouse's behavior to other mice. For example, in the pre-pull period, does Mouse 1 engage in more/less behaviors that might explain why their EDTs differ from other mice.

We agree and have made some comments in the summary above. Mouse M1 behaved somewhat differently then other mice including decreased # of lever pulls over time and not showing peak in inter-lever-pull intervals at 3sec as most of the other mice did.

Are past behaviors predictive of future lever pulls? That is, do mice perform movement sequences of lever pulls (both rewarded/unrewarded)? Do the periodicity of movement sequences relate to EDTs? When sequences are more common in a session, does this appear related to EDT? Do actions such as licking, grooming, body motion, etc. predict upcoming lever presses? If so, does the relative timing between actions relate to EDTs on a given session?

We have carried out analysis (see above and main manuscript) and have not found evidence of movement sequences in our data.

Providing video data in example sessions across the EDT spectrum (e.g. some sessions where EDT was -10 sec, and others where it was -5 sec), could be helpful to the reader in interpreting changes in animal behavior/state that might occur near the EDT.

We believe the additional provided lever-pull autocorrelation and lever-pull vs. body movement cross correlation analysis to support our findings. Additionally, EDTs beyond 10 sec were largely removed. (we note that each EDT time was obtained from dozens to hundreds of trials and it is not practical to visualize such information in video).

Reviewer #3 (Recommendations for the authors):

• The authors need to make a stronger case regarding the novelty of their study as compared to Murakami et al., 2014, 2017 and da Silva et al., 2018.

We had reviewed these studies prior to submitting our original manuscript and concluded they do not directly relate to the main findings of our study: that slow changes in neural activity lasting multiple seconds underlie self-initiated behavior; that future behaviors could be decode between 3 sec to 7 sec prior to movement; that the self-initiated behavior neural code is distributed across multiples regions not just motor cortex; that phases of neural oscillations across several cortical regions are highly stereotyped at the time of behavior; and that the variance of neural dynamics begins to change several seconds prior to behavior initiation. We provide additional comments on these studies as follows:

i. da Silva et al. 2018 is an in-depth study of dopamine neurons (which we have not targeted in our study) and showed that dopamine cell activity increases (naturally or optogenetically induced) in dopamine subcortical centers in the approximately 1sec prior to a behavior to facilitate movement. There is no specific discussion on decoding of future behavior nor timelines for decoding of such, though dopamine centers are certainly candidates in the search for the neural correlates of volitional action.

ii. Muarakami et al. 2014 is a study of secondary motor cortex neurons in a self-started task paradigm with the first component of the study having some similarities to the paradigm we implemented. The study finds that M2 neurons ramp to a “constant threshold at rates proportional to waiting time, strongly resembling integrator output”. The study focuses on M2 single neuron activity (whereas we targeted multiple cortical areas at the mesoscale activity level) and found that M2 neurons indeed ramped slowly prior to “poke in” behaviors on the scale of ~1 sec (or less) and “poke out” behaviors on the scale of 1 to 2 sec (we note that statistically significant differences of firing rates occurred generally only +/- 0.5 sec related to a poke in; e.g. Figure 5c of that paper). Additionally, mice carried out “poke-ins” quickly following the end of previous trials with little differences in firing rates in the pre-poke-in stage vs. duration of poke (e.g. Figure 3a of that paper). There is no analysis on behavior preparation. Additionally, there is no decoding analysis of the self-initiated components of the study. The study’s integrator model (Figure 8 of that paper) is focused largely on the hold condition of the study (not the self-initiated pre-poke stage), though such a model is also consistent with our data (and we see no novelty or controversy in that model being applicable to our data). We note that the task structure is different from our study. First, there are up to two stimuli as part of the task (the first tone is required for small water reward, the second for larger water reward). Second, abandoning waiting early (after the first water reward) still resulted in a smaller water reward – whereas performing a lever pull early in our paradigm yielded no water reward. Lastly, nose poking during a session could be viewed as actively sampling the nose port with their whiskers – which is a type of continuous stimulus sampling that was less present in our task (we agree that removing all sensory stimuli was not possible in our task and paradigm).

iii. Lastly, Murakami et al. 2017 is another interesting study on the neural dynamics preceding a nose-poke initiated task. It shows that “waiting time” biases are encoded by the secondary motor cortex at the single-trial level, while the medial prefrontal cortex only represents biases in action timing. These biases occurred on the ~1 sec time scale. There is no discussion or analysis of behavior preparation or decoding of the earliest neural correlates of future action preparation.

• By definition, voluntary action should be reason-responsive. Do the authors have any data to show lever-pull actions are goal-directed?

We thank the reviewer for their suggestion and have addressed the issue of learning at length above (see for example Essential Revision Point 2).

• Panels a,b,f in Figure 1 do not seem to contain any new information and could simply be mentioned in the text.

We thank the reviewer for this comment. We consider that some readers would benefit from a brief sketch of human studies paradigms.

• Please report the exact p-values.

We have added p-values in several missing sections.

• I am not an SVM expert but is it surprising to see decoding accuracy increasing with number of trials? What is the significance of this finding? This also refers to decoding data from multiple cortical regions rather than a specific region. Isn't it expected to see better decoding accuracy with more data?

Re: the correlation between decoding accuracy increases and number of trials is discussed at more length in the Essential Revisions Points 6,8 and 9. Briefly, while increasing the number of trials for a decoding paradigm usually improved decoding due to a decrease in the overall variance, in our case the increase in trials reflected stereotyped lever pulls which appear to affect EDT decoding. We have now made several changes including using only lockout data (i.e. only trials that were not preceded by lever pulls on the order of many seconds, e.g. 12sec or 15 sec, see Figure 2g,h).

• There are multiple interesting findings throughout the paper, but the authors failed to explain their significance. For example: (A) they found a higher bias of phases: i.e., behaviours were more likely to occur during a specific phase in mice than in humans. (B) There was a high diversity of phase preference between animals. (C) EDT improved longitudinally. (D) Limb and motor cortex oscillations had most power during pre-movement neural activity [but each animal showed a different pattern]. These are interesting findings but what are their significance and how do they contribute to addressing the research question?

We thank the reviewer for this issue, and we have added more interpretation in the Discussion of our study.

• The statistics are reported in quite an unusual way. Sometimes it is just described qualitatively without any inference test. Sometimes it is reported separately for each animal and sometime across the group. It is quite hard to make any conclusion about the findings when animals show different and in some cases opposite behaviour. Maybe the authors could perform mixed-effect modelling with by-subject random slope and intercept to investigate whether any of the findings is consistent across the animals and significant at the population level.

We thank the reviewer for these citations. We acknowledge that some of our results are described qualitatively and some quantitatively. In response to this concern (and concerns from other reviewers) we report additional statistical results in more detail in our study. However, we also note part of our findings is that we find inter-animal differences across several parameters including behaviors (e.g. # of rewarded lever pulls per session, trends in # of rewarded lever pulls per session) and neural dynamics (phase of neural areas activated during lever pulls vary significantly between animals, see Figure 3). As such, pooling animals into single categories and carrying out group statistics was not always feasible.

While we agree that mixed-effect modeling could be useful for longitudinal studies such as ours where some of the data is missing, we leave such analysis for a future study.

• The authors conclude that "Overall, these results link human voluntary action studies with rodent self-initiated behavior studies and suggest mice as an adequate model for studying the neural correlates of self-initiated action". This conclusion is not justified without a control condition contrasting self-initiated with externally-triggered action.

We thank the reviewer for their comment. Our conclusion refers only to the study of self-initiated behavior studies in humans and in mice. We have not carried out analysis of stimulus-triggered actions in mice nor have references any human studies that focus on this.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below. In particular, the reviewers would like the authors to respond to the following comments, providing edits to the manuscript as needed (please see the full reviews for further elaboration).

1) It seems the most likely interpretation is that EDTs relate to complete, or at least partial, pauses in behaviors (see Reviewer #1's recommendations for details).

Below we provide a detailed analysis in response to this Reviewer's specific recommendations.

2) In Figure 3, where the authors discuss their finding that movement initiation tends to coincide with peaks in a slow neural oscillation, this interpretation seems potentially problematic, as this oscillation could more simply be a decrease in neural activity during the pause in motor behaviors, and then the subsequent increase during the active motor period (so basically, an ON-OFF-ON transition in neural activity that is correlated to distinct motor events rather than some latent oscillation). An analysis that compares the duration of "quiescent periods" (nicely highlighted in Figure 1-Supp. 5c,d) to EDT is necessary to understand how to interpret these results (see Reviewer #1's recommendations for details).

We respond in detail below and have added Figure 1, Supplementary Figure 6 and Figure 1, Supplementary Figure 7.

3) The revised results are not as strong as in the original manuscript. More rigorous analysis of the EDTs revealed that the presence of sequential lever pulls had artificially lengthened the EDT, as anticipated by the reviewers. Additionally, the authors now acknowledge that some of the results are not as reliable as previously reported (i.e., large variability across animals in neural activity phases and longitudinal changes in neural dynamics), weakening the main conclusions of the paper.

Our response is as follows:

Re: sequential lever pulls artificially lengthening EDTs, this was an issue addressed in our initial submission where we carried out analysis excluding repeating lever pulls (e.g. Figure 6). On Revision #1, we added analysis showing that filtering data in Figure 6 resulted in EDTs ranging from -7sec to -3sec prior to lever pulls sufficient to support our conclusions re: EDT decoding.

Re: the longitudinal results being “unreliable”, we disagree (and this is not a term we used). We pointed out the original manuscript and Revision #1 that part of our results were the presence of diversity in mice in learning, performance and the underlying [ca] dynamics and decoded EDTs. We chose to leave all mice in the study rather than designing exclusionary thresholds (based on learning rates, decreased engagement etc.) to document this inherent variance.

Re: weakening the main conclusions of the paper, we believe the analysis added in Revision #1 and Revision #2 have strengthened and clarified our conclusions. In particular, we add Figure 8 which contains an analysis of [ca] dynamics preceding limb movements that are completely locked out from lever pulls and not preceded by any other movements by at least 5 seconds. This figure shows that for non-water rewarded random left paw movements, the average motor cortex [ca] signal over all mice begins to increase between -2sec to -1sec prior to body movement which is the dynamics and time scale observed in the EEG literature on the RP in humans. In contrast, for water rewarded lever pulls, motor cortex [ca] activity has a negative phase prior to movement which is part of the novel findings of our study.

In sum, these results show that when removing all confounding preceding movements, self-initiated left forelimb movements in most mice contain an excitatory motor and limb cortex signal that has a time course consistent with that observed in human EEG studies of the readiness potential. In contrast, self-initiated water rewarded lever pulls contain an inhibitory signal that starts earlier and has an inhibitory dynamic. Additionally, the dynamics underlying lever pulls are significantly less noisy than spontaneous actions and enable the decoding of upcoming behaviors using SVM methods (as shown in Figures 1,2 and 6), whereas the dynamics representing limb movements were too noisy to support SVM classification (see full explanation below; see also Methods).

In light of these results, we made the following changes:

Modified Fig1e to show [ca] averages for both left paw movements and rewarded lever pulls (and removed the panels on decoding of paw movements):

Results

Figure 1e

… (e) Average over all mice of pre-movement motor [ca] dynamics of left paw movements (black traces) and water-rewarded lever pulls (blue traces) (shading is standard error; see also Methods).

The Figure 8 is also added to our Main Results as follows:

Results (Figure 8)

The widefield [ca] activity correlates of self-initiated limb movement vs goal-oriented actions.

Given the results in Figures 1 – 7, namely, that prior to rewarded lever pulls there are systematic changes in cortex, e.g. inhibition of motor activity, on the order of several seconds prior to movement we sought to relate our results to human EEG studies of volitional action. In human volitional studies the motor cortex EEG signal preceding spontaneous body movements (e.g. flicking a wrist, pressing a button) generally shows an increase in activity commencing between -2sec to -1sec prior to the body movement. To compare our results to human studies we evaluated the [ca] activity preceding isolated random left forelimb movements, i.e. movements that were not related to lever pulls nor preceded by other movements for several seconds.

Identifying left paw movements isolated from other movements. We first computed the locations of all lever pulls relative to every left paw movement in windows of 20sec with 15sec of pre-paw movement and 5sec of post-paw movement (Figure 8a). Across all video recorded sessions we identified between 13,757 and 124,397 left paw movement bouts in individual mice (i.e. times where the left paw moved; see Methods; number of movements in all mice: M1: 54016; M2: 16241; M3: 18186; M4: 20487; M5: 13757; M6: 124397). We then ranked every left paw movement bout by the longest period of non-lever pull activity, i.e. we ranked paw movement bouts by the amount of lever pulls occurring in the previous 15sec or following 5sec (Figure 8a). We next extracted the trials with a complete lever-pull lockout (i.e. no lever pulls 15sec before or 5sec after) and added the locations of left and right paw movements and licking events (Figure 8b). We then realigned the surviving bouts by the longest period of body movement quiescence, i.e. we reranked the remaining paw movement again by quiescence (Figure 8c). Finally, we looked for left paw movements that were not preceded by any movements for at least 5sec. (Figure 8d). This approach revealed between 96 to 557 left paw movements that had at least 5sec of no preceding body movements and were completely isolated from lever pulls (number of movements per mouse: M1: 416, M2: 96, M3: 133, M4: 167, M5: 177, M6: 557).

Neural activity increases one to two seconds prior to left-limb movement. We next computed the average neural dynamic for the right hemisphere motor, upper-limb and retrosplenial cortex activity and found that both motor and upper-limb cortex exhibited a slow rising time course commencing at approximately -2sec prior to paw movement, with the upper-limb cortex signal being larger than motor cortex (Figure 8e for example from mouse M1). We computed the upper-limb cortex average signal for all animals and found that in 4 mice (M1, M3, M4, M5) this signal showed a significant increase prior to movement (i.e. rising above 3 x the standard deviation of the preceding neural activity) commencing approximately -2sec to -1sec relative to paw movement time (Figure 8f). Mouse M2 had an average [ca] signal which was noisy, likely due to the lower number of trials. Mouse M6 also had a noisy signal that showed an increase above 3 x std prior to movement, but not as pronounced as the other 4 mice. We note that mouse M6 had more than 5 months of recording and the pooled trials likely reflected significantly more variance due to pooling bouts from longitudinal data (see Figure 2, figure supplement 3 and Methods for a detailed discussion on the challenges of pooling widefield [ca] data across long periods of time). We also sought to decode upcoming behavior-locked out paw movements but found that SVM-based methods did no better than chance (not shown). This was likely due to higher dynamics occurring during non-goal oriented paw movements, but we also could not rule out the effect of pooling only a few hundred trials from tens of thousands of trials across many months of recording (see also Discussion).

Computing the average neural signals in motor, upper-limb and retrosplenial cortex from the means in all mice we found that both motor and upper-limb cortex showed a significant increase in the average neural signal beginning at approximately 2sec prior to paw movement (Figure 8g). In contrast, the average neural signal in these areas prior to water rewarded (locked-out) lever pulls showed a decrease commencing as early as -5sec prior to lever pull (Figure 8h).

In sum, we show that prior to non-lever pull related and isolated left paw movements, the [ca] neural activity in mouse motor cortex begins to increase at approximately -2sec to -1sec prior to paw movement – consistent with human EEG studies on the readiness potential dynamics occurring prior to spontaneous finger or hand movement. In contrast, self-initiated water rewarded lever pulls contain an inhibitory signal that starts earlier, at approximately -5 to -4 seconds prior to lever pull and contains stereotyped neural patterns that can be decoded to predict upcoming level pull timing (see Figures 1,2 and 6).

We removed Figure 2k, l, m panels which described decoding of left, right and licking behaviors but without controlling for confounding lever pulls. We now clarified those results:

Figure 2 Results:

… Importantly, with a few exceptions, licking or paw movements EDT distributions were not statistically different from lever pull time ETDs, however, due to the high correlation between paw movements and licking to lever pull times – we do not view them as completely independent analyses. Importantly, as we show below (see Figure 8) when considering only body movements that are isolated from lever pulls (i.e. not preceded by a lever pull in the previous 15sec, or following 5sec) and also not preceded by other body movements for at least 5sec, we find that [ca] averages show an increase in motor cortex (and other areas) and that EDTs are near 0sec as the signal is too noisy to enable decoding (see also Methods and Discussion).

4) The reviewers would like the authors to comment on the claim that they have identified a specific neural signature of self-initiated voluntary action. The authors show that pre-movement neural activity in mice contains structures that are not present in random neural activity. This observation is well supported by the data. However, to claim that this structure – neural dynamics becoming increasingly stereotyped prior to movement – is specific to self-initiated actions one needs to show that pre-self-initiated movement (uncued) neural activity in mice contains structures that are not present in externally-triggered movement (cued) neural activity. This comparison could also rule out other possible explanations such as motor confounds associated with the lever pulls or other related micro-movements. Comparison with random time during the task is not sufficient to make any conclusion regarding the self-initiated nature of the behavior.

The Reviewers suggest that our findings that “that pre-movement neural activity in mice contains structures that are not present in random neural activity” is not sufficiently established without evaluation whether similar dynamics are “present in externally-triggered movement (cued) neural activity.”

We respond as follows:

Our paradigm used self-initiated behavior invitations as trigger points for all analysis. And our findings are that there are stereotyped changes in [ca] dynamics prior to such behavior initiations sufficient to predict the timing of such initiations. However, we acknowledge this concern and have added the following to our discussion.

Discussion

Comparison of our study with cued action studies. In our experience, mice following external cues such as a light or sound triggers initiate their behaviors immediately and decoding the timing of future behavior on the scale of seconds is not possible. However, it is possible that in cue-triggered experiments – expert mice exhibit motor cortex inhibitory signals while they are waiting for a cue (e.g. see Schurger et al. 2012). However, such neural states would be conditioned on the eventual arrival of an external cue towards a future action and would not constitute self-initiated volitional action. Another possible paradigm would be to train mice to withhold action following a cue for several seconds prior to movement. In such a paradigm the neural correlates of action inhibition would be the focus rather than preparation of future action. Lastly, optogenetic approaches to activate or inhibit motor and forelimb cortex of mice after they have acquired the task could also be implemented, especially if mice learn a self-initiated rather than cued task (the latter being more often the case). Overall, we view these and other similar paradigms as useful complementary studies to our work and suggest them for future studies.

Reviewer #1 (Recommendations for the authors):

I commend the authors on the care they have taken to address reviewer concerns. The added attention to 15-second lockout data is very useful. I think the focus on the 15-second lockout periods largely addresses the issue that sequences (or even random periods of movement) bias EDT estimates. Figure b (rebuttal Page 3) aligns with the updated EDTs in Figure g on Page 4. That is, neural activity changes around 3-4 seconds prior to the lever pull event in Figure b (Page 3), and EDTs seem largely capped at 3-4 seconds in Figure g (Page 4). In addition, there is evidence that mice are sensitive to the 3-second experimental lockout, given the drop in movement probability as shown in Figure c (Page 6). Thus, there does appear to be a 'cognitive decision' associated with obtaining the reward.

But the clear analyses presented by the authors, highlight a new issue that has potential implications for the way they interpret the data. An interpretation that seems consistent with the analyses detailed on Pages 2-6, is that changes in neural activity related to EDTs are less so about pulling the lever, and more so about suppressing actions. For example, in Figure 2-Supplement 2, we see a gradual decline in activity in many brain regions. It appears that the onset of this decline aligns with EDT estimates via SVM. This decline appears to align with the movement data shown in Figure 1-Supp. 5c and 5d, which shows that overall movements start to decline about 3-5 seconds prior to the lever press event. And again, EDTs in the 15-second lockout datasets, appear to saturate around 3-5 seconds. And neural variability decreases in that window (Figure 5). Thus, it seems to be that this decline in spontaneous behaviors is related to the decline in neural activity, and the EDT estimates obtained by SVM. On the other hand, neural activity exhibits a sharp spike 500 ms prior to lever press (Figure 2-Supplement 2) which peaks at the same time as a sudden burst of all sorts of movements (not just lever presses) as seen in Figure 1-Supplement 5.

It seems the most likely interpretation is that EDTs relate to complete, or at least partial, pauses in behaviors. The mouse has learned to suppress movement, and these movement suppressions are needed to acquire the reward. These movement suppressions begin at about 3-5 seconds because this is indeed the experimentally imposed lockout period. In other words, mouse behavior seems to represent a sequence of actions: pause for 3 seconds, then move.

At the end of the day, this does still relate to volition. Without a cue, the mouse chooses to start a sequence, pause then move. But the idea that mice have learned a sequence, pause then move, does not seem to align with many critical points the authors make: e.g., "structured multi-second neural dynamics preceding self-initiated action" as claimed for example in the abstract, or the paper's title. Rather, if the mice are performing a sequence, pause then move, then the initial changes in neural activity (which are detected in EDT) are not about lever press, but about the pause that starts the sequence. In other words, it seems if the mice have a state change from movement to quiescence (and then lever press), then the EDTs should be detected not relative to the lever press but the movement quiescence that begins the "sequence".

The Reviewer suggests that “the most likely interpretation is that EDTs relate to complete, or at least partial, pauses in behaviors”. The Reviewer suggests that the results in Figure 3 showing differential firing on specific phases of widefield [ca] activity might be interpreted as neural activity phases, such as OFF-ON, occurring potentially due to a “pause in motor behaviors” during periods prior to lever pulls rather than reflecting the preparation of a future behavior. The Reviewer suggests comparing “quiescent period” duration to EDT might help to interpret the results.

In response to this comment we respond as follows:

We responded in part above in the summary, and Figure 8 also provides context to show that isolated left-paw movements do not exhibit motor cortex inhibition. That is, lever pulls towards a water reward might be a separate class of behavior with its own neural correlates. And we agree that inhibition of cortical activity may play a role in the preparation of such actions. But, as explained in the Discussion above, cortex inhibition as a component of preparation of future action may have confounds which lead to decreased activity. We view the issue as interesting and largely unexplored in humans and suggest that it be further addressed in future studies.

With respect to evaluating the role of quiescent periods to neural activity and EDTs, the Reviewer has suggested (i) splitting the 15sec lockout trials (or perhaps all trials) by the amount of movement occurring and (ii) then determining the relationship of pre-movement quiescence (e.g. duration) with EDT (or putatively [ca] dynamics). In response, we generated Figure 1, figure supplement 6. In sum, the figure shows that the vast majority of water rewarded lever pulls (i.e. 59% to 90% across all mice) contain several movements amounting to at least 1 second of motion in the period of -5sec to 0sec.

In sum, Figure 1—figure supplement 6 shows that:

1. As previously shown in Figure 1, figure supplement 5, mice tended to decrease their body movements in the period of -5sec to 0sec prior to a lever pull. However, the number of body movements prior to a lever pull lies on a continuum and there are few completely quiescent pre-lever pull periods.

2. In the vast majority of trials (59% to 90% across all mice) mice move for at least 1 sec (4 x 0.250sec time bins) in the period of -5sec to 0sec prior to lever pulls.

Taken together, these findings suggest that most of the time mice do not pause their motor behaviors in the period of -5sec to 0sec prior to a lever pull. However, given the above concerns regarding interpretation, we added additional content to the Discussion as shown above.

Another related issue might be the interpretation in Figure 3, where the authors discuss their finding that movement initiation tends to coincide with peaks in a slow neural oscillation. This interpretation seems potentially problematic, as this oscillation could more simply be a decrease in neural activity during the pause in motor behaviors, and then the subsequent increase during the active motor period (so basically, an ON-OFF-ON transition in neural activity that is correlated to distinct motor events rather than some latent oscillation).

Thus, while overall the mice do clearly show that they are able to volitionally control themselves for at least 3-5 seconds to obtain the reward (i.e., stop moving), this should not be taken to mean that the neural activity during this period is necessarily important to the upcoming lever press. Stated very succinctly, it is problematic to assign EDT-related neural activity to the lever press, when there is a large coincident change in the behavioral state of the animal that precedes the lever press (the complete or partial suppression of behavior). It seems more analysis on this quiescence is very important to the paper's interpretation of their results.

Data that are needed on this point would be an analysis that compares the duration of "quiescent periods" (nicely highlighted in Figure 1-Supp. 5c,d) to EDT. For example, I anticipate that occasions where the animal pauses body movements for a longer period prior to the lever press will relate to longer EDTs. On this note, it would be very helpful for the authors to provide an analogue of Figure 1-Supplement 5 (Panels c and d) for the 15-second lockout data specifically, and for all their mice as opposed to solely mouse M1. Do 15-second lockout data also show the 3-5 second quiescent periods prior to lever pulls? Another suggestion on this point: the error bars in Figure 1-Supp. 5d are large: both in the "pause" prior to time point 0, and the peak in activity at time point 0. This implies some heterogeneity in the extent to which animals pause, and the action they take after a pause. The authors could take advantage of this. If the authors binned trials based on (a) complete pause, (b) partial pause, and (c) no pause, do these groups show different neural activity patterns in the -5 to 0-sec range in Figure 2-Supp. 2 (a similar binning analysis could be done based on the length of the pause.)? If so, again, these analyses would be a reason to suspect that much of the neural signal change detected in the EDT has to do with a pause in activity prior to lever press, as opposed to the lever press itself. Trial-to-trial analyses could also be conducted on this issue, relating the pause duration (or perhaps pause magnitude, e.g., the extent to which behaviors stop prior to lever press) relates to neural activity on that trial (to this end, GLMs may be useful to parse overall activity into component behaviors).

The Reviewer suggests, as in the previous segment, that splitting the data based on a range of behavior amounts from least movement to partial movements to most movements may reveal the systematic relationship between pauses and EDT or [ca] activity.

We respond as follows:

We followed the Reviewers suggestions and carried out an “analysis that compares the duration of "quiescent periods"” and binned “trials based on (a) complete pause, (b) partial pause, and (c) no pause”. We provide Figure 1, Supplementary Figure 7.

The above analysis shows that:

In some mice, the average neural activity traces from the least active trials (red traces) appear to commence slightly earlier than the most active trials (black trials), for example mouse M1, M3 and M4 (panel f). However, the averages are noisy and the standard-error-of-the-mean shading overlaps between these two groups in all cases. In contrast, traces from mouse M2, M5 and M6 seem to show that either mid-activity trials (blue) or high-activity trials (black) begin to decrease or peak lower than the least active trials (red). The average of the averages of all mice (panel (g)) shows that the most active trials are largely within 3 standard deviations of the man while the mid-active and least-active averages show similar curves.

Taken together, these results suggest that during the most active trials motor cortex receives the most inhibition whereas in other circumstances the dynamics are likely to be similar.

We have additionally added the Method for this figure to our Manuscript and referenced it in Figure 1.

Methods

"Data processing for Figure 1, figure supplement 7. Pooling data across weeks or months of longitudinal recordings adds substantial noise and other confounds to our datasets (see Figure 2, figure supplement 3, and Main manuscript for Figure 6 for a detailed explanation). For the analysis here we pooled lever lockout trials (as in Figure 6) but were further limited to only those sessions where video was available. This reduced our overall trials by approximately 50% (or more) in all mice compared to Figure 6 analysis. The trials were further split into 3 groups representing least to most body movements. Additionally, trials pooled into these subgroups were ordered by movement amount – as opposed to ordered by day of acquisition time. This also has the effect of increasing the variance of data means due to time between individual trials in the subgroup. Given these issues, our analysis in this panel was limited to characterizing [ca] time courses only (i.e. it was not possible to also carry out EDT decoding on trials contained in these reduced datasets – as the time series were too noisy).”

Reviewer #2 (Recommendations for the authors):

This is the second review of the manuscript "Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement".

Overall, the authors have addressed in detail the concerns raised by the previous round of reviews.

On the one hand:

1) The authors have appropriately toned down claims about "voluntary" movements.

2) The authors have also performed new analyses to address the concerns about sequences of stereotyped movements but did not address the concerns of micro-movements due to a lack of proper video data. This limitation was acknowledged in the manuscript and I have no additional concerns in this respect.

On the other hand:

3) The revised results are not as strong as in the original manuscript. More rigorous analysis of the EDTs revealed that the presence of sequential lever pulls had artificially lengthened the EDT, as anticipated by the reviewers. Additionally, the authors now acknowledge that some of the results are not as reliable as previously reported (i.e., large variability across animals in neural activity phases and longitudinal changes in neural dynamics), weakening the main conclusions of the paper.

We thank the reviewer for the comment, we have addressed it above in the Main Concerns discussed above.

Reviewer #3 (Recommendations for the authors):

The authors have done a great job addressing the comments. The new analyses and figures have significantly improved the readability and quality of the manuscript.

I am, however, still not completely convinced by the authors' claim that they have identified a specific neural signature of self-initiated voluntary action. The authors show that pre-movement neural activity in mice contains structures that are not present in random neural activity. This observation is well supported by the data. However, to claim that this structure – neural dynamics becoming increasingly stereotyped prior to movement – is specific to self-initiated actions one needs to show that pre-self-initiated movement (uncued) neural activity in mice contains structures that are not present in externally-triggered movement (cued) neural activity. This comparison could also rule out other possible explanations such as motor confounds associated with the lever pulls or other related micro-movements. Comparison with random time during the task is not sufficient to make any conclusion regarding the self-initiated nature of the behavior.

We have responded to this comment above and added a section to our Discussion directly to address it.

https://doi.org/10.7554/eLife.76506.sa2

Article and author information

Author details

  1. Catalin Mitelut

    1. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    2. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
    3. Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, Canada
    4. Biozentrum, Centre for Molecular Life Sciences, University of Basel, Basel, Switzerland
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing
    For correspondence
    mitelutco@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0471-9816
  2. Yongxu Zhang

    Department of Engineering, University of Florida, Gainesville, United States
    Contribution
    Software, Formal analysis
    Competing interests
    No competing interests declared
  3. Yuki Sekino

    1. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    2. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2038-274X
  4. Jamie D Boyd

    1. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    2. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Data curation, Software, Methodology
    Competing interests
    No competing interests declared
  5. Federico Bollanos

    1. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    2. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Data curation, Investigation
    Competing interests
    No competing interests declared
  6. Nicholas V Swindale

    Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, Canada
    Contribution
    Funding acquisition, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7106-5114
  7. Greg Silasi

    Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada
    Contribution
    Conceptualization, Data curation, Supervision, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Shreya Saxena

    Department of Engineering, University of Florida, Gainesville, United States
    Contribution
    Software, Formal analysis, Supervision, Writing – review and editing
    Contributed equally with
    Timothy H Murphy
    Competing interests
    No competing interests declared
  9. Timothy H Murphy

    1. Department of Psychiatry, Kinsmen Laboratory of Neurological Research, University of British Columbia, Vancouver, Canada
    2. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing
    Contributed equally with
    Shreya Saxena
    For correspondence
    thmurphy@mail.ubc.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0093-4490

Funding

Canadian Institutes of Health Research (MOP-15360)

  • Catalin Mitelut
  • Yongxu Zhang
  • Yuki Sekino
  • Jamie D Boyd
  • Federico Bollanos
  • Nicholas V Swindale
  • Greg Silasi
  • Timothy H Murphy

Canadian Institutes of Health Research (MOP-12675)

  • Catalin Mitelut

Canadian Institutes of Health Research (FDN-143209)

  • Timothy H Murphy

National Science and Engineering Research Council of Canada (178702)

  • Catalin Mitelut
  • Yongxu Zhang
  • Yuki Sekino
  • Jamie D Boyd
  • Federico Bollanos
  • Nicholas V Swindale
  • Greg Silasi
  • Timothy H Murphy

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was supported by Canadian Institutes of Health Research (CIHR) Operating Grant MOP-15360 and National Science and Engineering Research Council of Canada 178702; Canadian Institutes of Health Research (CIHR) Operating Grant MOP-12675 and Foundation Grant FDN-143209 to THM. We thank Pumin Wang, Cindy Jiang for surgical assistance; Jeff LeDue for technical assistance. We also thank Kenny Kay, Xuexin Wei, and Allen Chan for comments on the initial manuscript.

Ethics

Mouse protocols were approved by the University of British Columbia Animal Care Committee and followed the Canadian Council on Animal Care and Use guidelines (protocols A13-0336 and A14-0266).

Senior Editor

  1. Christian Büchel, University Medical Center Hamburg-Eppendorf, Germany

Reviewing Editor

  1. Gordon J Berman, Emory University, United States

Publication history

  1. Received: December 18, 2021
  2. Preprint posted: December 20, 2021 (view preprint)
  3. Accepted: November 2, 2022
  4. Accepted Manuscript published: November 3, 2022 (version 1)
  5. Version of Record published: November 17, 2022 (version 2)

Copyright

© 2022, Mitelut et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Catalin Mitelut
  2. Yongxu Zhang
  3. Yuki Sekino
  4. Jamie D Boyd
  5. Federico Bollanos
  6. Nicholas V Swindale
  7. Greg Silasi
  8. Shreya Saxena
  9. Timothy H Murphy
(2022)
Mesoscale cortex-wide neural dynamics predict self-initiated actions in mice several seconds prior to movement
eLife 11:e76506.
https://doi.org/10.7554/eLife.76506

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