Motor planning brings human primary somatosensory cortex into action-specific preparatory states

  1. Giacomo Ariani  Is a corresponding author
  2. J Andrew Pruszynski
  3. Jörn Diedrichsen
  1. The Brain and Mind Institute, Western University, Canada
  2. Department of Computer Science, Western University, Canada
  3. Department of Physiology and Pharmacology, Western University, Canada
  4. Department of Psychology, Western University, Canada
  5. Robarts Research Institute, Western University, Canada
  6. Department of Statistical and Actuarial Sciences, Western University, Canada

Abstract

Motor planning plays a critical role in producing fast and accurate movement. Yet, the neural processes that occur in human primary motor and somatosensory cortex during planning, and how they relate to those during movement execution, remain poorly understood. Here, we used 7T functional magnetic resonance imaging and a delayed movement paradigm to study single finger movement planning and execution. The inclusion of no-go trials and variable delays allowed us to separate what are typically overlapping planning and execution brain responses. Although our univariate results show widespread deactivation during finger planning, multivariate pattern analysis revealed finger-specific activity patterns in contralateral primary somatosensory cortex (S1), which predicted the planned finger action. Surprisingly, these activity patterns were as informative as those found in contralateral primary motor cortex (M1). Control analyses ruled out the possibility that the detected information was an artifact of subthreshold movements during the preparatory delay. Furthermore, we observed that finger-specific activity patterns during planning were highly correlated to those during execution. These findings reveal that motor planning activates the specific S1 and M1 circuits that are engaged during the execution of a finger press, while activity in both regions is overall suppressed. We propose that preparatory states in S1 may improve movement control through changes in sensory processing or via direct influence of spinal motor neurons.

Editor's evaluation

In this elegant and rigorous study, the authors investigated the neural correlates of planning and execution of single finger presses in a 7T fMRI study focusing on primary somatosensory (S1) and motor (M1) cortices. BOLD patterns of activation/deactivation and finger-specific pattern discriminability indicate that M1 and S1 are involved not only during execution, but also during planning of single finger presses. These important results clearly establish that the role of primary somatosensory cortex goes beyond pure processing of tactile information and will be of great interest for researchers in the field of motor control and of systems neuroscience.

https://doi.org/10.7554/eLife.69517.sa0

Introduction

Animals are capable of generating a wide variety of dexterous behaviors accurately and effortlessly on a daily basis. This remarkable ability relies on the motor system reaching the appropriate preparatory state before each movement is initiated.

At the level of behavior, the process of motor programming, or planning, has long been shown to be beneficial to performance (Keele, 1968; Keele et al., 1976; Rosenbaum, 1980), leading to faster reaction times (Klapp and Erwin, 1976; Klapp, 1995; Haith et al., 2016) and more accurate response selection (Ghez et al., 1997; Wong and Haith, 2017; Ariani and Diedrichsen, 2019; Hardwick et al., 2019). The behavioral study of motor planning led to neurophysiological investigations showing the presence of preparatory signals in the patterns of neuronal firing in the dorsal premotor cortex (PMd, Cisek and Kalaska, 2004; Cisek and Kalaska, 2010; Hoshi and Tanji, 2006), the supplementary motor area (Hoshi and Tanji, 2004), and the posterior parietal cortex (Cui and Andersen, 2007; Cui and Andersen, 2011; Andersen and Cui, 2009). Building on this work, human neuroimaging studies have shown that activity in parietofrontal brain regions during planning of prehension movements can be used to decode several movement properties such as grip type (Gallivan et al., 2011b; Ariani et al., 2015), action order (Gallivan et al., 2016), and effector (Gallivan et al., 2011a, Gallivan et al., 2013; Leoné et al., 2014; Turella et al., 2016).

At the level of neural population dynamics (Vyas et al., 2020), motor planning can be understood as bringing the neuronal state toward an ideal preparatory point. Once this state is reached and the execution is triggered, the intrinsic dynamics of the system then let the movement unfold (Churchland et al., 2010; Shenoy et al., 2013). Preparatory neural processes have not only been observed in premotor and parietal areas, but also in primary motor cortex (M1, Tanji and Evarts, 1976; Crammond and Kalaska, 2000; Ariani et al., 2018). In contrast, the degree to which primary somatosensory cortex (S1) receives information about the planned movement before movement onset is less clear.

S1 is often considered to be mostly concerned with processing incoming sensory information from tactile and proprioceptive receptors arising after movement onset. Consistent with this notion, previous functional magnetic resonance imaging (fMRI studies have not detected the presence of planning-related information in this area Gallivan et al., 2011a; Gallivan et al., 2011b, Gallivan et al., 2016; Gallivan et al., 2013; although see Gale et al., 2021). However, challenging this notion, in the past years research has shown that S1 can be somatotopically activated even in the absence of tactile inputs, for instance during touch observation (Kuehn et al., 2014), attempted movements without afferent tactile inputs (Kikkert et al., 2021), and through attentional shifts (Puckett et al., 2017). Moreover, a recent human electrocorticography (ECoG) study suggested a possible role for S1 in cognitive-motor imagery (Jafari et al., 2020). The authors recorded neural activity from S1 while a tetraplegic participant imagined reaching movements and found that S1 neurons encoded movement direction during motor imagery in the absence of actual sensations. Another recent ECoG study in nonhuman primates (Umeda et al., 2019) showed grasp-specific information in the signals from S1 well before movement initiation, and only slightly later than in M1.

However, it remains unknown whether S1 plays a role during motor planning in human participants with an intact sensory system. Furthermore, we currently do not know how the signals during action preparation relate to those during execution, a fact that could provide important insight into the role these signals may play.

Here, we designed a high-field (7T) fMRI experiment to study what brain regions underlie the planning of individual finger presses and how these brain representations relate to those during execution. We used variable delays between an instructing cue and a go signal, as well randomly interspersed no-go trials, to temporally separate the evoked responses to movement planning and execution. Using advanced multivariate pattern analyses we were able to examine the relationship between the fMRI patterns related to planned and executed finger actions.

Results

Deactivation in sensorimotor regions during planning of finger actions

We instructed 22 participants to plan and execute repeated keypresses with individual fingers of their right hand on a keyboard device while being scanned with 7T fMRI. The key to be pressed corresponded to one of three fingers and was cued during the preparation phase by numbers (1 = thumb, 3 = middle, 5 = little, e.g., Figure 1A) presented on a computer screen that was visible to the participants lying in the scanner through an angled mirror. After a variable delay (4–8 s), participants received a color cue indicating whether to press the planned finger (go trials), or whether to withhold the response (no-go trials). Upon the go cue, participants had to initiate the correct response as fast as possible and make six presses of the designated finger, before receiving accuracy points for reward (see Materials and methods).

fMRI task and blood-oxygen-level-dependent (BOLD) responses.

(A) Example trial with timing information. Background colors indicate different experimental phases (yellow = preparation; green = move [go] or stay [no-go]; purple = reward; gray = intertrial interval, ITI). (B) Group-averaged BOLD response (N = 22) for go (blue) and no-go (orange) trials in a region that shows no planning-evoked activity (left M1, top), and one that shows some planning-evoked activity (left anterior superior parietal lobule [aSPL], bottom). Shaded areas indicate standard error of the mean (SEM). Background colors correspond to trial phases as in A.

To control for involuntary overt movements during the preparation phase, we required participants to maintain a steady force on all the keys during the delay, which was closely monitored online. To ensure that planning results would not be biased by the subsequent execution, we restricted all our analyses of the preparation phase to no-go trials only (see Materials and methods). First, we asked which brain regions showed an evoked response during the planning of finger presses (e.g., Figure 1B).

We focused our analysis on the lateral aspect of the contralateral (left) hemisphere (purple and white areas of Figure 2 inset), which included the primary motor and somatosensory cortex, as well as the premotor cortex and anterior parietal cortical regions. To examine brain activation during finger planning, we performed a univariate contrast of the preparation phase (across the three fingers) vs the resting baseline (Figure 2A). Overall, the instruction stimulus evoked little to no activation in our regions of interest (ROIs, see Materials and methods). In fact, compared to resting baseline, we observed significant deactivation (Figure 2E) in the primary motor cortex (M1, t21 = −6.939, p = 7.446e−07) and in the primary somatosensory cortex (S1, t21 = −5.508, p = 1.823e−05). Significant deactivation was also observed in the PMd (t21 = −2.929, p = 0.008). While anterior regions in the superior parietal lobule (aSPL) showed some signs of activation (Figures 1B and 2A), these did not reach statistical significance when tested at the ROI level (t21 = 1.881, p = 0.074).

Figure 2 with 2 supplements see all
Activation and distance analyses of movement planning and execution.

The inset shows the inflated cortical surface of the contralateral (left) hemisphere, highlighting the area of interest (A-D, purple) and the strip used for the profile region of interest (ROI) analysis (E, F, white). Major sulci are indicated by white dotted lines.( A) Univariate activation map (percent signal change) for the contrast planning > baseline (no-go trials only). (B) Multivariate searchlight map of the mean crossnobis distance between the planning of the three fingers (no-go trials only). (C) Same as A, but for the univariate contrast execution > baseline (go trials). (D) Same as B, but for the mean crossnobis distance between fingers during execution. Colorbars in A and C reflect mean percent signal change, whereas colorbars in B and D reflect mean crossnobis distance (arbitrary units). (E) Profile ROI analysis (see Materials and methods) of the mean percent signal change (± standard error of the mean [SEM]) during planning (no-go trials, orange) and execution (blue). The x-axis corresponds to Brodmann areas (BAs) selected from the white strip shown in the inset at the top. Horizontal bars indicate significance (p < 0.05) in a two-sided one-sample t-test vs zero for selected ROIs. (F) Same as E, but for the mean crossnobis distance (± SEM). Vertical dotted lines mark the approximate boundaries between BAs subdivisions of our main ROIs (see Materials and methods). Black triangles point to the approximate location of the main anatomical landmarks: Pre-CS = precentral sulcus; CS = central sulcus; Post-CS = postcentral sulcus. PMd (BA 6) = dorsal premotor cortex; M1 (BA 4a, 4b) = primary motor cortex; S1 (BA 3a, 3b, 1, 2) = primary somatosensory cortex; aSPL (BA 5) = anterior superior parietal lobule. For analogous results using the estimates of planning activity from all trials, see Figure 2—figure supplement 1. For the whole-brain maps of univariate and multivariate results, see Figure 2—figure supplement 2.

A wider whole-brain search (Figure 2—figure supplement 2) did not provide evidence for planning-related activation in other secondary motor areas. This lack of planning-related activation in high-order areas in planning is likely explained by the low task difficulty (i.e., little planning demands). Participants were only asked to plan repeated movements of a single finger, resulting in little amounts of overall planning activation. In contrast, execution strongly activated both primary and high-order sensorimotor regions (Figure 2C), with activation being significant in all tested ROIs (Figure 2E, all t21 > 14.824, all p < 1.351e−12).

Planning induces informative patterns in primary somatosensory and motor cortex

Although we found little univariate planning-related activation, preparatory processes need not increase the overall activation in a region. Rather, the region could converge to a specific preparatory neural state (Churchland et al., 2010), while activity increments and decrements within the region (i.e., at a finer spatial scale) average each other out. In this case, information about planned movements would be present in the multivoxel activity patterns in that region.

To test this idea, we calculated the cross-validated Mahalanobis dissimilarity, or crossnobis distance (see Materials and methods), between activity patterns. First, the activation patterns (beta weights) for the planning phase of no-go trials where prewhitened using the voxel-by-voxel covariance matrix. The distance was then calculated by comparing activity patterns across partitions (imaging runs), such that the value of the dissimilarity is on average zero if the two conditions only differ by measurement noise. Thus, systematically positive values of this dissimilarity measure indicate that the patterns reliably differentiate between the different planned actions (Walther et al., 2016; Arbuckle et al., 2020). Indeed, a surface-based searchlight approach (Oosterhof et al., 2012) revealed reliably positive crossnobis distance between the activity patterns related to planning of individual finger presses (Figure 2B), which the ROI analysis confirmed to be significantly greater than zero in both M1 (t21 = 2.343, p = 0.029) and S1 (t21 = 3.137, p = 0.005, Figure 2F).

The distribution on the flat surface map of these distance values during planning (Figure 2B) appeared to be highly similar to the distribution of distances during execution (Figure 2D). To quantify this topographic similarity, we computed the ratio between distances in different Brodmann area (BA) subdivisions of our ROIs (see Materials and methods), reasoning that a mismatch in location would result in large differences in ratio values. However, the ratio between planning and execution distances was roughly stable across the different subregions of sensorimotor cortex (BA 4a: 0.23, BA 4b: 0.16, BA 3a: 0.24, BA 3b: 0.19, BA 1: 0.22, BA 2: 0.31). In other words, the average distance between finger-specific activity patterns during planning was between 16% and 31% of the average pattern distance during execution. Thus, we not only show the existence of planning-related activity in S1, but also that S1 activity patterns are at least as informative as M1 activity patterns.

Visual inspection suggested that the informative patterns during planning may be concentrated more dorsally in M1 and S1 relative to execution. To test for the possibility that the location on the flat surface map of the peaks of the crossnobis distance for M1 and S1 was statistically different across subjects between planning and execution, we used a Hotelling T2 test that allowed us to compare the difference between two multivariate means of different distributions (i.e., the distributions of xy coordinates for the peaks of planning and execution). This test revealed no systematic difference in the location of the peak vertex between planning and execution across subjects (M1: T22,20 = 0.725, p = 0.712; S1: T22,20 = 2.424, p = 0.335).

Together, our analyses indicate that information about single finger actions is already represented during motor planning in the same parts of the primary motor and somatosensory cortices that are engaged during execution of the presses. Given that we only used the activity estimates from no-go trials (~40% of total trials), this information cannot be explained by a spillover from subsequent execution-related activity. An analysis using the estimates of planning activity from all trials yielded very similar results (see Figure 2—figure supplement 1), demonstrating that we could separate planning from execution-related signals.

Activity patterns are not caused by small movements during the preparation phase

The presence of planning-related information in primary sensorimotor regions was surprising, especially in S1, where it had not previously been reported in comparable fMRI studies (Gallivan et al., 2016; Gallivan et al., 2011b). To ensure that these results were not caused by overt movement, participants were instructed to maintain a steady force on the keyboard during the preparation phase, such that we could monitor even the smallest involuntary preparatory movements.

Inspection of the average force profiles (Figure 3A) revealed that participants were successful in maintaining a stable force between 0.2 and 0.4 N during preparation. However, averaging forces across trials may obscure small, idiosyncratic patterns visible during individual trials (Figure 3B) that could be used to distinguish the different movements. To test for the presence of such patterns, we submitted both the mean and standard deviation of the force traces on each finger to a multivariate dissimilarity analysis (see Materials and methods). Indeed, this sensitive analysis revealed that some participants showed small movement patterns predictive of the upcoming finger (positive behavioral distances in Figure 3C).

Small involuntary movements do not explain preparatory activity patterns in M1 and S1 (A).

Mean finger force (± standard error of the mean [SEM]) plotted in 10 ms bins, time aligned to instruction onset (dotted vertical line) and end of the preparation phase (dashed vertical lines), separately for the three fingers and go (blue) and no-go (orange) trials. (B) Example of an individual trial with a 6-s preparation phase, followed six presses of the little finger (green). Horizontal solid line denotes press threshold (1 N). Dash-dotted lines denote the boundaries of the finger preactivation red area in Figure 1A (see Materials and methods). Reaction time (RT) was defined as the time from the go cue (dashed vertical line) to the onset of the first press (left solid vertical line). Movement time (MT) was defined as the time from the onset of the first press (left solid vertical line) until the release of the last press (right solid vertical line). (C) Pearson’s correlation (r) between behavioral and neural distances in M1 and S1 (see Materials and methods) during the preparation phase (planning, orange). Each dot represents an individual participant (N = 22). Solid line shows linear regression fit; p values pairs refers to the slope and the intercept of the fitted line. (D) Same as C, but during the movement phase (execution, blue).

These distances, however, were ~200–300 times smaller than the average distances during execution (x-axis in Figure 3D), and we found no significant correlation between the magnitude of the behavioral differences for the preparation phase and the amount of planning information present in our sensory–motor ROIs (both p values for the slope of the linear fit >0.3 in Figure 3C). More importantly, a significantly positive intercept in the linear fit in Figure 3C (M1: p = 0.032; S1: p = 0.007) shows that, even after correcting for the influence behavioral patterns, the activity patterns in M1 and S1 remained informative (i.e., significantly positive neural distance even with no significant behavioral distance). Thus, the finding of finger-specific activity patterns in M1 and S1 cannot be explained by small involuntary movements during the preparation phase.

Single finger activity patterns from planning to execution are positively correlated

How do planning-related activity patterns in M1 and S1 relate to the activity patterns observed during execution? Neurophysiological experiments have suggested that patterns of movement preparation are orthogonal – or uncorrelated – to the patterns underlying active movement (Kaufman et al., 2014). This arrangement allows movement preparation to occur without causing overt movement.

When we compared the planning- and execution-related activity patterns as measured with fMRI, a technique that samples neuronal activity at a much coarser spatial resolution, we found the opposite result. Planning- and execution-related patterns for the same finger were tightly related. This can be seen already in the representational dissimilarity matrices (RDMs) that show the dissimilarity (crossnobis distance) for each pair of conditions (i.e., fingers 1 = thumb, 3 = middle, 5 = little for planning and execution phases).

First, the RDMs for M1 and S1 (Figure 4A) show a large difference between planning and execution patterns, which is due to the substantially higher average activation during movement compared to planning. This overall distance between planning and execution can also be appreciated in a three-dimensional (3D) projection of the RDMs using multidimensional scaling (MDS) to highlight the representational geometry between activity patterns (principal component PC1 in Figure 4B, top).

Correlated representations of single fingers across planning and execution.

(A) Representational dissimilarity matrices (RDMs) showing the average crossnobis distance between the activity patterns for digits 1 (thumb), 3 (middle), and 5 (little) during the preparation (no-go planning, orange) and movement (execution, blue) phases, for M1 (left) and S1(right), in the left hemisphere. (B) Two different views of a multidimensional scaling (MDS) plot that represents the distance between activity patterns in A as spatial distance in a three-dimensional (3D) coordinate system. Top, view highlighting the first principal component (PC1, difference in average activation between planning and execution). Bottom, rotated view highlighting the correspondence between representational geometries across planning and execution visible on PC2 and PC3. The black cross denotes the mean pattern across conditions. (C) Pattern component modeling (PCM) evaluation of models (x-axis) of different correlations between planning- and execution-related activity patterns. Shown in dark gray is the group average of the individual log-likelihood (± standard error of the mean [SEM] across participants) curves expressed as a difference from the mean log-likelihood across models (i.e., zero on the y-axis). Red dots indicate the best fitting correlation model for each participant (N = 22). Red dashed lines denote the average winning (i.e., best fitting) models across participants. Gray-shaded areas indicate models that perform statistically worse (p < 0.05) than the best fitting correlation model (determined in a cross-validated fashion, see Materials and methods). Pink-shaded areas indicate models that do not perform significantly worse than the best fitting correlation model (p ≥ 0.05).

Second, within each phase, the pattern for the thumb was more distinct than those for the other fingers, replicating previous results from execution alone (Ejaz et al., 2015; Yokoi et al., 2018). Importantly, however, when ignoring the overall difference between the mean patterns for planning and execution, by looking at a rotated view of the representational geometry (Figure 4B, bottom), it became clear that the finger patterns were arranged in a congruent way, with planning- and execution-related activity patterns for the same finger being closer to one another. This representation suggested that the finger-specific patterns during planning may be a scaled-down version of the patterns during execution.

To test this idea more precisely, we quantified the correspondence (i.e., correlation) between planning and execution patterns for each finger using pattern component modeling (PCM, Diedrichsen et al., 2018). Because of the biasing influence of measurement noise, simple correlations between measured fMRI patterns are substantially lower than their true correlation (see http://www.diedrichsenlab.org/BrainDataScience/noisy_correlation). PCM corrects for this bias by evaluating the likelihood of the data (taking into account the measurement noise), under a range of models with a true correlation between 0 and 1. In other words, rather than asking which correlation value is the best estimate given the data, PCM asks how likely the data is given different correlation values (see Materials and methods for details).

The log-likelihood of the data under each model (evaluated individually for each participant and then averaged) is shown in Figure 4C. Across participants, the averaged maximum likelihood estimate of the correlation (i.e., the average best fitting correlation model) was r = 0.83 (±0.053 standard error of the mean [SEM]) for M1 and r = 0.81 (±0.061 SEM) for S1 (Figure 4C, red dashed lines). By comparing these estimates to the zero-correlation model, we can conclude that the correlation of finger-specific patterns across planning and execution was significantly larger than zero in both M1 and S1 (both t21 > 13.288, p < 1.086e−10 in a two-tailed one-sample t-test against zero). However, the maximum likelihood estimates of the correlation cannot be used to evaluate whether the overlap of these patterns was only partial (r < 1) or complete (r = 1), as the estimates are still biased due to measurement noise (Walther et al., 2016; Diedrichsen et al., 2018).

Therefore, in a cross-validated fashion, we compared for each participant the log-likelihood of the best fitting model (determined on all other participants, see Materials and methods) to the log-likelihood under the model that the patterns are perfectly correlated (r = 1). Across participants, this difference was not significant for either M1 (t21 = 0.953, p = 0.176) or S1 (t21 = 0.148, p = 0.442). Given that no correlation model had significantly higher log-likelihoods than the 1-correlation model, we cannot rule out that the underlying true correlation was indeed 1. In other words, we have as much evidence that the correspondence was only partial as we do that the correspondence was perfect. By comparing the best fitting correlation model to every other correlation model, we have evidence that the true (i.e., noiseless) correlation between planning and execution finger-specific activity pattern was between 0.41 and 1.0 in M1 and between 0.54 and 1.0 in S1 (Figure 4C, pink-shaded areas).

Thus, our data are consistent with the idea that, at the resolution of fMRI, the activity patterns for planning and execution of finger presses in S1 and M1 are either partially overlapping or even a scaled version of each other.

Discussion

In the present study, we asked participants to produce repeated single finger presses while undergoing 7T fMRI. We used variable preparatory delays and no-go trials to cleanly dissociate the brain responses to the consecutive preparation and movement phases. We found that information about planned finger actions is present in both S1 and M1 before action onset, even though the overall level of activation in these regions was below resting baseline. Moreover, while execution elicited much higher brain activation, the fine-grained, finger-specific activity patterns were highly similar across planning and execution. Control analyses confirmed that the observed results were not caused by premovement finger activity.

Our finding that motor planning activates M1 in a finger-specific fashion was not necessarily surprising given many neurophysiological studies reporting anticipatory activity of M1 neurons related to movement intentions (Tanji and Evarts, 1976; Riehle and Requin, 1989; Alexander and Crutcher, 1990), as well as human neuroimaging showing shared information between delayed and immediate movement plans (Ariani et al., 2018). In contrast, the robust activity patterns related to single finger planning in S1 were more surprising, given that this region has classically been associated with the passive processing of somatosensory information from receptors in the skin, muscles, and tendons.

So, what could then be the role of S1 during movement planning? First, it is worth noting that there are substantial projections from S1 (BA 3a) that terminate in the ventral horn of the corticospinal tract (Coulter and Jones, 1977; Rathelot and Strick, 2006). Although stimulation of area 3a in macaques typically fails to evoke overt movements (Widener and Cheney, 1997), it has been suggested that this population of corticomotoneurons specifically projects to gamma motoneurons that control the sensitivity of muscle spindle afferents (Rathelot and Strick, 2006). Thus, it is possible that S1 plays an active role in movement generation by preparing the spindle apparatus in advance of the movement.

Second, the finger-specific preparatory state in S1 may reflect the prediction of the upcoming sensory stimulation, allowing for a movement-specific sensory gain control (Azim and Seki, 2019). It is likely that, this process is also accompanied by an allocation of attention to the cued finger. However, as voluntary (Gallivan et al., 2011a; Gallivan et al., 2011b) planning requires attention, our current dataset cannot distinguish between the two possibilities. Sensory stimuli could become attenuated to maintain movement stability and filter out irrelevant or self-generated signals. Indeed, multiple studies have shown that both somatosensation and somatosensory-evoked potentials in S1 decrease during voluntary movement (Starr and Cohen, 1985; Chapman et al., 1987; Jiang et al., 1990; Seki and Fetz, 2012). Alternatively, sensory processing of the expected salient signals could be enhanced to improve movement execution.

While several previous fMRI studies did not find action-specific encoding in S1 during planning (Gallivan et al., 2011a; Gallivan et al., 2011b, Gallivan et al., 2016; Gallivan et al., 2013), concurrently with our study a second paper found movement-specific modulation of S1 preparatory activity (Gale et al., 2021). Together, these two papers provide convergent evidence that motor planning triggers notable changes in the neural state of the somatosensory system and that such changes can be detected with fMRI in humans.

The second important finding in our paper was the close correspondence between finger-specific activity patterns across planning and execution – which appears to be at odds with the idea that these two processes occupy orthogonal neural subspaces to avoid overt movement during planning (Kaufman et al., 2014; Elsayed et al., 2016). We think that there are at least two possible explanations for this. First, the divergence of results could be caused by the difference in behavioral paradigms. While the neuronal correlates of movement planning in nonhuman primates have largely been studied using upper limb movements, we used here individuated finger presses. If for single finger actions even single-neuron activity patterns are highly correlated between planning and execution, then overt movement during planning would need to be actively suppressed, for example through the deactivation that we observed around the central sulcus.

An alternative and perhaps more likely explanation of the discrepancy lies in the different measurement modalities. Orthogonality was observed in electrophysiological recordings of individual neurons, whereas the fMRI measurements we employed here mainly reflect excitatory postsynaptic potentials (Logothetis et al., 2001) and average metabolic activity across hundreds of thousands of cortical neurons. Thus, it is possible that planning preactivates the specific cortical columns in M1 and S1 that are also most active during movement of that finger. Within each of these cortical microcircuits, however, planning-related activity could still be orthogonal to the activity observed during execution at the single-neuron level (e.g., see Arbuckle et al., 2020, for a similar observation for cortical representations of flexion and extension finger movements). This would suggest a new hypothesis for the architecture of the sensory–motor system where movement planning preactivates the action-specific circuits in M1 and S1. However, it does so in a fashion that the induced planning-related activity is, in terms of the firing output of neurons, orthogonal to the patterns during execution.

Materials and methods

Participants

Twenty-three right-handed neurologically healthy participants volunteered to take part in the experiment (13 F, 10 M; age 20–31 years, mean 23.43 years, SD 4.08 years). Criteria for inclusion were right-handedness and no prior history of psychiatric or neurological disorders. Handedness was assessed with the Edinburgh Handedness Inventory (mean 82.83, SD 9.75). All experimental procedures were approved by the Research Ethics Committee at Western University (HSREB 107061). Participants provided written informed consent to procedures and data usage and received monetary compensation for their participation. One participant withdrew before study completion and was excluded from data analysis (final N = 22).

Apparatus

Repeated right-hand finger presses were performed on a custom-made MRI-compatible keyboard device (Figure 1A). Participants only used the tips of their fingers to press on the keys. The keys of the device did not move but force transducers underneath each key measured isometric force production at an update rate of 2 ms (Honeywell FS series; dynamic range 0–25 N; sampling 200 Hz). A keypress/release was detected when the force crossed a threshold of 1 N. The forces measured from the keyboard were low pass filtered to reduce noise induced by the MRI environment, amplified, and sent to PC for online task control and data recording.

Task

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We used a task in which participants produced repeated keypresses with the tip of their right-hand fingers in response to numerical cues appearing on a computer screen (white outline, Figure 1A). On each trial, a string of six numbers (instructing cue) instructed which finger press to plan (1 = thumb, 3 = middle, 5 = little).

The length of the preparation phase (yellow background in Figure 1) was randomly sampled to be 4 s (56% of trials), 6 s (30%), or 8 s (14%). To limit and monitor unwanted movements during the preparation phase, we required participants to preactivate their fingers by maintaining a steady force of around 0.2–0.3 N on all of the keys during the preparation phase. As a visual aid, we displayed a red area (between 0 and 0.5 N) and asked participants to remain in the middle of that range with all the fingers (touching either boundary of the red area would count as unwanted movement, thus incurring an error). We preferred this technique over using electromyography (EMG) recordings to monitor micromovements during the preparation phase because extensive pilot experiments for our studies of ipsilateral representations (Diedrichsen et al., 2013) and mirroring (Ejaz et al., 2018) showed that force fluctuations from preactivated hand muscles provide a more sensitive readout of involuntary muscle activations compared to EMG signals acquired during fMRI.

At the onset of the movement phase (green background), participants received a color cue (go/no-go cue) indicating whether to perform the planned finger presses (blue outline = go, p = 0.6), or not (orange outline = no-go, p = 0.4). The role of no-go trials was to decouple the hemodynamic response to the successive planning and execution events, which would otherwise always overlap in fast fMRI designs due to the sluggishness of the fMRI response (Ariani et al., 2018).

To encourage planning during the delay period, at the go cue the digits were masked with asterisks, and participants had to perform the presses from memory. Participants had 2.5 s to complete the movement phase, and a vanishing white bar under the asterisks indicated how much time was left to complete all of the keypresses. Participants received online feedback on the correctness of each press with asterisks turning either green, for a correct press, or red, for incorrect presses. As long as the participants remained within task constraints (i.e., six keypresses in less than 2.5 s), an exact movement speed was not enforced. In no-go trials, participants were instructed to remain as still as possible maintaining the finger preactivation until the end of the movement phase (i.e., releasing any of the keys would incur an error).

During the reward phase (0.5 s, purple background) points were awarded based on performance and according to the following scheme: −1 point in case of no-go error or go cue anticipation (timing errors); 0 points for pressing any wrong key (press error); 1 point in case of a correct no-go trial; and 2 points in case of a correct go trial.

Intertrial intervals (ITI, gray background) were randomly drawn from {1, 2, 4, 8, 16 s} with the respective proportion of trials {0.52, 0.26, 0.13, 0.6, 0.3}.

Experiment design and structure

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Our chosen distribution of preparation times, ITIs, and no-go trials, was determined by minimizing the variance inflation factor (VIF) for a given length of scan:

VIF=varEvarX

where var(E) is the mean estimation variance of all the regression weights (planning- and execution-related regressors for each finger), and var(X) the mean estimation variance had these regressors been estimated in isolation. The VIF quantifies the severity of multicollinearity between model regressors by providing an index of how much the variance of an estimated regression coefficient is increased because of collinearity. Large values for VIF mean that model regressors are not independent of each other, whereas a VIF of 1 means no inflation of variance. After optimizing the design, the VIF was quite low, on average around 1.15, indicating that we could separate planning- and execution-related activity without a large loss of experimental power.

Participants underwent one fMRI session consisting of 10 functional runs and 1 anatomical scan. In an event-related design, we randomly interleaved three types of repeated single finger presses involving the tip of the thumb (1), the middle (3), and the little (5) fingers (e.g., 111,111 for thumb presses, Figure 1A) and three types of multifinger sequences (e.g., 135,315).

The day before the fMRI scan, participants familiarized themselves with the experimental apparatus and the go/no-go paradigm in a short behavioral session of practice outside the scanner (five blocks, about 15–30 min in total). This short training made the requirement of maintaining a steady force on all keys during the preparation phase very easy. In fact, the system was calibrated so that the natural weight of the hand on the keys was enough to bring the finger forces within the correct range to be maintained. Thus, it is likely that little online control was required by the participants before pressing the keys.

For the behavioral practice, ITIs were kept to a fixed 1 s to speed up the task, and participants were presented with different sequences from what they would see while in the scanner. These six-item sequences were randomly selected from a pool of all possible permutations of the numbers 1, 3, and 5, with the exclusion of sequences that contained consecutive repetitions of the same number. Given that the current paper is concerned with the relationship between representations of simple planning and execution, here we will focus only on the results for single finger actions. The results for multifinger sequences are intended for publication in a future paper.

Each single finger trial type (e.g., 111,111) was repeated five times (two no-go and three go trials), totaling 30 trials per functional run. Two periods of 10 s rests were added at the beginning and at the end of each functional run to allow for signal relaxation and provide a better estimate of baseline activation. Each of the 10 functional runs took about 5.5 min and the entire scanning session (including the anatomical scan and setup time) lasted for about 75 min.

Imaging data acquisition

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High-field fMRI data were acquired on a 7T Siemens Magnetom scanner with a 32-channel head coil at Western University (London, Ontario, Canada). The anatomical T1-weighted scan of each participant was acquired halfway through the scanning session (after the first five functional runs) using a Magnetization-Prepared Rapid Gradient Echo sequence (MPRAGE) with voxel size of 0.75 × 0.75 × 0.75 mm isotropic (field of view = 208 × 157 × 110 mm [A–P, R–L, F–H], encoding direction coronal). To measure the blood-oxygen-level-dependent responses in human participants, each functional scan (330 volumes) used the following sequence parameters: GRAPPA 3, multiband acceleration factor 2, repetition time (TR) = 1.0 s, echo time (TE) = 20 ms, flip angle (FA) = 30°, slice number: 44, voxel size: 2 × 2 × 2 mm isotropic. To estimate and correct for magnetic field inhomogeneities, we also acquired a gradient echo field map with the following parameters: transversal orientation, field of view: 210 × 210 × 160 mm, 64 slices, 2.5 mm thickness, TR = 475 ms, TE = 4.08 ms, FA = 35°.

Preprocessing and univariate analysis

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Preprocessing of the functional data was performed using SPM12 (fil.ion.ucl.ac.uk/spm) and custom MATLAB code. This included correction for geometric distortions using the gradient echo field map (Hutton et al., 2002), and motion realignment to the first scan in the first run (three translations: x, y, z; three rotations: pitch, roll yaw). Due to the short TR, no slice timing corrections were applied. The functional data were coregistered to the anatomical scan, but no normalization to a standard template or smoothing was applied. To allow magnetization to reach equilibrium, the first four volumes of each functional run were discarded. The preprocessed images were analyzed with a general linear model (GLM). We defined separate regressors for each combination of the six finger actions (single, multi) × two phases (preparation, movement). To control for the effect of potential overlap between execution activity and the preceding planning activity, we also estimated a separate GLM with separate regressors for the preparation phases of go and no-go trials, resulting in a total of 18 regressors (12 go + 6 no-go), plus the intercept, for each run. Each regressor consisted of a boxcar function (on for 2 s of each phase duration and off otherwise) convolved with a two-gamma canonical hemodynamic response function with a peak onset at 5 s and a poststimulus undershoot minimum at 10 s (Figure 1B).

Given the relatively low error rates (i.e., number of error trials over total number of trials, timing errors: 7.58 ± 0. 62%; press errors: 1.18 ± 0.26%, see Task), all trials were included to estimate the regression coefficients, regardless of whether the execution was correct or erroneous. Ultimately, the first-level analysis resulted in activation images (beta maps) for each of the 18 conditions per run, for each of the participants.

Surface reconstruction and ROI definition

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Individual subject’s cortical surfaces were reconstructed using Freesurfer (Dale et al., 1999). First, we extracted the white-gray matter and pial surfaces from each participant’s anatomical image. Next, we inflated each surface into a sphere and aligned it using sulcal depth and curvature information to the Freesurfer average atlas (Fischl et al., 1999). Both hemispheres in each participant were then resampled into Workbench’s 164 k vertex grid. This allowed us to compare similar areas of the cortical surface in each participant by selecting the corresponding vertices on the group atlas.

Anatomical ROIs were defined using a probabilistic cytoarchitectonic atlas (Fischl et al., 2008) projected onto the common group surface. Our main ROIs were defined bilaterally as follows: primary motor cortex (M1) was defined by including nodes with the highest probability of belonging to BAa 4a and 4b, within 2 cm above and below the hand knob anatomical landmark (Yousry et al., 1997); primary somatosensory cortex (S1) was defined by the nodes related to BA 1, 2, 3a, and 3b; PMd was defined at the junction between the superior frontal sulcus and the precentral sulcus (BA 6); finally, the anterior part of the superior parietal lobule (aSPL, BA 5) included areas anterior, superior, and ventral to the intraparietal sulcus. ROI definition was carried out separately in each subject using FSL’s subcortical segmentation. When resampling functional onto the surface, to avoid contamination between M1 and S1 activities, we excluded voxels with more than 25% of their volume in the gray matter on the opposite side of the central sulcus.

Multivariate distance analysis

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To detect single finger representations across the cortical surface, we used representational similarity analysis (RSA; Diedrichsen and Kriegeskorte, 2017; Walther et al., 2016) with a surface-based searchlight approach (Oosterhof et al., 2011). For each node, we selected a region (the searchlight) corresponding to 100 voxels (12 mm disc radius) in the gray matter and computed cross-validated Mahalanobis (crossnobis, Walther et al., 2016) dissimilarities between pairs of evoked activity patterns (beta estimates from first-level GLM) of single finger sequences, during both preparation and movement phases.

Prior to calculating the dissimilarities, beta weights for each condition were spatially prewhitened that is weighted by the matrix square root of the noise covariance matrix estimated from the residuals of the GLM. The noise covariance matrix was slightly regularized toward a diagonal matrix (Ledoit and Wolf, 2004). Multivariate prewhitening has been shown to increase the reliability of dissimilarity estimates (Walther et al., 2016). The dissimilarity was then computed by multiplying the difference between two conditions patterns with the pattern difference for the same conditions of any other run, and then averaging over all runs and voxels. This resulted in 15 dissimilarities between the 6 conditions (3 single fingers, separately for planning and execution), which can be visualized as a 6 × 6 RDM (Figure 4A). An alternative visualization can be obtained using classical MDS, which shows the six conditions as points projected into the 3D space spanned by the eigenvectors of the patterns that were associated with the three largest eigenvalues (i.e., the three principal components).

For the searchlight analysis, we assigned the average distance between any of the three planning conditions to the central node of the searchlight. The region was then moved across all nodes across the surface sheet obtaining a cortical map (Figure 2B). An equivalent analysis was conducted for the execution patterns (Figure 2D). Cross-validation ensures the distances estimates are unbiased, such that if two patterns differ only by measurement noise, the mean of the estimated value would be zero. This also means that estimates can sometimes become negative. Therefore, dissimilarities significantly larger than zero indicate that two patterns are reliably distinct, similar to an above-chance performance in a cross-validated pattern-classification analysis.

The searchlight analysis was mainly used for visualization purposes. Additionally, we conducted the multivariate analysis separately for each anatomically defined ROI (e.g., Figure 4A). For the profile ROI analysis (both univariate and multivariate, e.g., Figure 2E, F), we defined 50 rectangular surface-based searchlights in each hemisphere that covered the virtual strip shown in the top inset of Figure 2 and that were aligned to the boundaries between different ROI subdivisions. Based on these surface-based searchlights, we defined the voxel-based subdivisions in individual brains. For statistical comparisons, these subdivisions were successively grouped by averaging within-ROI subdivisions (see Figure 2E, F). This approach allowed us to compute both ROI-level statistical comparisons and the analysis of the ratio of distances in the different subdivisions of our main ROIs (e.g., M1 into BA4a and BA4b). Statistical comparisons consisted of two-sided one-sample t-test vs zero for selected ROIs.

Correlation between behavioral and neural distances

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To ensure that our planning results were not contaminated by unwanted micromovements during the preparation phase, we calculated the behavioral distance between the different fingers on the basis of keyboard force data and correlated behavioral and neural distances.

For behavioral distances, we first extracted force data (2-ms temporal resolution, smoothed with a Gaussian kernel of 9.42 full width at half maximum, FWHM) and binned it in 10 ms steps (downsampling largely due to memory constraints) for both the preparation and movement phases (Figure 3A). Next, for each subject, we calculated the mean (5) and the standard deviation (5) of the time-averaged force of each finger for each condition (3 sequences × 2 phases = 6) and block (10). These subject-specific finger force patterns (60 × 10) were multivariately prewhitened using their covariance matrix. Finally, we calculated the cross-validated squared Euclidean distances for each condition (6 × 6 RDM) and averaged distances between the three finger presses for each phase (preparation, movement).

These mean finger force distances for each subject were correlated with the mean voxel activity distances from the two phases for two ROIs (M1 and S1, Figure 3C,D). To statistically assess that the neural distances were still significantly larger than zero even in the absence of behavioral distances, we computed the pvalue for the intercept of the linear fit.

PCM correlation models

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Visual inspection of the RDM and MDS plots (Figure 4A, B) suggested that the finger-specific activity patterns during planning and execution might be arranged in a congruent fashion. This correspondence can be assessed by determining the correlation between the planning and execution activity patterns for matching fingers (i.e., planning finger 1 with executing finger 1), after accounting for the average activity pattern for planning and execution across fingers.

The problem with simple Pearson’s correlations or cross-validated correlations is that these measures are biased by noise. Even if the patterns for planning and execution were perfectly correlated (i.e., a scaled version of each other), the empirical correlation estimates would not be one (see http://www.diedrichsenlab.org/BrainDataScience/noisy_correlation). Therefore, we used (PCM Diedrichsen et al., 2018, openly available at github.com/DiedrichsenLab/PcmPy; copy archived at swh:1:rev:076b9a685ed116b1f0b83a68a0955d0cc5323a42, Ariani, 2022) to generate different models, each assuming a specific correlation between planning and execution patterns on the interval between 0 and 1 in steps of 0.01. We then computed the log-likelihood of the observed data (Y, the 6 activation patterns observed in 10 runs) from each participant under each correlation model (r): pYr , which is plotted in Figure 4C. PCM assumes that both the true activity patterns and the measurement noise are randomly distributed with a multivariate Gaussian distribution. The likelihood of the data under each model can then be analytically evaluated (for details see Diedrichsen et al., 2011; Diedrichsen et al., 2018; Diedrichsen et al., 2021). This likelihood depends only on the covariance matrix of the measured activity patterns and the predicted covariance matrix from the model. (Note: more precisely it relies on the measured and the predicted second moment matrix, as we do not subtract out the mean of each pattern across voxels.) Like RSA, PCM therefore abstracts away from the actual activity patterns, as it only depends on the relationship between the patterns, but does not have to model the pattern themselves. In fact, there is a 1:1 relationship between the second moment matrix used in PCM and the RDM used in RSA (Diedrichsen and Kriegeskorte, 2017). In both cases, the correlation between two conditions (e.g., planning and execution, for each finger) can be seen as the diagonal of the off-diagonal block of this matrix. The approach is also equivalent to an encoding model estimated with Ridge regression (Diedrichsen and Kriegeskorte, 2017), with the advantage that it can be estimated in closed form without fallback on cross-validation.

Here, we used 100 PCM correlation models with correlations in the range [0–1] in equal step sizes. The number of correlation models was chosen arbitrarily—ultimately, it only determines the amount of correlation values tested (i.e., the resolution on the x-axis in Figure 4C). By exploring the entire log-likelihood function across different correlations models, this approach allows us to test specific hypothesis even if the signal-to-noise level is low.

Apart from a fixed correlation, each model contained five free parameters, each describing the variance of specific pattern component. The first two parameters captured the variance of the common pattern for all execution patterns and the variance of the common pattern for all planning patterns. Together, these two components captured the overall difference between planning and execution. The next two parameters captured the variance associated with the three fingers under the two conditions. Finally, the noise parameter determined the variance of the measurement noise. Because all correlation models had the same number of parameters, we simply maximized the likelihood for each correlation model in respect to these parameters.

The curve in Figure 4C shows the average log-likelihood for each correlation model (100 models from 0 to 1 in equal steps sizes), relative to the mean log-likelihood across models (zero on the y-axis). Differences between the log-likelihoods can be interpreted as log-Bayes factors. Group inferences were performed using a simple t-tests on the log-likelihoods.

To compare specific models to the best fitting model, we had to correct for the bias arising from picking the best model and testing it on the same data. Therefore, we used N − 1 subjects to determine the group winning model, and then chose the log-likelihood of this model for the left-out subject (for whom this model may not be the best one) as the likelihood for the ‘best’ model. This was repeated across all subjects and a one-sided paired-sample t-test was performed on the recorded log-likelihood and every other model.

This test revealed which of the correlation models were significantly worse (i.e., associated with a lower log-likelihood) than the winning model that was independently estimated via cross-validation (gray-shaded area in Figure 4C).

In sum, PCM has the advantage over alternative approaches in that it provides stable inferences even for noisy data, offering an optimal evaluation (in the likelihood sense) of the real evidence present in the data about the true correlation between two activity patterns. For technical implementation details of PCM, including a full example of PCM correlation models, see the documents of the openly available toolbox written in Python (pcm-toolbox-python.readthedocs.io/en/latest/demos/demo_correlation.html).

Data availability

The data used to create the figures in this study can be found on github https://github.com/g14r/single-finger-planning (copy archived at swh:1:rev:076b9a685ed116b1f0b83a68a0955d0cc5323a42).

The following data sets were generated

References

  1. Book
    1. Keele SW
    2. Summers JJ
    3. Keele SW
    (1976)
    The Structure of Motor Programs
    In: Keele SW, editors. Motor Control. Elsevier. pp. 109–142.

Decision letter

  1. Chris I Baker
    Senior and Reviewing Editor; National Institute of Mental Health, National Institutes of Health, United States
  2. Andrea Serino
    Reviewer
  3. Sanne Kikkert
    Reviewer; ETH Zürich, Switzerland

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 "Motor planning brings human primary somatosensory cortex into action-specific preparatory states" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Chris Baker as the Reviewing Editor/Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Andrea Serino (Reviewer #1); Sanne Kikkert (Reviewer #3).

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) Expand and clarify descriptions of the methods and analyses.

2) Conduct additional analyses to help substantiate the claim that planning and execution patterns are scaled version of each other.

3) Conduct additional analyses of SMA and pre-SMA to show the expected planning-related pattern of activity in these areas, especially given the null results in premotor and parietal areas.

4) Given another recent publication with related results (Gale et al., 2021), consider expanding the results to include ipsilateral regions, whole volume analyses (see Reviewer #2).

5) Temper interpretations of null results.

Reviewer #1 (Recommendations for the authors):

• To help the reader, the description of the PCM analyses should be improved. In particular:

1. It is not clear how the correlation models are conceived.

2. It is not clear under which form the data are treated (pattern, RDM or second-moment matrix) and how the planning/execution are compared under a given correlation model.

3. The overall procedure of PCM should be better described in the methods.

4. Figure 4D is rather hard to understand and the legends should be improved.

• Providing a comparison between M1 and S1 (at the RDM level) could be interesting (e.g., is the representational structure more similar between M1 and S1 for planning or for execution?).

Reviewer #2 (Recommendations for the authors):

Even though the study is of high quality, I have a concern regarding the fact that recent research showed similar evidence for the role of S1 during motor planning (Gale et al., 2021). These findings partially limit the novelty and the impact of the present investigation. Following the evidence emerging from this recent research, I think the authors might consider conducting additional analyses to strengthen their research and better characterise the presented results.

Given the high spatial resolution of the study, I think that it might be helpful to focus on specific sub-fields of the somatosensory cortex to dissect their specific roles. In addition, based on the recent description of the possible role of ipsilateral cortices in representing movements, it would be also interesting to investigate the role of ipsilateral S1 (and M1) and to compare the pattern of finger-specific movements within these regions with the regions of the contralateral hemisphere. Finally, it would be interesting to provide whole brain data both for the univariate and the multivariate analyses in order to provide a global overview of the involved brain regions and of the brain areas hosting finger-specific patterns.

1) Lack of activation during the planning phase

Most previous fMRI studies on motor planning using complex movements instructed participants to stay still during the planning phase. It is not surprising to see deactivation or activation at baseline during this phase.

In this study, the lack of activation in S1 and M1 during planning is surprising for me, given that the participants were engaged in a visuomotor task throughout the whole duration of the preparation/planning phase (Figure 1A, lines 303-308). Participants had to pre-activate their fingers by maintaining a steady force on all the keys during the preparation phase. Moreover, a visual feedback showing the applied force was displayed and participants had to apply a force to maintain the level of this feedback within a specific range.

This type of task is rather demanding, so I would expect to see the engagement of parieto-frontal motor networks during motor planning. Could it be possible that the lack of activation in S1 and M1 during planning is simply due to how the data were modelled in the GLM. The authors reported that they modelled only the first 2 seconds of the preparation phase (lines 394-397) as this phase has different durations (4-8 seconds). As an additional control analysis, it might be interesting to directly compare the preparation phase with the initial and final baseline by explicitly modelling the baseline in the GLM.

As a general suggestion, I would recommend the authors to provide whole brain analysis as supplementary materials (see point 4).

2) Specificity of the described effects in the somatosensory cortex

I would suggest investigating the role of the different subsections of S1. One possibility could be to adopt the parcellation of the Anatomy toolbox, as implemented in a recent investigation (Gale et al., 2021), or by analysing separately the different cytoarchitectonic maps adopted to create the S1 ROI (see lines 416-417). This study (Gale et al., 2021) suggest that S1 subsectors might represent different information during action planning. Support to the possible role of S1 in motor control comes from a recent neurophysiological study showing the possible encoding of complex arm movements, and not only of somatosensory information, within area BA 2 of macaque monkeys (Chowdhury et al., 2020). The higher spatial resolution of the present study might provide support to the data presented in the and/or new insights of the specificity of the described effect in the somatosensory cortex.

3) Role of ipsilateral hemisphere

I would also suggest conducting the same type of analyses on S1 and M1 of the ipsilateral hemisphere as a meaningful control site. Moreover, this analysis could provide novel insights on the possible role of ipsilateral S1 in representing ipsilateral movements. Indeed, there is a growing body of evidence showing the representation of ipsilateral movements in the motor cortex (Bundy and Leuthardt, 2019), but much less is known on the role of S1. If a representation of the planned movements is present in one or more subsectors of S1 (see Gale et al. 2021), the comparison between the informative patterns across the two phases of the task (and maybe even across hemispheres) might be also of interest.

4) Whole brain univariate analyses and searchlight analyses

I think it would be useful to provide whole brain data of the univariate and multivariate analyses for the two phases of the task. The authors might consider adding the results of these analyses as supplementary figures.

Whole brain univariate results could provide an overview of the network of regions engaged during the planning of finger movements. Looking at Figure 1, it seems that there is only an anterior parietal region recruited by the task (possibly at an uncorrected level). I would expect to see activation in parieto-frontal regions. This analysis might also partially answer to my first point.

I also would suggest conducting searchlight analyses for the two phases of the task. These analyses might serve different purpose. First, these results could provide insights on additional regions (e.g. parietal regions, SII) representing different planned finger movements. Second, as most (if not all) previous MVPA studies adopting paradigm with a delayed execution in the field investigated complex hand/arm actions, the present data might provide an indirect indication on which part of the parieto-frontal motor networks are engaged for simple finger movements and which are specific for more complex arm/hand actions.

Finally, the comparison/conjunction between univariate and multivariate results might provide insights on the regions within the motor networks which are both engaged by the task and host a representation of the different movements. This analysis would be particularly interesting for the planning phase of the task.

5) Comparison of the topography of informative patterns between the planning and execution phase of the task

I apologise in advance if this concern is related to my misinterpretation of this analysis, as I'm not familiar with the Hotelling T2 test (lines 130-132). My understanding of this test is that it allows to test the difference between two (multivariate) means of different distributions. I think that the authors compared the distribution of the crossnobis distance for M1 and S1 across subjects.

If my understanding of the analysis is correct, then this analysis showed that the distribution of crossnobis distances within M1 and S1 is consistent across the two phases, but this analysis doesn't directly test if the spatial organization of the informative patterns across the 2 phases is different. I would suggest to directly compare the distribution of crossnobis distance between the two phases across subjects independently in each vertex of M1 and S1. The result of this analysis would be a spatial map of the difference between the distributions of the two phases. This would allow to appreciate if there is any spatial asymmetry between the 2 phases.

As a general recommendation, I would suggest providing additional details in the description of this analysis to allow an easier understanding of its implications.

Reviewer #3 (Recommendations for the authors):

1. I urge the authors to include more details regarding the analysis they conducted. This is an overall recommendation, but a few things were specifically unclear to me:

a. Since the PCM is not a commonly used analysis, the manuscript would especially benefit from more conceptual explanation regarding the rationale of conducting this analysis, further explanation on the method, and how the achieved r-values can be interpreted.

b. What does the colourbar scaling represent in Figure 1B and C – is this percent signal change?

c. Similarly – Are all coloured vertices in Figure 2A-D significantly activated/ have significant distances? Or what does a value of 0.1 and 0.001 represent? Also, are the maps in Figure 2A and C corrected for multiple comparisons? This is not reported in the Methods section or the figure legend.

d. What analysis was ran to test for informative patterns in M1 and S1 while correcting for the influence of behavioural patterns – this analysis is not reported in the methods section.

e. Did the authors correct for multiple comparisons across the ROIs tested?

f. For the analysis presented Figs 2E and F, did the authors average across the y-coordinates on the whole brain flatmap, or were this values extracted from a single straight line? i.e. do the distances on the cortical surface represent averages or rather values from single vertices? This analysis is not mentioned in the Methods section.

g. The PCM rationale is better explained in the Results section, from only reading the methods section the rationale behind this method was unclear.

h. Regarding the PCM analysis: If a model of 0.4 correlation is most predictive, can we conclude the representational patterns are significantly correlated? Or how can we interpret these correlational models?

i. Regarding the PCM analysis: How could a best fitting model perform better from the one-correlation model (i.e. does it make sense to test achieved r>1)? Isn't a correlation of 1 the maximum achievable value? I think that if activity patterns during planning and execution are truly a mere scaled version of each other, the correlation between the activity patterns should be 1. So if the achieved r is sig less than 1, this would argue against the scaled version argument. This means that the authors should test if their model performs sig less than the one-correlation model.

2. What could potentially be added to the argument against micromovements explaining neural distances in S1/M1 during movement planning is that the expected correlation would be positive, while you see a non-significant negative correlation. It would also be interesting to test whether there are significant differences between the behavioural and neural distances during planning and execution for each ROI. For me that would be more convincing than showing a mere non-significant correlation (without Bayesian stats).

https://doi.org/10.7554/eLife.69517.sa1

Author response

Reviewer #1 (Recommendations for the authors):

• To help the reader, the description of the PCM analyses should be improved. In particular:

1. It is not clear how the correlation models are conceived.

Each correlation model was created as a second moment matrix with a fixed correlation value on the diagonal of the off-diagonal blocks. The number of correlation models was chosen arbitrarily, in N steps between 0 and 1 (in our case N = 100). Ultimately, the number of models does not matter, it only determines the resolution of correlation values tested (i.e., the resolution on the x-axis in Figure 4C). We now provide more details in the description of PCM analyses (page 24, line 733-758).

2. It is not clear under which form the data are treated (pattern, RDM or second-moment matrix) and how the planning/execution are compared under a given correlation model.

In PCM a representational model is formulated by its predicted second moment matrix. The data is evaluated by determining the likelihood of the data under a Gaussian distribution with that second moment. We transformed the second moment matrices into RDMs in Figure 4A, as we believe that many readers will find an RDM slightly more intuitive. However, there is a 1:1 relationship between second moment matrices and RDMs. In both cases, the correlation between planning and execution (across fingers) can be seen as the diagonal of the off-diagonal block of this matrix.

3. The overall procedure of PCM should be better described in the methods.

We now expanded our description of the overall PCM procedure (page 23-24, line 717-758). In addition to the original PCM paper, we now also provide a link to a Jupyter Notebook example of running a PCM analysis for a simulated example with a very similar structure.

4. Figure 4D is rather hard to understand and the legends should be improved.

We have now added information to the figure legend to improve understanding of the different components in Figure 4.

• Providing a comparison between M1 and S1 (at the RDM level) could be interesting (e.g., is the representational structure more similar between M1 and S1 for planning or for execution?).

Because we measured only 3 fingers, the RDM within planning and execution is characterized only by three distances. Thus, a formal comparison of the RDMs between M1 and S1 has very little power. However, as can be seen from Figure 4B, the average RDM structure stays very comparable across planning and execution both in M1 and S1. Indeed, in a more complete experiment where we measured all 5 fingers (Ejaz et al., 2015, supplementary figure 4), we found no significant difference in the RDM structure across M1 and S1.

Reviewer #2 (Recommendations for the authors):

Even though the study is of high quality, I have a concern regarding the fact that recent research showed similar evidence for the role of S1 during motor planning (Gale et al., 2021). These findings partially limit the novelty and the impact of the present investigation. Following the evidence emerging from this recent research, I think the authors might consider conducting additional analyses to strengthen their research and better characterise the presented results.

Given the high spatial resolution of the study, I think that it might be helpful to focus on specific sub-fields of the somatosensory cortex to dissect their specific roles. In addition, based on the recent description of the possible role of ipsilateral cortices in representing movements, it would be also interesting to investigate the role of ipsilateral S1 (and M1) and to compare the pattern of finger-specific movements within these regions with the regions of the contralateral hemisphere. Finally, it would be interesting to provide whole brain data both for the univariate and the multivariate analyses in order to provide a global overview of the involved brain regions and of the brain areas hosting finger-specific patterns.

We thank Reviewer #2 for the overall appreciation of our study. We agree that adding whole-brain maps (including ipsilateral M1/S1) as supplementary material would provide a more complete picture of our results. Using 3 possible actions and advanced statistical methods like PCM to test for the correspondence of movement-specific activation patterns between planning and execution, our study provides an important further insight into the planning activity in M1 and S1. Second, on a technical note, we believe that our paper provides a more thorough control for micro-movements during planning (by monitoring finger forces during the preparation phase) and a cleaner separation of planning and execution (by limiting the analysis to no-go trials). We now acknowledge the Gale et al. (2021) paper in the Discussion section (page 16, line 456-461).

1) Lack of activation during the planning phase

Most previous fMRI studies on motor planning using complex movements instructed participants to stay still during the planning phase. It is not surprising to see deactivation or activation at baseline during this phase.

In this study, the lack of activation in S1 and M1 during planning is surprising for me, given that the participants were engaged in a visuomotor task throughout the whole duration of the preparation/planning phase (Figure 1A, lines 303-308). Participants had to pre-activate their fingers by maintaining a steady force on all the keys during the preparation phase. Moreover, a visual feedback showing the applied force was displayed and participants had to apply a force to maintain the level of this feedback within a specific range.

This type of task is rather demanding, so I would expect to see the engagement of parieto-frontal motor networks during motor planning. Could it be possible that the lack of activation in S1 and M1 during planning is simply due to how the data were modelled in the GLM. The authors reported that they modelled only the first 2 seconds of the preparation phase (lines 394-397) as this phase has different durations (4-8 seconds). As an additional control analysis, it might be interesting to directly compare the preparation phase with the initial and final baseline by explicitly modelling the baseline in the GLM.

As a general suggestion, I would recommend the authors to provide whole brain analysis as supplementary materials (see point 4).

Reviewer #1 made a similar remark about the nature of the baseline task and the lack of activation during planning. In short, we have good reasons to believe that this can be explained by a combination of training effect and limited planning demands for single finger presses.

2) Specificity of the described effects in the somatosensory cortex

I would suggest investigating the role of the different subsections of S1. One possibility could be to adopt the parcellation of the Anatomy toolbox, as implemented in a recent investigation (Gale et al., 2021), or by analysing separately the different cytoarchitectonic maps adopted to create the S1 ROI (see lines 416-417). This study (Gale et al., 2021) suggest that S1 subsectors might represent different information during action planning. Support to the possible role of S1 in motor control comes from a recent neurophysiological study showing the possible encoding of complex arm movements, and not only of somatosensory information, within area BA 2 of macaque monkeys (Chowdhury et al., 2020). The higher spatial resolution of the present study might provide support to the data presented in the and/or new insights of the specificity of the described effect in the somatosensory cortex.

Thanks for this suggestion, we agree that looking into different S1 subsections could potentially enrich our paper. Therefore, we have repeated our profile ROI analysis (Figure 2E-2F) to allow for separation of the different subdivisions of M1 and S1. We used rectangular searchlights (50 per virtual strip in each hemisphere) that were defined to align with the boundaries between the different subdivision (see Author response image 1A, where each color corresponds to a different searchlight). Based on these surface-based ROIs, we defined the voxel-based ROIs in the individual brains. For statistical purposes, the rectangular searchlights were successively grouped by averaging within-ROI subdivisions (see Author response image 1B). This surface-based approach provides a more accurate separation of different subdivisions than the volume-based approach—see Fischl et al. (2008). The subdivisions are now clearly labeled in the profile plots (Author response image 1C) and new (Figure 2E-2F).

Author response image 1
A.

Example of rectangular searchlights used to examine different subsections of our ROIs, superimposed on the flat map of the left hemisphere. Each color corresponds to a different searchlight (i.e., one point on the x-axis in B). Black outlines denote approximate boundaries of the different subdivisions of the ROIs (see also vertical dotted lines in B). White dotted lines indicate the three main sulci for our regions of interest: precentral sulcus (pre-CS), central sulcus (CS), and postcentral sulcus (post-CS). B. New crossnobis distance analysis (top) and corresponding ratio of the distances (bottom) during planning (no-go trials only, orange) and execution (blue). Black triangles denote approximate location of sulci as anatomical landmarks.

As for potential differences between the subdivisions, we hypothesized that the relative size of the distances during planning and execution activity may differ between subregions. However, when we computed the ratio between distances during planning and execution, we did not find any major differences in the different subdivisions of M1 and S1 (see black line at the bottom of (Author response image 1B) ; BA 4a: 0.23, BA 4b: 0.16, BA 3a: 0.24, BA 3b: 0.19, BA 1: 0.22, BA 2: 0.31; mean across subdivisions: 0.234 ± 0.019). Thus, this shows that the evidence for planning activity (in relationship to what is seen in execution) is roughly stable across subregions of S1 and M1. We included this analysis in the manuscript (page 7-8, line 143-205).

3) Role of ipsilateral hemisphere

I would also suggest conducting the same type of analyses on S1 and M1 of the ipsilateral hemisphere as a meaningful control site. Moreover, this analysis could provide novel insights on the possible role of ipsilateral S1 in representing ipsilateral movements. Indeed, there is a growing body of evidence showing the representation of ipsilateral movements in the motor cortex (Bundy and Leuthardt, 2019), but much less is known on the role of S1. If a representation of the planned movements is present in one or more subsectors of S1 (see Gale et al. 2021), the comparison between the informative patterns across the two phases of the task (and maybe even across hemispheres) might be also of interest.

We agree that the role of ipsilateral S1 and M1 during planning and execution is a highly interesting question, albeit slightly orthogonal to the scope of our study. Indeed, in Berlot et. al (2018b) we speculated whether widespread representation of finger movements in the ipsilateral hemisphere could be explained by planning-related processes. Our current data only provide mixed evidence for this idea. When we performed the profile analysis (analogous to Figure 2E-2F) in the ipsilateral hemisphere we found slight deactivation around the central sulcus (comparable to the contralateral hemisphere) and very small (non-significantly different from zero) distances during planning (Author response image 2) . From this analysis, it is hard to conclude whether this null result speaks against a role of the ipsilateral hemisphere in planning or is simply due to lack of power / noise in the data.

Author response image 2
A.

Profile ROIs analysis of activation (mean percent signal change) in the ipsilateral (right) hemisphere for planning (orange) and execution (blue). Vertical dotted lines denote boundaries of ROI subdivisions. Black triangles denote the three main sulci in the right hemisphere: postcentral sulcus (post-CS), central sulcus (CS), and precentral sulcus (pre-CS). Horizontal bars indicate significance (p < 0.05) in a 2-sided one-sample t-test vs zero. B. Same as A but for multivariate crossnobis distance.

4) Whole brain univariate analyses and searchlight analyses

I think it would be useful to provide whole brain data of the univariate and multivariate analyses for the two phases of the task. The authors might consider adding the results of these analyses as supplementary figures.

Whole brain univariate results could provide an overview of the network of regions engaged during the planning of finger movements. Looking at Figure 1, it seems that there is only an anterior parietal region recruited by the task (possibly at an uncorrected level). I would expect to see activation in parieto-frontal regions. This analysis might also partially answer to my first point.

I also would suggest conducting searchlight analyses for the two phases of the task. These analyses might serve different purpose. First, these results could provide insights on additional regions (e.g. parietal regions, SII) representing different planned finger movements. Second, as most (if not all) previous MVPA studies adopting paradigm with a delayed execution in the field investigated complex hand/arm actions, the present data might provide an indirect indication on which part of the parieto-frontal motor networks are engaged for simple finger movements and which are specific for more complex arm/hand actions.

Finally, the comparison/conjunction between univariate and multivariate results might provide insights on the regions within the motor networks which are both engaged by the task and host a representation of the different movements. This analysis would be particularly interesting for the planning phase of the task.

We agree and now provide both univariate activation and multivariate searchlight whole brain (both hemispheres) maps for the two phases of the task (planning and execution) as supplementary figure (Figure 2 – supplement 2).

5) Comparison of the topography of informative patterns between the planning and execution phase of the task

I apologise in advance if this concern is related to my misinterpretation of this analysis, as I'm not familiar with the Hotelling T2 test (lines 130-132). My understanding of this test is that it allows to test the difference between two (multivariate) means of different distributions. I think that the authors compared the distribution of the crossnobis distance for M1 and S1 across subjects.

If my understanding of the analysis is correct, then this analysis showed that the distribution of crossnobis distances within M1 and S1 is consistent across the two phases, but this analysis doesn't directly test if the spatial organization of the informative patterns across the 2 phases is different. I would suggest to directly compare the distribution of crossnobis distance between the two phases across subjects independently in each vertex of M1 and S1. The result of this analysis would be a spatial map of the difference between the distributions of the two phases. This would allow to appreciate if there is any spatial asymmetry between the 2 phases.

As a general recommendation, I would suggest providing additional details in the description of this analysis to allow an easier understanding of its implications.

To improve the comparison of the topography of activity patterns between planning and execution, we additionally computed the ratio between planning and execution profiles (page 7-8, line 145-205). We also embraced the reviewer’s suggestion and expanded on the logic for this comparison (page 7, line 143-147).

Reviewer #3 (Recommendations for the authors):

1. I urge the authors to include more details regarding the analysis they conducted. This is an overall recommendation, but a few things were specifically unclear to me:

a. Since the PCM is not a commonly used analysis, the manuscript would especially benefit from more conceptual explanation regarding the rationale of conducting this analysis, further explanation on the method, and how the achieved r-values can be interpreted.

We expanded our methods to better explain the rationale for our analysis and the interpretation both in the result section (page 12-13, line 293-390) and in methods (page 23-24, line 717-758). We also have provided reference to the now extensive online documentation on the PCM toolbox and the fully-fledged example of the correlation analysis presented here.

b. What does the colourbar scaling represent in Figure 1B and C – is this percent signal change?

There is no colorbar in Figure 1B. If the reviewer is referring to Figure 2A-D, colorbars in A and C reflect percent signal change (as in 2E), whereas colorbars in B and D reflect distance (arbitrary units, as in 2F). We now clarified this information in the figure and figure caption.

c. Similarly – Are all coloured vertices in Figure 2A-D significantly activated/ have significant distances? Or what does a value of 0.1 and 0.001 represent? Also, are the maps in Figure 2A and C corrected for multiple comparisons? This is not reported in the Methods section or the figure legend.

All surface maps are intended for visualization purposes only and are not corrected for multiple comparisons. We based our conclusions on statistical tests performed at the ROI level (see Figure 2E-F, horizontal bars). The numbers below the colorbars are not p-values; rather, they indicate either percent signal change values (A-C) or crossnobis distance values (B-D).

d. What analysis was ran to test for informative patterns in M1 and S1 while correcting for the influence of behavioural patterns – this analysis is not reported in the methods section.

The analysis showing informative patterns in M1 and S1 while correcting for the influence of behavioural patterns can be seen in Figure 3C. Page 10, line 264-267: “More importantly, a significantly positive intercept in the linear fit in Figure 3C (M1: p = 0.032; S1: p = 0.007) shows that, even after correcting for the influence behavioral patterns, the activity patterns in M1 and S1 remained informative”. We now added the intercept p-values also in Figure 3C and this rationale in the methods section (page 23, line 712-715).

e. Did the authors correct for multiple comparisons across the ROIs tested?

We did not, but we limited the number of tests by only including two ROIs in our PCM analysis.

f. For the analysis presented Figs 2E and F, did the authors average across the y-coordinates on the whole brain flatmap, or were this values extracted from a single straight line? i.e. do the distances on the cortical surface represent averages or rather values from single vertices? This analysis is not mentioned in the Methods section.

The inset at the top of Figure 2 shows the inflated cortical surface of the contralateral (left) hemisphere and highlights the virtual strip used for the cross-section analysis (white). The outline of the white strip shows the area that was used to create the new profile ROIs that result in the values plotted on the y-axis of Figure 2E-F. We added this new analysis description to the methods section (page 22-23, line 684-696), and to the caption of Figure 2.

g. The PCM rationale is better explained in the Results section, from only reading the methods section the rationale behind this method was unclear.

We now added more information about the rationale for the PCM analysis to the methods section as well (page 23-24, line 718-732).

h. Regarding the PCM analysis: If a model of 0.4 correlation is most predictive, can we conclude the representational patterns are significantly correlated? Or how can we interpret these correlational models?

We can conclude that the patterns are significantly correlated because correlation models from 0.4 to 1 perform significantly better than the zero-correlation model. However, given that the likelihood of the data did not differ significantly among models with a correlation of > 0.4, we do not see any evidence to prefer one of these models over each other. This could be either because the true correlation is partial (less than 1), or simply due to measurement noise (even if the true correlation was in fact 1).

i. Regarding the PCM analysis: How could a best fitting model perform better from the one-correlation model (i.e. does it make sense to test achieved r>1)? Isn't a correlation of 1 the maximum achievable value? I think that if activity patterns during planning and execution are truly a mere scaled version of each other, the correlation between the activity patterns should be 1. So if the achieved r is sig less than 1, this would argue against the scaled version argument. This means that the authors should test if their model performs sig less than the one-correlation model.

The maximum correlation achievable is indeed 1. We did not test whether the best fitting model has a correlation > 1. Rather, we tested if the best fitting model (at the group level, found using a cross-validation, see Methods) had a higher log-likelihoods than the log-likelihoods for the 1-correlation model (Figure 4C). So, by “performs better”, we mean “has a higher likelihood”, not “has a higher correlation”. Our analysis shows that the best fitting model does not perform significantly better than the 1-correlation model. This is compatible with the view that planning and execution patterns are a scaled version of each other. We now clarify this in the text (page 13, line 380-384).

2. What could potentially be added to the argument against micromovements explaining neural distances in S1/M1 during movement planning is that the expected correlation would be positive, while you see a non-significant negative correlation. It would also be interesting to test whether there are significant differences between the behavioural and neural distances during planning and execution for each ROI. For me that would be more convincing than showing a mere non-significant correlation (without Bayesian stats).

We agree that the idea of micro-movements would have predicted a positive correlation. The argument that the correlation was non-significant, and even slightly negative, does not provide the strongest evidence against this hypothesis. Our conclusion that micromovements cannot explain the representations found is that, even if we control for the influence of micromovements, the distances both in M1 and S1 are significant. This is now clearly outlined in the text (page 10, line 264-267) and we added the significant p-value for the intercept to Figure 3C.

https://doi.org/10.7554/eLife.69517.sa2

Article and author information

Author details

  1. Giacomo Ariani

    1. The Brain and Mind Institute, Western University, London, Canada
    2. Department of Computer Science, Western University, London, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing - original draft, Writing - review and editing
    For correspondence
    giacomo.ariani@gmail.com
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9074-1272
  2. J Andrew Pruszynski

    1. The Brain and Mind Institute, Western University, London, Canada
    2. Department of Physiology and Pharmacology, Western University, London, Canada
    3. Department of Psychology, Western University, London, Canada
    4. Robarts Research Institute, Western University, London, Canada
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0786-0081
  3. Jörn Diedrichsen

    1. The Brain and Mind Institute, Western University, London, Canada
    2. Department of Computer Science, Western University, London, Canada
    3. Department of Statistical and Actuarial Sciences, Western University, London, Canada
    Contribution
    Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Writing - review and editing
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0264-8532

Funding

Canada First Research Excellence Fund (BrainsCAN)

  • Jörn Diedrichsen

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 a NSERC Discovery Grant (RGPIN-2016-04890) awarded to JD, and the Canada First Research Excellence Fund (BrainsCAN). The authors wish to thank Eva Berlot for helpful discussions and contributions to data analysis.

Ethics

All experimental procedures were approved by the Research Ethics Committee at Western University (HSREB protocol 107061). Participants provided written informed consent to procedures and data usage and received monetary compensation for their participation.

Senior and Reviewing Editor

  1. Chris I Baker, National Institute of Mental Health, National Institutes of Health, United States

Reviewers

  1. Andrea Serino
  2. Sanne Kikkert, ETH Zürich, Switzerland

Publication history

  1. Preprint posted: December 18, 2020 (view preprint)
  2. Received: April 17, 2021
  3. Accepted: January 11, 2022
  4. Accepted Manuscript published: January 12, 2022 (version 1)
  5. Version of Record published: January 24, 2022 (version 2)

Copyright

© 2022, Ariani 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. Giacomo Ariani
  2. J Andrew Pruszynski
  3. Jörn Diedrichsen
(2022)
Motor planning brings human primary somatosensory cortex into action-specific preparatory states
eLife 11:e69517.
https://doi.org/10.7554/eLife.69517
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