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Locus coeruleus spiking differently correlates with S1 cortex activity and pupil diameter in a tactile detection task

  1. Hongdian Yang  Is a corresponding author
  2. Bilal A Bari
  3. Jeremiah Y Cohen
  4. Daniel H O'Connor  Is a corresponding author
  1. Department of Molecular, Cell and Systems Biology, University of California, Riverside, United States
  2. Department of Neuroscience, Brain Science Institute, and Kavli Neuroscience Discovery Institute, Johns Hopkins School of Medicine, United States
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Cite this article as: eLife 2021;10:e64327 doi: 10.7554/eLife.64327

Abstract

We examined the relationships between activity in the locus coeruleus (LC), activity in the primary somatosensory cortex (S1), and pupil diameter in mice performing a tactile detection task. While LC spiking consistently preceded S1 membrane potential depolarization and pupil dilation, the correlation between S1 and pupil was more heterogeneous. Furthermore, the relationships between LC, S1, and pupil varied on timescales of sub-seconds to seconds within trials. Our data suggest that pupil diameter can be dissociated from LC spiking and cannot be used as a stationary index of LC activity.

Introduction

Multiple lines of evidence implicate the locus coeruleus/norepinephrine (LC/NE) system in perceptual task performance. First, LC activity modulates feedforward processing of sensory stimuli (Hirata et al., 2006; Devilbiss et al., 2006; Rodenkirch et al., 2019) and impacts sensory cortex states (Constantinople and Bruno, 2011; Polack et al., 2013). Second, LC activity correlates with task performance (Rajkowski et al., 1994; Usher et al., 1999) and pupil diameter (Rajkowski et al., 1994; Joshi et al., 2016; Reimer et al., 2016; Liu et al., 2017). Finally, pupil diameter is thought to index arousal and has been found to be correlated with neuronal and behavioral detection or discrimination sensitivity (Reimer et al., 2014; McGinley et al., 2015a; McGinley et al., 2015b; Vinck et al., 2015; Lee and Margolis, 2016; Schriver et al., 2018; Lee et al., 2020; Cazettes et al., 2021), as well as decision bias (de Gee et al., 2014; de Gee et al., 2020). Since sensory cortex activity impacts perceptual reports (Sachidhanandam et al., 2013; Miyashita and Feldman, 2013), these observations suggest the hypothesis that LC/NE modulates sensory cortex activity and affects perceptual task performance and that this effect can be monitored noninvasively via the easy-to-measure pupil diameter. Testing this hypothesis requires simultaneous measurement of (1) LC activity, (2) cortical activity, ideally subthreshold membrane potential, and (3) pupil diameter, all during perceptual task performance. Here, we recorded spiking activity of optogenetically-tagged LC units together with pupil diameter in mice performing a tactile detection task (Yang et al., 2016). In a subset of experiments, we also performed simultaneous whole-cell current clamp recordings in S1 (Figure 1).

Figure 1 with 1 supplement see all
Cortical membrane potential, LC spike rate, and pupil recorded during a tactile detection task.

(a) Task schematic, trial structure, and all trial types of the single-whisker detection task (Yang et al., 2016). (b) Schematic of tetrode recording in LC, whole-cell recording in S1, and pupil tracking during the task. (c) Expression of ChR2 in a Dbh;Ai32 mouse (ChR2-EYFP: green; tyrosine hydroxylase TH: red). (d) Left: Responses of a ChR2-expressing LC unit to opto-tagging (lightning bolts: blue light pulses) and tail pinch. Middle: LC unit responses to 12 blue light pulses (200 ms) aligned to individual pulse onset. Ticks represent spikes. PSTH is shown at the bottom. Right: Typical wide waveforms of LC units and an electrolytic lesion (arrow: lesion site) in the LC (white) showing the recording location. (e) Example simultaneously recorded LC activity, S1 Vm, and pupil with auditory cue and whisker stimulation onsets indicated. Trace is from a brief period of non-performance during a behavioral session and so there are no licks. The example pupil size is typical for all sessions (Figure 1—figure supplement 1).

Results

First, we report the analysis of LC and pupil recordings during behavior (e.g., Figure 2a). Consistent with prior reports (Joshi et al., 2016; Reimer et al., 2016; Liu et al., 2017), cross-correlogram analysis revealed that LC spiking activity and pupil diameter were correlated across entire sessions, with pupil dilation following LC spikes (peak correlation coefficient: 0.15 ± 0.02; time lags: 2.61 ± 0.39 s, n = 39 recordings, Figure 2b). Mean LC spiking activity aligned with trial onsets showed prominent responses to a tone delivered at the beginning of each trial, as well as in trials where mice made Go (licking) responses (Hit and False Alarm trials, Figure 2a,c). LC spiking activity to the tone was comparable to Go responses (p=0.24, Figure 2d, Materials and methods). On Hit trials, where mice successfully licked to the whisker stimulus, pre-stimulus LC activity (measured in a 0.5 s window prior to stimulus onset) was slightly but significantly lower than Miss trials, where mice failed to lick to the whisker stimulus (Figure 2e). We note that on Miss trials, LC responded weakly to the whisker stimulus alone (<0.5 sp/s above baseline, Figure 2—figure supplement 1). LC activity measured in a short window (0.2 s) after stimulus onset was larger on Hits compared with Misses (Figure 2e; the same trend holds for 0.1 s window, data not shown). Ideal-observer analysis showed that both pre- and post-stimulus LC activity significantly predicted perceptual reports of the mice on a trial-by-trial basis, with choice probabilities (Yang et al., 2016) of 0.47 ± 0.014 (p=0.032, n = 43) for pre-stimulus and 0.59 ± 0.017 (p=4.6e-6, n = 43) for post-stimulus LC activity, respectively (Figure 2e). LC activity aligned to the time of licking showed that spiking responses began ~200 ms prior to licking (Figure 2f).

Figure 2 with 2 supplements see all
LC and pupil responses during behavior.

(a) Example LC recording with pupil tracking. Left: LC spike raster separated by trial types. Right: Mean pupil diameter (± s.e.m.) separated by trial types. Gray and black arrows indicate tone and stimulus onsets, respectively. Gray and black bars indicate the time windows during which pupil responses to tone and to Go (behavioral responses) were quantified, respectively. We note that based on the temporal profiles of pupil diameter in different trial types (i.e., in the presence or absence of tactile stimulus or licking) and that the tactile stimulus starts 1 s after tone onset, pupil responses to tone and Go can be segregated (Materials and methods). (b) Top: Grand average cross-correlogram between LC spike train and pupil diameter (n = 39). Individual LC spikes were convolved with a 400 ms wide Gaussian kernel. Spike times were shuffled and LC–pupil correlations computed to establish controls (narrow gray band around zero). Bottom: Histogram of peak correlation coefficient (left), and time lags (right) between LC spike train and pupil diameter for each paired recording (magenta dot: mean). Both distributions are significantly larger than 0 (peak correlation coefficient: 0.15 ± 0.02, p=8.3e-7, signed rank = 743; time lags: 2.61 ± 0.39 s, p=7.8e-7, signed rank = 744, n = 39). (c) Grand average trial-aligned LC spike rate (n = 43, top), and pupil diameter (n = 36, bottom) averaged by different trial types. Gray and black arrows indicate tone and stimulus onsets, respectively. In Hit trials, the latency of LC responses to tone onset was 0.064 ± 0.005 s, and to whisker stimulation onset was 0.111 ± 0.008 s, and the reaction time (first lick latency to whisker stimulation onset) of the mice was 0.58 ± 0.03 s. (d) Left: LC responses to tone (T) and Go responses (G) during Hit trials with median indicated. Tone vs. Go: 4.79 (3.70–6.66) sp/s vs. 4.68 (3.33–7.26) sp/s, median (IQR), p=0.24, signed rank = 496.5, n = 43. Right: Pupil responses to tone and Go responses during Hit trials with median indicated. Tone vs. Go: 0.003 (−0.015–0.015) mm vs. 0.027 (−0.010–0.063) mm, median (IQR), p=6.4e-5, signed rank = 559, n = 36. Gray lines indicate individual recordings. (e) Top: Pre-stimulus (baseline) and post-stimulus (evoked) LC spike rate for Hit and Miss trials with median indicated (Baseline: Hit vs. Miss, 0.66 (0.30–3.51) sp/s vs. 1.55 (0.68–3.00) sp/s, median (IQR), p=0.0083, signed rank = 254.5; Evoked: Hit vs. Miss, 3.24 (1.78–5.49) sp/s vs. 1.82 (0.95–3.45) sp/s, median (IQR), p=5.5e-7, signed rank = 782.5, n = 43). Gray lines indicate individual recordings. Bottom: Histogram of choice probability for Hit vs. Miss trials based on baseline and evoked LC activity (magenta dots: mean). Choice probabilities are significantly deviated from 0.5. Baseline: 0.47 ± 0.014, p=0.032, signed rank = 295.5; Evoked: 0.59 ± 0.017, p=4.6e-6, signed rank = 751, n = 43. (f) Lick-aligned LC spike rate (top) and pupil diameter (∆Pupil, bottom) averaged by trial types: Hit (blue), FA (green).

Figure 2—source data 1

MATLAB R2016b file with data shown in panels b, d and e.

https://cdn.elifesciences.org/articles/64327/elife-64327-fig2-data1-v1.zip

In striking contrast, pupil diameter minimally increased in response to the tone. Instead, pupil strongly dilated on Hit and False Alarm trials, in which mice made Go (licking) responses (Figure 2a,c,d; tone vs. Go: p=6.4e-5, n = 36, Materials and methods) (Lee and Margolis, 2016). Interestingly, pupil response to the tone was larger on Misses compared to Hits and significantly predicted perceptual choices of the mice (Figure 2—figure supplement 2). Pupil diameter changes (∆Pupil) aligned to the time of licking showed that pupil responses occurred after licking (Figure 2f).

Together, these data show that LC and pupil responses were positively correlated. Both LC activity and pupil diameter increased during licking responses, but LC also strongly responded to the tone, a salient sensory cue that alerted mice to trial onsets. Thus, LC activity and pupil diameter appear to reflect different sets of task events during this behavior.

Next, we analyzed recordings where we simultaneously measured membrane potential (Vm) of S1 neurons (mostly from layer 2/3, Figure 3—figure supplement 1), along with LC spiking and/or pupil diameter during the detection task. Our goal was to determine how LC spiking related to cortical activity and to pupil diameter during task performance. We used spike-triggered averages (STAs) to quantify how individual spikes from single LC units correlated with changes in Vm and pupil diameter. LC spike-triggered Vm analyses revealed that LC spikes were associated with a depolarization in cortical neurons (1.39 ± 0.35 mV, n = 12, Figure 3a–c). On average, Vm depolarization associated with an LC spike peaked after the spike, with short time lags from an LC spike to peak depolarization in S1 (0.17 ± 0.06 s, n = 12, Figure 3a–c; also see Figure 3—figure supplement 2).

Figure 3 with 3 supplements see all
Different relationships between LC spikes, S1 Vm, and pupil diameter.

(a) Two examples of LC spike-triggered average ∆Vm. (b) Group mean of LC spike-triggered average ∆Vm (± s.e.m., n = 12). (c) Histograms of peak ∆Vm and peak lags (showing all LC–S1 pairs) with means indicated (magenta dots). Both distributions are significantly larger than 0 (peak ∆Vm: 1.39 ± 0.35 mV, p=4.9e-4, signed rank = 78; peak lags: 0.17 ± 0.06 s, p=4.9e-4, signed rank = 78, n = 12). (d) Two examples of LC spike-triggered average ∆Pupil. (e) LC spike-triggered average ∆Pupil group mean (± s.e.m., n = 7). (f) Histograms of peak ∆Pupil and peak lags (showing all LC–Pupil pairs) with means indicated (magenta dots). Both distributions are significantly larger than 0 (peak ∆Pupil: 0.03 ± 0.01 mm, p=0.016, signed rank = 28; peak lags: 1.89 ± 0.25 s, p=0.016, signed rank = 28, n = 7). (g) Two examples of Pupil–Vm cross-correlograms. (h) Group mean of Pupil–Vm cross-correlograms (± s.e.m., n = 19). (i) Histograms of peak Pupil–Vm correlation coefficient and peak lags (showing all S1–Pupil pairs) with means indicated (magenta dots). Both distributions are not significantly deviated from 0 (peak correlation coefficient: 0.05 ± 0.04, p=0.33, signed rank = 119; peak lags: - 0.22 ± 1.01 s, p=0.87, signed rank = 99, n = 19). (j) Group mean of the time derivative of pupil (Pupil’)–Vm cross-correlograms (± s.e.m., n = 19). (k) Histograms of peak Pupil’-Vm correlation coefficient and peak lags with means indicated (magenta dots). Both distributions are significantly larger than 0 (peak correlation coefficient: 0.15 ± 0.03, p=1.6e-4, signed rank = 189; peak lags: 1.31 ± 0.24 s, p=1.3e-4, signed rank = 190, n = 19). (l) Left: Peak correlation coefficient for LC–Vm, Pupil–Vm and Pupil’–Vm pairs after progressive high-pass filtering of S1 Vm. Right: Exponential decay functions (corr. coef. = a*exp(−freq*μ)) were fitted to these curves. The time constant μ is significantly different (repeated-measures ANOVA, F(2, 36)=74.5, p=1.6e-13, n = 19). Post hoc Tukey–Kramer tests revealed that the LC–Vm relationship had the slowest decay and Pupil–Vm had the fastest decay. LC–Vm vs. Pupil–Vm, p=5.9e-8; LC–Vm vs. Pupil’–Vm, p=0.0037; Pupil–Vm vs. Pupil’–Vm, p=7.1e-7.

Figure 3—source data 1

MATLAB R2016b file with data shown in Figure 3 c, f, i, k and l.

https://cdn.elifesciences.org/articles/64327/elife-64327-fig3-data1-v1.zip

Consistent with the previous cross-correlogram analysis based on a larger set of LC–pupil recordings (Figure 2b), here STA analysis showed that pupil diameter increased in association with individual spikes from LC single units (0.03 ± 0.01 mm, n = 7), with peak dilation occurring roughly 10-fold slower than peak Vm depolarization (time lags from an LC spike to peak pupil dilation: 1.89 ± 0.25 s, n = 7, Figure 3d–f).

Given that pupil diameter and LC activity are positively correlated, and that pupil diameter has been often considered to index LC activity (Aston-Jones and Cohen, 2005; McGinley et al., 2015b), we next tested whether the pupil–S1 relationship resembled the LC–S1 relationship. Cross-correlogram analyses revealed heterogeneous correlations between pupil diameter and S1 Vm, with both positive and negative correlations as well as positive and negative time lags (peak correlation coefficient: 0.05 ± 0.04; time lags: −0.22 ± 1.01 s, n = 19, Figure 3g–i). In sharp contrast, the time derivative of pupil diameter (pupil’) was positively correlated with S1 Vm in a more consistent manner (peak correlation coefficient: 0.15 ± 0.03; time lags: 1.31 ± 0.24 s, n = 19, Figure 3j,k; Reimer et al., 2016; Reimer et al., 2014). We further examined how well LC spiking and pupil diameter can predict cortical Vm fluctuations at different timescales. We found that LC activity was superior in predicting cortical dynamics faster than ~200–300 ms (exponential decay time constant: LC–Vm vs. Pupil–Vm vs. Pupil’-Vm, 1.02 ± 0.09 vs. 6.59 ± 0.60 vs. 1.53 ± 0.14, Figure 3l; repeated-measures ANOVA, F(2, 36)=74.5, p=1.6e-13, n = 19). Post hoc Tukey–Kramer tests revealed that LC activity reflects higher frequency correlations with Vm compared with the pupil (LC–Vm vs. Pupil–Vm, p=5.9e-8; LC-Vm vs. Pupil’–Vm, p=0.0037; Pupil–Vm vs. Pupil’–Vm, p=7.1e-7). On the other hand, the LC–Pupil’ relationship was similar to that of LC–Pupil (compare Figure 3—figure supplement 3 with Figure 3e,f).

Together, these data show that LC spikes preceded S1 depolarizations and pupil dilations. LC spiking correlated with both Vm and pupil diameter changes, but on vastly different timescales (~0.2 s vs. ~2 s). Our data also show that the time derivative of pupil diameter, but not the absolute pupil size, is a good predictor of S1 Vm fluctuations. However, LC spiking can track fast Vm fluctuations better than either pupil or pupil’.

Individual trials in our detection task contained distinct events, including the tone that alerted mice of the trial start (‘Tone’), the whisker stimulus on Go trials (‘Stimulus’), and licks (‘Lick’), as well as other periods in which mice did not receive stimuli or make lick responses (‘Quiet’). For a more granular perspective on how LC spiking correlated with changes in Vm and pupil diameter, we computed LC spike-triggered averages separately in these different event windows (task epochs, Materials and methods).

While single LC spikes were associated with prominent changes in both cortical Vm and pupil diameter, we found that these associations strikingly depended on task epoch: Vm depolarization associated with an LC spike had the biggest response to tone/licking and almost no response during the quiet periods (Figure 4a). In contrast, pupil dilation associated with an LC spike had the biggest response to licking and almost no response to the tone (Figure 4b; this was true as well for pupil dilation after z-scoring, Figure 4—figure supplement 1). The pupil’ associated with an LC spike had an intermediate response to the tone (Figure 4c). In addition, peak pupil dilation, pupil’ and Vm depolarization appeared to have different dependencies on LC spike counts, with a roughly monotonic relationship between pupil and LC, and a much weaker dependence between Vm and LC (Figure 4—figure supplement 2). The relationship between Vm and LC spike count could partly but not fully be attributable to differences in the LC inter-spike intervals (Figure 4—figure supplement 2a). Thus, the correlations between LC spiking and Vm, and between LC spiking and pupil diameter, are non-stationary, even on the timescale of a few seconds. Importantly, these epoch dependencies were different for Vm and pupil – with the biggest response occurring to the tone for Vm and the smallest response occurring to the tone for pupil – suggesting that the correlations between LC activity and Vm and pupil each reflect distinct unmeasured underlying processes.

Figure 4 with 3 supplements see all
Correlations between LC spikes, S1 Vm, and pupil diameter depend on task epoch.

(a) Top: LC spike-triggered ∆Vm separated by task epoch: tone, stimulus, lick, and quiet. Bottom: Bar graphs of peak ∆Vm for each epoch. Dots indicate individual paired recordings. Repeated-measure ANOVA, F(3, 33)=9.2, p=1.4e-4, n = 12. Post hoc Tukey–Kramer tests revealed that peak ∆Vm in lick, stimulus, and tone epochs were not different from each other. Lick vs. Stim, p=1.00; Lick vs. Tone, p=0.76; Stim vs. Tone, p=0.94. Peak ∆Vm in quiet epochs was lower. Quiet vs. Lick, p=0.0059; Quiet vs. Stim, p=0.0038; Quiet vs. Tone, p=0.0041. (b) Top: LC spike-triggered ∆Pupil separated by task epoch. Bottom: Bar graphs of peak ∆Pupil for each epoch. Dots indicate individual paired recordings. Repeated-measure ANOVA, F(3, 57)=22.1, p=1.3e-9, n = 20. Post hoc Tukey–Kramer tests revealed that peak ∆Pupil in lick and stimulus epochs were larger than in tone and quiet epochs. Lick vs. Stim, p=0.10; Tone vs. Quiet, p=0.76; Lick vs. Tone, p=3.7e-7; Lick vs. Quiet, p=6.2e-4; Stim vs. Tone, p=1.1e-4; Stim vs. Quiet, p=0.0027. (c) Top: LC spike-triggered pupil’ separated by task epoch. Bottom: Bar graphs of peak pupil’ for each epoch. Dots indicate individual paired recordings. Repeated-measures ANOVA, F(3, 57)=35.3, p=4.9e-13, n = 20. Post hoc Tukey–Kramer tests revealed that peak pupil’ in lick and stimulus epochs were larger than in tone, and peak pupil’ in quiet epochs was the lowest. Lick vs. Stim, p=0.46; Tone vs. Quiet, p=0.0013; Lick vs. Tone, p=1.0e-4; Lick vs. Quiet, p=1.4e-8; Stim vs. Tone, p=0.0058; Stim vs. Quiet, p=6.7e-6.

Discussion

Tonic LC activity is thought to reflect attentive state (Usher et al., 1999; Aston-Jones and Cohen, 2005). We found that pre-stimulus baseline LC spiking predicted behavioral responses, suggesting that being in a hyperactive or distractable state underlies failed stimulus detection. We excluded trials where mice licked before stimulus onset from our analysis; thus, the higher baseline spiking in Miss trials was not caused by premature licking. However, it is possible that higher baseline LC activity was correlated with more whisking, which has been shown to lead to failed stimulus detection (Ollerenshaw et al., 2012; Kyriakatos et al., 2017). Overall, the effect was weak, possibly due to the use of an auditory cue that puts the mice in a more homogeneous arousal/attentive state. In other tasks without such alerting cues, task performance may have a stronger dependence on arousal and pre-stimulus LC activity.

Recent optogenetic and pharmacological work has shown that activating the LC–NE system enhances detection/discrimination sensitivity (Gelbard-Sagiv et al., 2018; Rodenkirch et al., 2019; McBurney-Lin et al., 2020). Thus, LC–NE acting on early sensory areas (i.e., primary sensory thalamus, primary sensory cortex) likely affects perceptual decision making (McBurney-Lin et al., 2019). We showed that S1 depolarizations immediately follow LC spiking, suggesting that LC activity at least partially contributes to the amount of depolarization in S1 neurons (Constantinople and Bruno, 2011; Polack et al., 2013). Given that sensory-evoked S1 responses predict the behavioral choices of the mice (Sachidhanandam et al., 2013; Yang et al., 2016; Kwon et al., 2016), our results suggest that the LC–S1 pathway is involved during this task. However, the effect could be mediated directly through LC innervation of S1 or indirectly through LC innervating other early somatosensory areas, such as the VPM thalamus (Rodenkirch et al., 2019). Future gain- and loss-of-function experiments are needed to determine causal roles of these distinct pathways to S1 and to determine how different LC activity patterns (e.g., tonic vs. phasic) affect sensory processing and task performance.

LC responded strongly to an auditory cue (tone) meant to alert the mice to the beginning of a trial. Such responses may be mediated via synaptic inputs to LC from the brainstem (e.g., nucleus paragigantocellularis) (Ennis and Aston-Jones, 1988; Aston-Jones et al., 1991; Van Bockstaele et al., 1993; Llorca-Torralba et al., 2016). While this tone carried no information about the presence of a tactile stimulus or reward on any given trial, and therefore was not associated with a particular movement response, it did inform the mice about the time when a tactile stimulus could occur (in our task the duration between the tone and stimulus onset was fixed). The robust LC spiking responses to this cue are therefore consistent with LC’s role in promoting alertness or preparedness to detect a weak stimulus (Berridge and Waterhouse, 2003; Aston-Jones and Cohen, 2005). We also found that LC responded to operant licking responses, which is consistent with earlier work showing that LC encoded overt decision execution (Kalwani et al., 2014).

Our data show that while LC spiking and pupil diameter correlate well at long timescales, and both can predict changes in cortical dynamics, LC does so an order of magnitude faster. Moreover, the correlation between pupil and Vm is much more heterogeneous than between LC and Vm. In support of previous studies, our results suggest that compared with change in the absolute size of pupil diameter, its time derivative is a better predictor of cortical states (Reimer et al., 2014; Reimer et al., 2016). Importantly, the relationships between LC activity, S1 Vm, and pupil depended on task epoch. Because these epochs changed on the timescale of a few seconds, our data imply that pupil diameter can be dissociated from LC spiking and cannot be used as a stationary index of LC activity. However, comparing across repeats of similar epochs should yield a more accurate prediction of LC spiking by pupil diameter. That is, in attempting to use pupil diameter as a proxy for LC spiking, our data suggest it would be useful to separately normalize distinct task epochs. Future work should examine the LC–pupil relationship using fine-scale analyses that consider behavioral states at a granular level specific to individual tasks.

Pupil size changes have been linked to activity in multiple brain areas and neuromodulatory systems (Joshi et al., 2016; Reimer et al., 2016; Cazettes et al., 2021), and different pupil response profiles reflect different cognitive processes (Schriver et al., 2020). Therefore, it is possible that the pupil exhibits dynamic coupling with different underlying brain circuits in a cognitive process (behavioral epoch)-dependent way. In addition, a recent study showed that pupil responses to dorsal raphe stimulation exhibited task uncertainty-dependent variations (Cazettes et al., 2021). Thus, it is also possible that other modulatory systems (e.g., serotonergic and cholinergic, Berridge and Waterhouse, 2003) modulate the pupil–LC coupling in a dynamic manner.

Materials and methods

Key resources table
Reagent type (species)
or resource
DesignationSource or referenceIdentifiersAdditional information
Strain, strain background (M. musculus)DBH-CreMMRRCCat# 036778-UCD, RRID:MMRRC_036778-UCD
Strain, strain background (M. musculus)Ai32Jackson LaboratoryCat#: JAX:012569, RRID:IMSR_JAX:012569
Software, algorithmBControlPrinceton Universityhttps://brodylabwiki.princeton.edu/bcontrol
Software, algorithmWaveSurferHHMI Janeliahttp://wavesurfer.janelia.org/
Software, algorithmMATLABMathWorksRRID:SCR_001622
Software, algorithmStreamPixNorpixRRID:SCR_015773
Software, algorithmAdobe IllustratorAdobeRRID:SCR_010279
OtherHigh-speed CMOS cameraPhotonFocusDR1-D1312-200-G2-8
OtherTelecentric lensEdmund OpticsCat#: 55–349
OtherPipette glassWarner InstrumentsCat#: 640792
OtherTetrode driveCohen et al., 2012N/A
AntibodyAnti-TH primary antibody (rabbit, polyclonal)Thermo-FisherCat#: OPA1-04050, RRID: AB_325653(1:1000)
AntibodySecondary antibody (goat, polyclonal)Thermo-FisherCat#: A-11008, RRID:AB_2534079(1:500)

All procedures were performed in accordance with protocols approved by the Johns Hopkins University Animal Care and Use Committee. Mice were DBH-Cre (B6.FVB(Cg)-Tg(Dbh-cre) KH212Gsat/Mmucd, 036778-UCD, MMRRC) and Ai32 (RCL-ChR2(H134R)/EYFP, 012569, JAX), singly housed in a vivarium with reverse light–dark cycle (12 hr each phase). Male and female mice of 6–12 weeks were implanted with titanium head posts as described previously (Yang et al., 2016). After recovery, mice were trained to perform a Go/NoGo single whisker detection task as described previously (Yang et al., 2016). Behavioral apparatus was controlled by BControl (C. Brody, Princeton University). A custom ‘lickport’ was placed within reach of the mouse’s tongue and used both to deliver water rewards and to record the time of each lick, determined via measurement of an electrical conductance change caused by contact between tongue and lickport. On go trials, a single whisker was deflected for 0.5 s with a 40 Hz sinusoidal deflection (rostral to caudal, peak angular speed ~800 deg/s). A ‘response window’ was defined as 0.2–2 s after the time of whisker stimulus onset for go trials or the time that the whisker stimulus would have onset on no-go trials. A ‘hit’ trial occurred when mice licked the lickport within the response window, and a drop of water was delivered. On go trials, if mice did not lick within the 1.8 s response window, the trial was scored as a ‘miss’ and no reward or punishment was delivered. Go trials were randomly mixed with no-go trials, in which the whisker was not deflected. No more than three consecutive trials of the same type were allowed. On no-go trials, if mice licked within the response window, it was scored as a ‘false alarm’, and mice were punished with a 3–5 s time-out. If mice licked during the time-out, an additional time-out was triggered. A ‘correct rejection’ occurred when mice withheld licking during the response window. Correct rejections were not rewarded. A 0.1 s auditory cue (8 kHz tone, ~80 dB SPL) was introduced starting 1 s before stimulus onset. During all sessions, ambient white noise (cut off at 40 kHz, ~80 dB SPL) was played through a separate speaker to mask any other potential auditory cues associated with movement of the piezo stimulator. Trials where mice made ‘premature’ licking during the period between tone and 0.1 s after whisker stimulation onset were excluded from further analysis. To align LC activity and pupil traces to the ‘first lick’ (Figure 2f), we used the first lick occurring in the response window. A total of 46 recordings from eight mice are reported (mean hit rate: 0.52 ± 0.03; false alarm rate: 0.11 ± 0.02).

Custom microdrives with eight tetrodes and an optic fiber (Cohen et al., 2012, 0.39 NA, 200 µm core) were built to make extracellular recordings from LC neurons. Each tetrode comprised four nichrome wires (100–300 kΩ). A ~1 mm diameter craniotomy was made (centered at −5.2 mm caudal and 0.85 mm lateral relative to bregma) for implanting the tetrodes to a depth of 2.7 mm relative to the brain surface. The microdrive was advanced in steps of ~100 µm each day until reaching LC, identified by optogenetic tagging of DBH+ neurons expressing ChR2, tail pinch response, wide extracellular spike waveforms, and post hoc electrolytic lesions. Broadband voltage traces were acquired at 30 kHz (Intan Technologies) and filtered between 0.1 and 10 kHz. Signals were then bandpass filtered between 300 and 6000 Hz, and spikes were detected using a threshold of 4–6 standard deviations. The timestamp of the peak of each detected spike, as well as a 1 ms waveform centered at the peak, was extracted from each channel for offline spike sorting using MClust (Redish, 2014). At the conclusion of the experiments, brains were perfused with PBS followed by 4% PFA, post-fixed overnight, then cut into 100 μm coronal sections, and stained with anti-tyrosine hydroxylase antibody (Thermo-Fisher OPA1-04050).

Pupil video was acquired at 50 Hz using a PhotonFocus camera and StreamPix five software. Light from a 940 nm LED was passed through a condenser lens and directed to the right eye, reflected off a mirror, and directed into a 0.25× telecentric lens. WaveSurfer (https://www.janelia.org/open-science/wavesurfer) triggered individual camera frames synchronized with electrophysiological recordings.

In a subset of animals, we performed simultaneous intracellular current clamp (whole-cell) recordings in conjunction with LC recording and/or pupil tracking during behavior. A craniotomy over the C2 barrel was made based on intrinsic signal imaging (Yang et al., 2016). In some cases, we also made craniotomies over nearby barrels based on the known somatotopy of S1 (Welker and Woolsey, 1974; Wilson et al., 2000) to increase yield. Whole-cell recording procedures, quality control, and data processing were performed as described previously (Yang et al., 2016). Briefly, borosilicate glass pipettes (1.5 mm OD, 0.86 mm ID; Harvard Apparatus) were pulled (P-97, Sutter) to have a long shank and were 4–7 MΩ when filled with solution containing (in mM): 135 potassium gluconate, 4 KCl, 10 sodium phos-phocreatine, 4 ATP magnesium salt, 0.3 GTP sodium salt hydrate, 10 HEPES, 3 mg/ml biocytin (pH 7.3 with KOH). Electrophysiological signals (Multiclamp 700B, Molecular Devices) were filtered at 10 kHz and acquired at 20 kHz using Ephus or WaveSurfer.

For Figure 2d, LC responses to the tone were calculated using a 300 ms window starting at tone onset, and LC responses to Go were calculated using a 300 ms window starting 200 ms after stimulus onset to capture peak responses. These estimates were based on LC response profiles in Figure 2c. Pupil responses to the tone were calculated using a 1 s window starting 1 s after tone onset. This estimate was primarily based on the pupil response profile during CR trials (e.g., Figure 2a,c, indicated by th gray bar), where there was no whisker stimulus or licking response. Pupil responses to Go (licking) were calculated using a 1 s window starting 1.5 s after stimulus onset (e.g., False Alarm trials in Figure 2a,c, indicated by the black bar). Based on the temporal profiles of pupil diameter in different trial types shown in Figure 2a,c, and that the whisker stimulus started 1 s after tone onset, pupil responses to tone and Go can be segregated. These estimates were consistent with the results showing that pupil dilated 1–2 s after LC spikes (Figures 2b and 3d-f).

For Figure 2e, pre-stimulus LC baseline activity was calculated using a 500 ms window ending 50 ms before stimulus onset. Post-stimulus activity was calculated using a 200 ms window starting 20 ms after stimulus onset, before licking responses (Yang et al., 2016). Choice probabilities were computed as described previously (Yang et al., 2016).

To compute lick-aligned changes in LC spiking and pupil diameter, we only used licks that occurred at least 0.5 s after the previous lick. To compute LC spike-triggered S1 Vm and pupil, we only used LC spikes that occurred at least 0.5 s after the previous spike. For STA analysis, peak ∆Vm, ∆Pupil, or the time derivative of Pupil (Pupil’) were defined as the largest positive or negative value within the observed window (±1 s or ±10 s, respectively).

For cross-correlogram analysis, each LC spike train was convolved with a 400 ms wide Gaussian kernel (results hold for 200 ms kernel, data not shown). Peak correlation coefficients were defined as the largest positive or negative value within the observed window (±1 s or ±10 s). To examine how well LC spiking and pupil diameter could predict cortical Vm fluctuations at different timescales (Figure 3l), Vm was high-pass filtered at 0, 0.2, 0.4, 0.6, 0.8, 1, 2, 3, 4, and 5 Hz separately. Cross-correlogram analysis between the filtered Vm and LC (pupil) activities was then performed as described above, and largest absolute values of peak correlation coefficients were taken.

Task epochs were defined as follows: ‘Tone’ epochs: −0.2 s to 0.3 s with respect to tone onset (we also tested 0 to 0.5 s, Figure 4—figure supplement 3); ‘Stimulus’ epochs: −0.2 s to 0.3 s with respect to stimulus onset (i.e., only on trials with whisker stimulation); ‘Licking’ epochs: −0.2 s to 0.3 s with respect to licks that occurred at least 0.5 s after the previous lick; ‘Quiet’ epochs: non-overlapping 0.5 s segments excluding the three types of epoch defined previously during the entire session.

Thirty-nine LC–pupil pairs from seven mice were included in Figure 2b, including single- and multi-units, with and without S1 recordings. For the rest of Figure 2, LC analysis included 43 recordings from eight mice, each with at least four Hit and four Miss trials. Among those, 36 were with pupil recordings and were used for pupil analysis. Twelve pairs of S1 whole-cell and LC single-unit recordings from six mice were included in Figures 3a–c and 4a, of which seven were with pupil recordings and included in Figure 3d–f. Nineteen S1–pupil recordings from three mice were included in Figure 3g–l. Twenty pairs of LC SU and pupil recordings from five mice were included in Figure 4b,c, with and without S1 recordings.

Data were reported as mean ± s.e.m. unless otherwise noted. Statistical tests were by two-tailed Wilcoxon signed-rank unless otherwise noted. We did not use statistical methods to predetermine sample sizes. Sample sizes are similar to those reported in the field. We assigned mice to experimental groups arbitrarily, without randomization or blinding.

References

Decision letter

  1. Joshua I Gold
    Senior Editor; University of Pennsylvania, United States
  2. Jacob Reimer
    Reviewing Editor
  3. Qi Wang
    Reviewer; Columbia University, United States
  4. Carl CH Petersen
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This paper will be of interest to neuroscientists studying the effect of brain state on sensory processing and behavior and the effects of noradrenergic input from the locus coeruleus on cortex. By combining multiple types of recordings in the same behaving animal, the authors are able to build on and extend previous work that has demonstrated some of these effects in separate experiments.

Decision letter after peer review:

Thank you for submitting your article "Locus coeruleus spiking differently correlates with somatosensory cortex activity and pupil diameter" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Joshua Gold as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Qi Wang (Reviewer #2); Carl C H Petersen (Reviewer #3).

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

As the editors have judged that your manuscript is of significant interest, but as described below that additional analyses are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

Yang et al., make careful measurements of the spiking activity of optogenetically-identified locus coeruleus (LC) neurons during a whisker detection task in head-restrained mice together with measurement of neuronal membrane potential in the primary somatosensory cortex (S1) and pupil diameter. A substantial body of previous work has linked LC activity with cortical intracellular membrane potential (Vm) depolarization, pupil dilation, and behavioral performance, but simultaneous measurements of all of three of these correlates in the same behaving animal – as the authors perform here – are rare and technically challenging, and these experiments offer the potential to study features of timing and trial-to-trial variability that might otherwise be inaccessible. The authors focus on two issues in particular: First, the relative ability of pupil dilation and "ground truth" recordings of LC firing to predict behavioral performance and membrane depolarization in sensory cortex. Perhaps not surprisingly, both pupil and LC are informative, but the time scale of pupil correlations with behavior and intracellular membrane potentials is an order of magnitude longer (1s-10s of seconds), compared to LC firing. Second, the authors discovered that the impact of LC spikes upon S1 membrane potential and pupil diameter differed across behavioural epochs. Given that pupil diameter is often interpreted as a direct correlate of LC activity, the results may be of broad interest. There are many potential factors that likely contribute to this heterogeneity across different task epochs that the authors do not explore, including other neuromodulator systems like acetylcholine, and population-level changes in cortical activity, but reviewers agreed that the high quality of the experimental methodology make the data strong.

Essential revisions:

1) Given the qualitative difference between the tonic and phasic modes in the LC, it would be helpful to have a plot like Figure 4—figure supplement 2, but conditioned on different inter-spike intervals. Specifically it would be interesting to know whether the larger effect on membrane potential during the trial epochs compared to quiet periods might be due to differences in the LC ISI distributions in these epochs (i.e. presence or absence of bursting rather than total spike counts).

2) Some justification or analysis of the specific time windows chosen for analysis of the pupil in Figure 2A would be useful. It is unclear why tone epochs were defined as [-0.2 0.3s] around tone onset. LC activity should not be affected in the period prior to tone as animals didn't know when the tone would occur.

3) Prestimulus whisking was found to be an important determinant of hit vs miss trials in a related whisker-detection paradigm (Kyriakatos et al., 2017). Does LC activity correlate with whisking, as well as licking? Here, the authors report that pre-whisker stimulus LC spiking differed across hit and miss trials (with lower spike rate in hit trials), suggesting the LC activity could participate in determining hit vs miss, but perhaps it is an indirect correlation because of whisking. This might be good to discuss, and perhaps analyze if whisker movements were tracked in these experiments. The authors might also discuss potential future optogenetic and pharmacological tests of the hypothesis that LC activity contributes to task performance.

4) The authors should give a more complete methods description. In general, I am not sure why the authors chose to write in such a short format, and they might expand on some points. How might the auditory stimulus drive LC spiking? How might LC spiking affect membrane potential in S1?

5) It is very intriguing that tone prior to stimulus presentation evoked strong LC responses but no changes in pupil size. The underlying mechanisms may be complicated, but some further discussion would be helpful. There may be an interesting link here with a recent work where task-evoked pupil dilation was decomposed to contributions of different cognitive processes, which may be differently modulated by the LC-NE system (Schriver et al., 2020).

6) In Figure 1E, the minimal pupil size seemed already fairly large. Was this panel a poor example or animals were already in a high arousal state throughout sessions?

7) The manuscript reported changes in raw pupil diameter in Figure 2, Figure 3, Figure 4. Given the large dynamic range of pupil fluctuations reported in literature, would Z-scored pupil size be a better way to lump data across animals/sessions?

8) It would be very interesting to know if there were any differences between regular spiking neurons and fast spiking neuron in terms of their correlation with LC spiking activity.

https://doi.org/10.7554/eLife.64327.sa1

Author response

Essential revisions:

1) Given the qualitative difference between the tonic and phasic modes in the LC, it would be helpful to have a plot like Figure 4—figure supplement 2, but conditioned on different inter-spike intervals. Specifically it would be interesting to know whether the larger effect on membrane potential during the trial epochs compared to quiet periods might be due to differences in the LC ISI distributions in these epochs (i.e. presence or absence of bursting rather than total spike counts).

We have done this analysis and now show the results as new panels in Figure 4—figure supplement 2. The results suggest that the ISI distribution may contribute, but cannot fully account for, the larger effect on Vm during trial epochs compared with quiet periods. Specifically, at the lowest spike count numbers, the ISIs for Tone/Lick epochs were lower than Quiet epochs, suggesting that there could be a partial role for ISI. However, although Quiet and Stimulus epochs differ for the Vm response across LC spike counts (Figure 4—figure supplement 2A, left panel), the ISIs are similar (Figure 4—figure supplement 2A, middle panel).

We now refer to this in the text:

“The relationship between Vm and LC spike count could partly but not fully be attributable to differences in the LC inter-spike intervals (Figure 4—figure supplement 2A).”

2) Some justification or analysis of the specific time windows chosen for analysis of the pupil in Figure 2A would be useful. It is unclear why tone epochs were defined as [-0.2 0.3s] around tone onset. LC activity should not be affected in the period prior to tone as animals didn't know when the tone would occur.

We initially chose a [-0.2 0.3 s] window for the Lick epochs, because LC activity could in principle either precede or follow lick movements. We then chose the same window for the other epochs for consistency. However, as noted by the Reviewers, this window need not precede the tone. We have therefore performed new analyses for the Tone epoch using a [0 0.5 s] window, and found that this did not significantly affect our conclusions. We show this new analysis in new Figure 4—figure supplement 3 and reference it in the Materials and methods:

“Task epochs were defined as: “Tone” epochs: -0.2 s to 0.3 s with respect to tone onset (we also tested 0 to 0.5 s, Figure 4—figure supplement 3)”

3) Prestimulus whisking was found to be an important determinant of hit vs miss trials in a related whisker-detection paradigm (Kyriakatos et al., 2017). Does LC activity correlate with whisking, as well as licking? Here, the authors report that pre-whisker stimulus LC spiking differed across hit and miss trials (with lower spike rate in hit trials), suggesting the LC activity could participate in determining hit vs miss, but perhaps it is an indirect correlation because of whisking. This might be good to discuss, and perhaps analyze if whisker movements were tracked in these experiments.

We do not have whisker tracking data for these experiments. However, we now discuss the possible link between whisking, LC activity and trial outcome:

“We found that pre-stimulus baseline LC spiking predicted behavioral responses, suggesting that being in a hyperactive or distractable state underlies failed stimulus detection. We excluded trials where mice licked before stimulus onset from our analysis, thus the higher baseline spiking in Miss trials was not caused by premature licking. However, it is possible that higher baseline LC activity was correlated with more whisking, which has been shown to lead to failed stimulus detection25,26.”

The authors might also discuss potential future optogenetic and pharmacological tests of the hypothesis that LC activity contributes to task performance.

We have added to the Discussion as follows:

“Recent optogenetic and pharmacologic work has shown that activating the LC-NE system enhances detection/discrimination sensitivity1,27,28.”

And also:

“Future gain- and loss-of-function experiments are needed to determine causal roles of these distinct pathways to S1, and to determine how different LC activity patterns (i.e., tonic vs. phasic) affect sensory processing and task performance.”

4) The authors should give a more complete methods description. In general, I am not sure why the authors chose to write in such a short format, and they might expand on some points. How might the auditory stimulus drive LC spiking? How might LC spiking affect membrane potential in S1?

We have substantially expanded the Materials and methods section and now report several details here instead of only citing our past work. We agree this improves clarity.

We have added to the Discussion to address the other points. Specifically, we now write:

“We showed that S1 depolarizations immediately follow LC spiking, suggesting that LC activity at least partially contributes to the amount of depolarization in S1 neurons4,5. Given that sensory-evoked S1 responses predict the behavioral choices of the mice21,23,30, our results suggest that the LC-S1 pathway is involved during this task. However, the effect could be mediated directly through LC innervation of S1, or indirectly through LC innervating other early somatosensory areas, such as the VPM thalamus1. Future gain- and loss-of-function experiments are needed to determine causal roles of these distinct pathways to S1, and to determine how different LC activity patterns (i.e., tonic vs. phasic) affect sensory processing and task performance.”

We also now write:

“LC responded strongly to an auditory cue (tone) meant to alert the mice to the beginning of a trial. Such responses may be mediated via synaptic inputs to LC from the brainstem (e.g. nucleus paragigantocellularis)31–34.”

5) It is very intriguing that tone prior to stimulus presentation evoked strong LC responses but no changes in pupil size. The underlying mechanisms may be complicated, but some further discussion would be helpful. There may be an interesting link here with a recent work where task-evoked pupil dilation was decomposed to contributions of different cognitive processes, which may be differently modulated by the LC-NE system (Schriver et al., 2020).

We have added a paragraph to the Discussion to address these points. Specifically, we now write:

“Pupil size changes have been linked to activity in multiple brain areas and neuromodulatory systems8,10,18, and different pupil response profiles reflect different cognitive processes37. Therefore, it is possible that the pupil exhibits dynamic coupling with different underlying brain circuits in a cognitive process- (behavioral epoch-) dependent way. In addition, a recent study showed that pupil responses to dorsal raphe stimulation exhibited task uncertainty-dependent variations18. Thus, it is also possible that other modulatory systems (e.g., serotonergic and cholinergic)30 modulate the pupil-LC coupling in a dynamic manner.”

6) In Figure 1E, the minimal pupil size seemed already fairly large. Was this panel a poor example or animals were already in a high arousal state throughout sessions?

We now plot a histogram of median pupil size from all sessions, with the value of the example trace indicated (new Figure 1—figure supplement 1). This shows that the example trace is typical, and that the pupil in the example trace still has dynamic range for further dilation.

We are unable to use baseline pupil diameter to assess overall levels of arousal across mice and sessions, because we used controlled ambient light from an LED to put the pupil in a non-saturated state. However, our use of an auditory cue was intended to put the mice in as homogenous an arousal state as possible at the beginning of each trial. Our Discussion now refers to this fact in the following paragraph:

“However, it is possible that higher baseline LC activity was correlated with more whisking, which has been shown to lead to failed stimulus detection25,26. Overall, the effect was weak, possibly due to the use of an auditory cue that puts the mice in a more homogeneous arousal/attentive state. In other tasks without such alerting cues, task performance may have a stronger dependence on arousal and pre-stimulus LC activity.”

7) The manuscript reported changes in raw pupil diameter in Figure 2, Figure 3, Figure 4. Given the large dynamic range of pupil fluctuations reported in literature, would Z-scored pupil size be a better way to lump data across animals/sessions?

Several of our analyses will not be affected by z-scoring. These include the cross-correlation analyses (Figure 2B, Figure 3G-L, Figure 3—figure supplement 2A-B), the paired-observation analyses (Figure 2D and Figure 2—figure supplement 2A), and the analyses of peak timings shown in our histograms (Figure 3F and Figure 3—figure supplement 3B). For the remaining analyses, Z-scoring will scale the values for an individual recording, and potentially affect the degree to which the different recordings will contribute to averages. We have confirmed that z-scoring does not affect our conclusions for these remaining analyses. For the most critical analysis, that of Figure 4B, we have added a new Figure 4—figure supplement 1 to show a version of Figure 4B obtained after z-scoring the pupil data. The results are nearly identical. We now refer to this new Figure 4—figure supplement 1 as follows:

“In contrast, pupil dilation associated with an LC spike had the biggest response to licking and almost no response to the tone (Figure 4B; this was true as well for pupil dilation after z-scoring, Figure 4—figure supplement 1).”

8) It would be very interesting to know if there were any differences between regular spiking neurons and fast spiking neuron in terms of their correlation with LC spiking activity.

We agree this is an interesting question. However, we do not have identifiable fast-spiking neurons in our dataset of recordings.

https://doi.org/10.7554/eLife.64327.sa2

Article and author information

Author details

  1. Hongdian Yang

    Department of Molecular, Cell and Systems Biology, University of California, Riverside, Riverside, United States
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    hongdian@ucr.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5203-9519
  2. Bilal A Bari

    Department of Neuroscience, Brain Science Institute, and Kavli Neuroscience Discovery Institute, Johns Hopkins School of Medicine, Baltimore, United States
    Contribution
    Formal analysis, Methodology
    Competing interests
    No competing interests declared
  3. Jeremiah Y Cohen

    Department of Neuroscience, Brain Science Institute, and Kavli Neuroscience Discovery Institute, Johns Hopkins School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Resources, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4768-7124
  4. Daniel H O'Connor

    Department of Neuroscience, Brain Science Institute, and Kavli Neuroscience Discovery Institute, Johns Hopkins School of Medicine, Baltimore, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    dan.oconnor@jhmi.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9193-6714

Funding

National Institute of Neurological Disorders and Stroke (R01NS089652)

  • Daniel H O'Connor

National Institute of Neurological Disorders and Stroke (1R01NS104834-01)

  • Jeremiah Y Cohen
  • Daniel H O'Connor

National Institute of Neurological Disorders and Stroke (1R01NS107355)

  • Hongdian Yang

National Institute of Neurological Disorders and Stroke (1R01NS112200)

  • Hongdian Yang

National Institute of Mental Health (F30MH110084)

  • Bilal A Bari

Klingenstein-Simons (Fellowship Awards in Neuroscience)

  • Hongdian Yang

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

Acknowledgements

We thank Edward Zagha for comments on the manuscript and Dwight E Bergles for DBH-Cre mice. This work was supported by UCR startup (HY), Klingenstein-Simons Fellowship Awards in Neuroscience (HY), NIH grants F30MH110084 (BAB), 1R01NS107355 (HY), 1R01NS112200 (HY), R01NS089652 (DHO), 1R01NS104834-01 (DHO, JYC).

Ethics

Animal experimentation: All procedures were performed in accordance with protocols (#MO18M187) approved by the Johns Hopkins University Animal Care and Use Committee.

Senior Editor

  1. Joshua I Gold, University of Pennsylvania, United States

Reviewing Editor

  1. Jacob Reimer

Reviewers

  1. Qi Wang, Columbia University, United States
  2. Carl CH Petersen

Publication history

  1. Received: October 26, 2020
  2. Accepted: March 2, 2021
  3. Version of Record published: March 15, 2021 (version 1)

Copyright

© 2021, Yang 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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