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Neural antecedents of self-initiated actions in secondary motor cortex

Nature Neuroscience volume 17, pages 1574–1582 (2014)Cite this article

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Abstract

The neural origins of spontaneous or self-initiated actions are not well understood and their interpretation is controversial. To address these issues, we used a task in which rats decide when to abort waiting for a delayed tone. We recorded neurons in the secondary motor cortex (M2) and interpreted our findings in light of an integration-to-bound decision model. A first population of M2 neurons ramped to a constant threshold at rates proportional to waiting time, strongly resembling integrator output. A second population, which we propose provide input to the integrator, fired in sequences and showed trial-to-trial rate fluctuations correlated with waiting times. An integration model fit to these data also quantitatively predicted the observed inter-neuronal correlations. Together, these results reinforce the generality of the integration-to-bound model of decision-making. These models identify the initial intention to act as the moment of threshold crossing while explaining how antecedent subthreshold neural activity can influence an action without implying a decision.

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Figure 1: The waiting task and the behavioral results.
Figure 2: Difference in movement times in impatient and patient trials.
Figure 3: Ramp-to-threshold type predictive activity.
Figure 4: An M2 neuron with transient predictive activity.
Figure 5: Population data of predictive activity.
Figure 6: Action specificity of predictive activity.
Figure 7: Action specificity of ramp-to-threshold activity.
Figure 8: Integrator model.

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Acknowledgements

We thank the members of the Mainen laboratory for discussion, M. Terrelonge for assistance with recording experiments, B. Burbach and M. Vinhas for technical assistance, E. Lottem for daily discussions, and C. Feierstein, H. Shteingart, Y. Loewenstein, J. Erlich, J. Paton and B. Atallah for helpful comments on the manuscript. This work was supported by the Uehara Memorial Foundation (M.M.), Fundação Bial (127/08, M.M.), Fundação para a Ciência e a Tecnologia SFRH/BPD/46314/2008, M.M.; SFRH/BD/33274/2007, M.I.V.; SFRH/BD/32947/2006, G.M.C.), European Research Council Advanced Investigator Grant (250334, Z.F.M.) and Champalimaud Foundation (Z.F.M.). G.M.C. was supported by Fundação para a Ciência e a Tecnologia, as part of the BEB/CNC PhD programme.

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Authors and Affiliations

  1. Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal

    Masayoshi Murakami, M Inês Vicente, Gil M Costa & Zachary F Mainen

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  1. Masayoshi Murakami
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  2. M Inês Vicente
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  3. Gil M Costa
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Contributions

M.M. and Z.F.M. designed the experiments, analyses and models and wrote the manuscript. M.M. conducted the experiments with assistance from M.I.V. and G.M.C. M.M. analyzed the data and implemented the model.

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Correspondence to Zachary F Mainen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Histograms of response times to tone 2 of all the rats.

Rats used for electrophysiology are indicated by (E) after a rat name on top. Rats without a significant peak in a response time histogram are indicated by an asterisk after a rat name. The same format as in Figure 1f.

Supplementary Figure 2 M2 neurons were activated in different phases of the task.

(a) An example neuron which was activated at the poke-in period (from 0.2 s before to 0.2 s after the poke-in). The left panel shows the activity aligned to the poke-in, the middle panel aligned to the poke-out, the right panel aligned to the water delivery. Raster plots (top) represent activity of the neuron around the waiting period, with each row corresponding to a single trial and each black tick to a single spike. The impatient trials in pink background and the patient trials in blue background. Trials are chronologically ordered from top to bottom within each type of trials. Color ticks represent Tone 1 (light green), Tone 2 (dark green), poke-in (white), poke-out (white), poke-in into the reward port (light blue) and water delivery (blue). Perievent time histograms (PETHs) at the bottom represent activity in the impatient trials (red) and the patient trials (blue), smoothed with a Gaussian filter (s.d. = 50 ms). (b) A neuron which was activated at the delay period (from 0.4 s after the poke-in to 0.4 s before the poke-out). (c) A neuron which was activated at the poke-out period (from 0.2 s before to 0.2 s after the poke-out). (d) A neuron which was activated at the water-poke-in period (from 0.2 s before to 0.2 s after the water-poke-in). (e) A neuron which was activated at water delivery (from 0.1 s to 0.5 s after the water-delivery). (f) Fraction of neurons activated (open bar) or suppressed (filled bar) at each period (Mean ± SEM across rats) (Wilcoxon signed-rank test, P < 0.05, comparing the firing rate at the period of interest with that at 0.4 s time window randomly chosen in each trial).

Supplementary Figure 3 Flow of the analysis for waiting time predictive neurons.

(a) Flow of the ramp-to-threshold neuron analysis. Numbers in the parenthesis represent number of neurons in each category. The analysis classified 27 neurons as ramp-to-threshold neurons (0,4,4,6,7,4,1,1 neurons from each rat). (b) Flow of the transient neuron analysis. The analysis classified 64 neurons as transient neurons (3,3,0,6,22,27,2,1 neurons from each rat). The details of each analysis are described in Online Methods.

Supplementary Figure 4 Schematic drawings of hypothetical activity of ramp-to-threshold type predictive neurons.

A difference in times to cross the threshold in different waiting time trials could result from identical rate but different onset time of ramping activity, which would be expected from a stereotypical movement-related activity27. In contrast, for a neuron reflecting the output of a neural integrator, the difference in the threshold crossing times should arise from the different rates of ramping. (a) Perievent time histograms of a hypothetical “preparatory-type” ramp-to-threshold neuron. The format is the same as in Figure 3. Two horizontal lines indicate a high threshold line and a low threshold line. (b) Perievent time histograms of a hypothetical “movement-type” ramp-to-threshold neuron. The format is the same as in Figure 3. (c) Relationship between the waiting time and the ramp rate for the preparatory-type neuron. The ramp rate was measured between the high and low threshold lines indicated in a. The logarithm of ramp rate is negatively correlated with the logarithm of waiting time. (d) Relationship between the waiting time and the ramp rate for the movement-type neuron. The ramp rate is constant and independent of the waiting time.

Supplementary Figure 5 Choice of waiting action at block switches between nose-poke waiting blocks and lever-press waiting blocks.

(a) An example session in which a rat performed both nose-poke and lever-press waiting task. The x-axis indicates number of trials and y-axis the waiting time in each trial. The waiting time of the nose-poke trials is indicated in a positive direction in the y-axis and a lever-press waiting time in a negative direction. The lever-press block is indicated with a light yellow background. Gray circle indicates the short poke trials, red the impatient trials, and blue the patient trials. (b) Choice between nose-poke waiting and lever-press waiting around block switches. Fraction of trials in which rats chose to perform nose-poke waiting is plotted as a function of trials (20 trials before switch and 40 trials after switch). The left panel shows the choice of waiting action at the block switch from the nose-poke block to the lever-press block. The right panel shows the choice of waiting action at the block switch from the lever-press block to the nose-poke block. All the recording sessions from 3 rats are combined. The gray shade indicates 95% confidence interval calculated with binomial fitting. The lever-press block is indicated by light yellow background. The rats quickly adapted to the block switch within a few trials.

Supplementary Figure 6 Transient correlation analysis focused on a period before poke in in the current trial and during intertrial interval (ITI).

Ninety-one neurons are selected with the same transient correlation analysis as in the main result, but focused on different time bins (Eight non-overlapping 1.5s time bins, including 2 bins from a 3s before poke-in in the current trial and 2 bins from the first 3s ITI period of the previous trials (1 – 3 trials back)). An ITI period of 3 trial back is shown in (a), 2 trials back in (b), 1 trial back in (c) and a pre-poke-in period of the current trial in (d). P-value shown here are calculated with 0.4s overlapping time bins with 0.02s time steps for each neuron. The color code is the same as in Figure 5c. Bonferroni correction for multiple comparisons were used for selecting neurons (P < 0.05), but P-value shown here was not corrected. Note that the waiting time predictive activity was already present at 2 – 3 seconds before the poke-in and during ITI period after the previous trial, but much weaker in 2 trials back and 3 trials back.

Supplementary Figure 7 Difference in activities in the impatient and patient trials of the predictive neurons.

Because Tone 2 terminated the waiting of rats in patient trials, the rats’ willingness to wait was presumably longer than the actual waiting time. This gives rise to two additional predictions that we were able to test. First, transient waiting-time predictive neurons that fired at a higher rate in long waiting impatient trials should fire even more vigorously in patient trials, while neurons which fired less in long waiting trials should fire even less in the patient trials. Second, in patient trials, the ramp-to-threshold neurons should not reach the threshold firing rate for leaving before Tone 2. (a) A scatter plot showing the relationship between the correlation between firing rates and waiting times in the impatient trials and impatient/patient selectivity index. We calculated an impatient/patient selectivity index using the time window(s) with significant predictive activity in the correlation analysis. Because we wished to compare activities in impatient and patient trials without a contribution of difference in waiting time distribution, we selected subset of impatient and patient trials to match the waiting time distributions from two trial types, as described for the analysis of the movement time. The impatient/patient selectivity index was defined as the difference in a mean firing rate in the impatient and patient trials normalized by the sum of the mean firing rates. The value ranges from –1 to 1; 1 indicates a neuron selective for the impatient trials. Each circle represents a time bin with significant correlation between firing rate and waiting time in the impatient trials. A neuron which has multiple time bins with significant correlation is represented more than once in the plot. Filled circles indicate time bins with significant impatient/patient selectivity index (Wilcoxon signed-rank test, P < 0.05). Open circles indicate time bins with non-significant selectivity index. (b) Distribution of the impatient/patient selectivity index for all time bins with significant positive correlation between firing rates and waiting times in impatient trials. Arrow indicates the median selectivity index. The distribution is significantly shifted toward negative (Wilcoxon signed-rank test, P < 0.01) (c) The same as in b but for time bins with negative correlation. The distribution is significantly shifted toward positive (Wilcoxon signed-rank test, P < 0.0001). Analysis of patient trials for the ramp-to-threshold type neurons (d,e). (d) Activity of an example ramp-to-threshold neuron (the same neuron as in Figure 3) in impatient and patient trials aligned to poke-out. A gray shaded area indicates the poke-out time window (from –500 ms to –250 ms from poke-out) used for the population analysis in e. A green dashed line indicates a range of possible Tone 2 onset time in the patient trials. Activity was higher in the impatient trials than in patient trials in the analysis window (P < 0.05, Wilcoxon signed-rank test). (e) Distribution of impatient/patient selectivity index for ramp-to-threshold neurons. Patient trials with more than 250 ms reaction time were excluded to prevent contamination of Tone 2 response in the analysis time window. Patient trials with less than 60 ms response time were also excluded, because probably those are trials in which Tone 2 presentation happened to coincide with the time the rat was about to leave, estimated from the response time distribution pooled across rats (data not shown). After exclusion of those trials, the procedure for matching the waiting time distribution of impatient and patient trials was performed, as described before. Ramp-up and ramp-down neurons were pooled together. The sign of impatient/patient selectivity index for the ramp-down neurons was flipped. A red histogram indicates neurons with significant correlation between ramp rates and waiting times. A black histogram indicates neurons without significant correlation. While a distribution of the selectivity index of neurons with significant ramp rate correlation was shifted toward positive (Wilcoxon signed-rank test, P < 0.05), that of neurons without significance was not (P > 0.5).

Supplementary Figure 8 Effect of waiting times and ongoing movements on M2 neural activity.

We used a stepwise regression (MATLAB, stepwisefit), because the number of independent variables is relatively large compared with the data size. The significance of each coefficient was estimated by running the same analysis after shuffling a variable of interest across trials (1000 times). We first tracked the position (X, Y) and orientation of the rat’s body, using custom-written software with Python using the OpenCV library. For the firing rate correlation analysis, the average firing rate of each 400 ms time bin was regressed using stepwise regression using for independent variables waiting time and 6 movement variables of the same time bin: X-, Y-, angular-position, X-, Y- and angular-velocity. P-values were corrected for multiple comparisons (Bonferroni correction). (a) An example neuron that showed a significant correlation between waiting time and firing rate with the stepwise regression analysis. Perievent time histograms (PETHs) around start of waiting shown in the same way as in Figure 4 (left). A scatter plot shows the relationship between X position (x-axis), waiting time (y-axis) and firing rate (color axis) (right). Note that the color gradient (i.e. firing rate gradient) is obvious in the waiting time axis but not in the X position axis. None of the other movement variables explained the firing rate (data not shown). (b) An example neuron that showed a spurious correlation between waiting time and firing rate in the original analysis, which disappears if movement variables are included in the regression analysis. PETHs around start of waiting (left). A scatter plot shows the relationship between Y position (x-axis), waiting time (y-axis) and firing rate (color axis) (right). Note that the firing rate gradient is obvious in the Y position axis but not as much in the waiting time axis. (c) Fractions of neurons with firing rate significantly correlated with waiting times, as described in the main text, applied to the neurons from video recorded sessions (n = 164 neurons) is shown in gray bar. Fractions of neurons with firing rate significantly correlated with each of the variables shown in the bottom labels using a stepwise regression analysis are shown in black and white bars. (d) Fractions of neurons with significant correlation with each variable shown in the label among waiting time predictive neurons in the original analysis. Eighty percent (20 of 25) of neurons remained significantly correlated with waiting times with the stepwise regression. (e) Fractions of neurons with rate of ramping activity significantly correlated with waiting times among ramp-to-threshold type predictive neurons from video recorded sessions, as described in the main text, are shown in gray. Fractions of neurons with ramp-rate significantly correlated with each of the variables described in the label are shown in black and white bars. Seventy-five percent (6 of 8) of ramp-rate correlated neurons remained significant with the stepwise regression. For the ramp-rate correlation analysis, we regressed the rate of ramping activity with the same variables described above. Motor variables were calculated at a single time bin from 0 to 400 ms relative to the start of waiting, because this is the period common to all the trials. We also performed the analysis using the entire waiting time as a single time bin (different bin size for different waiting time trials). This yielded similar results (data not shown).

Supplementary Figure 9 Trial history analysis using a stepwise multiple linear regression.

(a) A fraction of neurons with activity significantly correlated with waiting times (current impatient trials) using a standard simple linear regressions analysis (analysis described in the main text) is shown in gray (left most bar). Fraction of neurons significantly correlated with each of the trial history variables shown in the bottom labels using a stepwise multiple linear regression are shown in black (waiting time of current impatient trials) and white bars (other trial history variables). We used a stepwise regression (MATLAB, stepwisefit), because the number of independent variables is relatively large compared with the data size (number of trials). The significance of each coefficient was estimated by running the same analysis after shuffling a variable of interest across trials (1000 times). Note that using the stepwise multiple linear regression analysis including trial history variables, 15.7 % of neurons are significantly correlated with waiting times of the current trial (a black bar), comparable to 18.0% with the analysis shown in Figures 5 and 6 (a gray bar). (b) Time course and the sign of correlation for all the waiting time predictive neurons with stepwise multiple linear regressions (n = 56 neurons). The format is the same as is in Figure 5c. The time course of the predictive activity was also comparable to the original analysis. We also observed neurons that carried information about trial history, consistent with previous studies21. We did not further analyze those correlations because it was beyond the scope of our current study.

Supplementary Figure 10 Anatomical location of recording sites.

(a) Nissl stained coronal section of a rat frontal cortex. The arrow indicates a site of an electrolytic lesion. (b) Fluorescent image of an adjacent section. The arrow indicates a track of DiI coated tetrode. (c) Corresponding section from the rat brain atlas52. (d) Recording sites of all the neurons analyzed for the ramp-up predictive activity (correlation between time to cross a threshold firing rate and waiting time). Neurons with significant correlation between times to cross a threshold firing rate and waiting times are shown with color. Open pink circle, ramp-up neurons without significant correlation between ramp rates and waiting times. Filled pink circle, ramp-up neurons with significant correlation between ramp rates and waiting times. No obvious clustering of predictive neurons was observed. (e) The same as in d but for ramp-down neurons. Open blue circle, ramp-down neurons without significant correlation between ramp rates and waiting times. Filled blue circle, ramp-down neurons with significant correlation between ramp rates and waiting times. No obvious clustering of predictive neurons was observed. (f) Recording site of all the neurons analyzed for the predictive activity (correlation between firing rate and waiting time at a certain time window). Neurons whose activity was positively correlated with waiting time are shown in green, while neurons with negative correlations are shown in orange. No obvious clustering of predictive neurons was observed.

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Supplementary Figures 1–10 (PDF 2593 kb)

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Behavior from an example recording session.

For this rat, the nose-poke waiting port was located at the left side, lever-press waiting port at the right side and reward port at the center. Entering the nose-poke waiting port was indicated by a filled white circle on top of the waiting port, which is an open circle outside the waiting period. Presentation of Tone 1 is indicated by light green bars at both sides and Tone 2 by dark green bars. Delivery of a water reward was indicated by a blue circle appearing on top of the reward port. Although the onset of the presentation of tones and water is accurate, the duration of those are not accurately represented in the movie, because they are brief in the actual experiment and hard to see in the movie. (MOV 7179 kb)

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Murakami, M., Vicente, M., Costa, G. et al. Neural antecedents of self-initiated actions in secondary motor cortex. Nat Neurosci 17, 1574–1582 (2014). https://doi.org/10.1038/nn.3826

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