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, 28 (7), 2326-2339

Temporally Segmented Directionality in the Motor Cortex

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Temporally Segmented Directionality in the Motor Cortex

S B Suway et al. Cereb Cortex.

Abstract

Developing models of the dynamic and complex patterns of information processing that take place during behavior is a major thrust of systems neuroscience. An underlying assumption of many models is that the same set of rules applies across different conditions. This has been the case for directional tuning during volitional movement; a single cosine function has been remarkably robust for describing the encoding of movement direction in different types of neurons, in many locations of the nervous system, and even across species. However, detailed examination of the tuning time course in motor cortex suggests that direction coding may be labile. Here, we show that there are discrete time epochs within single reaches, between which individual neurons change their tuning. Our findings suggest that motor cortical activity patterns may reflect consistent changes in the state of the control system during center-out reaching. These transitions are likely linked to different behavioral components, suggesting that the task defines changes in the operational structure of the control system.

Figures

Figure 1.
Figure 1.
Simulated examples of tuning vs. time. Each example is composed of 8 straight, center-out, point-to-point reaches. The top row is the firing rate time course of the simulated neuron, with colored traces corresponding to movement in a particular direction. The bottom row shows the PD calculated in separate bins throughout the trial. (A,D) A canonical neuron that behaves according to the classic cosine model, in which direction is the only determinant of firing rate. Assuming a straight movement, the firing rate would be a step-function and constant during the movement. The PDs of tuning functions calculated in small windows throughout the trial are constant across windows. (B,E) A more typical firing rate pattern in which the neuron shows several peaks of firing during reaching. In this case, the entire length of the profile is scaled uniformly by direction and the PD is stationary. (C,F) A neuron with varying modulation patterns during the trial. Neural modulation again has multiple components during the reach, but firing rate is both direction- and time- dependent. At each point in time, direction has a different effect on firing rate. In this case, the PD changes continually through the trial.
Figure 2.
Figure 2.
Experimental design and kinematic results. (A) Monkeys reached for radial targets displayed in a 3D computer monitor. (B) Representative kinematics from a single monkey C experiment. 1 Target display; 2 Movement onset; 3 Peak velocity; 4 End of movement; 5 Reward administration. (C) Two-dimensional target arrangement, monkey C. Left—targets individually displayed in VR. Right—Trajectories of the hand collected during one experiment, with all repeated movements shown (47 per target). (D) Two-dimensional target arrangement, monkey N. (E) Three-dimensional target arrangement, monkey F. Trial-averaged hand trajectories are shown for monkeys N and F. In panels (C–E), the color of the targets corresponds to that of the trajectories. See also Supplementary Figure 1 for electrode array placement.
Figure 3.
Figure 3.
Stability vs. time for the population. (A) Reliability of PD estimates. To determine trial-to-trial variability (width of confidence interval) for each neuron and at each time bin, PDs were repeatedly estimated using a bootstrap method (see Materials and Methods). Black lines show the population median for width of PD confidence intervals (95%) computed by the bootstrap; gray lines show quartiles (Q1, Q3) for the population. The distribution began narrowing 200 ms before movement onset, with tuning becoming maximally reliable for most neurons around movement onset. (B) The same reliability trend was found when using R2 for the cosine tuning regression. Tuning functions for each neuron were calculated in each 20 ms bin by averaging firing rates over task repetitions. Black and gray lines show the population median, Q1, and Q3 R2 of the cosine fits to each neuronal tuning curve. (C) PD stability was assessed within 200 ms overlapping windows that were incremented in 20 ms bins. The stability test, used previously for the whole trial, was then applied to each 200 ms window for each analyzed cell. To decrease the influence of noise, bins with poor tuning (R2 < 0.6) were excluded. The percentage of units with stable PDs peaked 100 ms before movement onset, decreased toward the beginning of movement, and then gradually increased toward the end of the movement when the cursor was in the target. Square markers in panel C correspond to target show, movement onset, peak velocity, movement offset, and reward administration.
Figure 4.
Figure 4.
Episodic modulation patterns. Firing rate patterns of 4 different neuronal units are shown in columns. (A) The first row shows trial-averaged firing rate profiles for each target direction. Square markers correspond to alignments on target show, movement onset, peak velocity, and movement offset. (B) Tuning function amplitudes (estimated modulation depth, mˆt) calculated in each bin are shown in the second row. (C) Gaussian-shaped components fit to the tuning amplitudes are shown in the third row. (D) Eigenvectors calculated from correlation matrices across bins are shown in the fourth row (darker line intensity for eigenvectors with more explanatory power). (E) PDs computed repeatedly over the course of the trial are shown in the fifth row. The darkened portions highlight the 100 ms period centered on each Gaussian-shaped component and which was tested for PD stability. Error bars show the 95% CI (computed using bootstrap, see Materials and Methods).
Figure 5.
Figure 5.
PD stability during component times. (A) Component-wise directional stability. The majority of fitted components for each monkey were found to have stable PDs for the 100 ms period centered on the component peak (see Materials and Methods for statistical method). (B) Number of stable components fit per neuron. The majority of components had stable PDs for at least 100 ms. When considering the number of components fit per neuron, regardless of stability, the breakdown was similar. From 0 to 3 components, the percentages for monkey C were 27, 33, 35, 4; for monkey F 6, 56, 34, 3; and for monkey N 39, 42, 18, 0. Total number of components fit for monkeys C, F, and N was 109, 161, and 146, respectively.
Figure 6.
Figure 6.
Temporal distribution of tuning components. The timings of Gaussian-shaped peaks from “stable” components were used to make histograms of component occurrence for each monkey (left columns in A and B). Gaussian mixture models were fit to component times, and the Akaike information criterion (AIC) was computed for fits with 1 through 5 clusters (right columns in A and B). We consistently observed a minimum AIC for 3 clusters, suggesting components fall into 1 of 3 epochs, which are similar across monkeys. (A) Stable component times observed during “hold” tasks. (B) Stable component times observed during “no hold” tasks. Histograms in the bottom row of A and B represent data pooled from each preceding histogram. The 3 epochs are most distinctive in the “hold” task, but can also be observed in the “no hold” task. Movement begins at Time = 0 and the filled square symbols denote the behavioral events of target show, peak velocity, movement offset, and end of hold (if relevant). The time scales in A have been scaled linearly between event times to match those of monkey C; time scales in B have been scaled to match monkey N.
Figure 7.
Figure 7.
Population vector reconstructions of cursor trajectories to 16 center-out targets for monkey C. Neural trajectories were generated by calculating population vectors in each bin (from 100 ms prior to movement until 100 ms prior to end of trial) and adding the vectors tip-to-tail. Target colors are as in Figure 2. (A) The population vectors were calculated using a single, initial PD for each neuron. This method resulted in highly distorted trajectories. (B) PDs from within each component (see Materials and Methods) were used to construct the population vectors. This method largely remedied the distortions seen in A. (C) Tuning functions were calculated in each time bin and their PDs were used for the neural trajectories. This method yielded only modest improvements over the method used in B.
Figure 8.
Figure 8.
Offline component recognition. We investigated the possibility that patterns of population activity could be used to identify the tuning segment (component) of each single unit at any given moment in a trial. We trained classifiers (LDA), one for each unit, using the concurrent firing rate samples from every simultaneously recorded unit. For each unit’s classifier, samples in each bin were labeled based on their timing, relative to the unit’s previously fitted components, with transitions delimited by the point at which one component’s amplitude became larger than the previous component’s. These labels were used to train the linear discriminant classifier for that unit, which was then used to predict the “current” temporal epoch for each 20 ms sample during each trial. Five-fold cross-validated success rates for units with at least 2 components were high: 94.7 ± 1.4%, 95.2 ± 1.6%, and 92.5 ± 2.0% for monkeys N, F, and C, respectively. Histograms of success rate per unit are displayed for each monkey. Success rate by chance was computed by randomly shuffling the group labels prior to training the classifiers, and yielded much worse classification: 50.1% ± 0.3, 48.8 ± 5.1%, and 48.2 ± 5.4% for monkeys N, F, and C, respectively.

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