Instantaneous Midbrain Control of Saccade Velocity

Abstract

The ability to interact with our environment requires the brain to transform spatially represented sensory signals into temporally encoded motor commands for appropriate control of the relevant effectors. For visually guided eye movements, or saccades, the superior colliculus (SC) is assumed to be the final stage of spatial representation, and instantaneous control of the movement is achieved through a rate code representation in the lower brain stem. We investigated whether SC activity in nonhuman primates (Macaca mulatta, 2 male and 1 female) also uses a dynamic rate code, in addition to the spatial representation. Noting that the kinematics of amplitude-matched movements exhibit trial-to-trial variability, we regressed instantaneous SC activity with instantaneous eye velocity and found a robust correlation throughout saccade duration. Peak correlation was tightly linked to time of peak velocity, the optimal efferent delay between SC activity and eye velocity was constant at ∼12 ms both at onset and during the saccade, and SC neurons with higher firing rates exhibited stronger correlations. Moreover, the strong correlative relationship and constant efferent delay observation were preserved when eye movement profiles were substantially altered by a blink-induced perturbation. These results indicate that the rate code of individual SC neurons can control instantaneous eye velocity and argue against a serial process of spatial-to-temporal transformation. They also motivated us to consider a new framework of saccade control that does not incorporate traditionally accepted elements, such as the comparator and resettable integrator, whose neural correlates have remained elusive.SIGNIFICANCE STATEMENT All movements exhibit time-varying features that are under instantaneous control of the innervating neural command. At what stage in the brain is dynamical control present? It is well known that, in the skeletomotor system, neurons in the motor cortex use dynamical control. In the oculomotor system, in contrast, instantaneous velocity control of saccadic eye movements is not thought to be enforced until the lower brainstem. Using correlations between residual signals across trials, we show that instantaneous control of saccade velocity is present earlier in the visuo-oculomotor neuraxis, at the level of superior colliculus. The results require us to consider alternate frameworks of the neural control of saccades.

Keywords: efference copy; local feedback model; motor execution; movement variability; neural integrator; oculomotor.

Figure 1.

Illustration of across-trial analysis for normal saccades. A, Radial velocity traces (top) and their corresponding neural activity (bottom) from one example cell. Positive velocity values represent instantaneous eye movement toward the target, whereas negative values represent movement away. Four traces are highlighted in color to illustrate the fluctuations of individual trials around the mean (thick traces). B, Same traces after subtraction of the mean waveform. Boxed sections represent samples of activity and velocity used for regression. Although these samples were analyzed at various delays, only −9 ms delay is shown here. C, Velocity samples from box in B are plotted as a function of their corresponding activity sampled at a −9 ms delay. Regression line and the correlation coefficient are provided. D, Correlation coefficient values (color) represented as a function of the delay and time point of the saccade.

Figure 2.

Temporal profiles of eye velocity and neural activity for normal saccades. Green traces represent the 10% of trials with highest peak velocity. Purple traces represent the 10% of trials with lowest peak velocity. Thick, matching-color traces in each subplot represent averages. Blue and red traces are from Figure 1A. Data are from one session. In addition to the general similarity of waveforms between the velocity (top) and their corresponding activity (bottom), there is also a correlation between the temporal features. Traces with high (low) peak velocity and short (long) deceleration duration are associated with similar profiles in the neural activity.

Figure 3.

Summary of within- and across-trial correlation analyses for normal saccades. A, Within-trial correlation analysis. Black line indicates the correlation coefficient between activity and velocity residuals as a function of the temporal shift. Gray outline represents 2 SEs around the mean patterns from 189 neurons. Pink line and outline represent the mean correlation coefficients and 2 SDs from the mean of the shuffled data. B, Across-trials correlation analysis. Heatmap of correlation coefficients between SC activity and eye velocity residuals for each time point during the saccade and temporal shift between the two residual vectors (“delay”). Arrow and horizontal hyphenated line represent the efferent delay (−12 ms) at which the average correlation was highest. Left and right vertical red lines indicate the beginning and end, respectively, of the shortest saccade in the dataset. Data past the rightmost red bar exclude saccades that terminated before the time points on the x axis. C, Correlation coefficients as a function of saccade time points for the optimal efferent delay shown in B. Pink line and outline represent the mean and 2 SDs for the across-trials analysis performed on shuffled data.

Figure 4.

Temporal characteristics of activity-velocity correlation. A, Histogram of average peak correlation time relative to average peak velocity time for each neuron. The count on y axis indicates the number of neurons. B, Histogram of cumulative duration (as proportion of total saccade length) for which the correlation remained above significance level. C, Relationship between peak correlation and the duration of the correlation. Each point indicates one neuron. Blue line indicates the best fit line to the data.

Figure 5.

Analysis of data recorded from laminar probes. A, Peak correlation of each SC neuron is plotted against the average peak firing rate of that neuron. Neurons recorded in the same penetration are plotted using the same color. Thus, each color represents data from different sessions. The best fit line to each session's data is shown in the matching color. B, Data from A, de-meaned and pooled across sessions. Each de-meaned value is obtained after subtracting the respective average across all neurons in its track. Red hyphenated line indicates the best fit line.

Figure 6.

Illustration of across-trial analysis for blink-perturbed saccades. Same format as in Figure 1. A, Velocity and spike density traces. B, Residuals of traces in A. C, Correlation between velocity and activity residuals at −9 ms delay. D, Heatmap of correlations at every delay and at every saccade time point.

Figure 7.

Comparison of correlation analyses for blink-perturbed and normal saccades. Within-trial correlation between activity and velocity residuals for (A) normal and (D) blink-perturbed saccades available for 50 of 189 neurons. The heatmaps of correlation coefficients obtained from across-trials analysis for (B) normal and (E) blink-perturbed movements. Correlation coefficients as a function of saccade time points for the optimal efferent delay for (C) normal and (F) blink-perturbed saccades. The plots follow the same conventions used in Figure 3.

Figure 8.

Linear regression features between SC activity and eye velocity. A, A pairwise comparison of the regression slopes obtained for normal (x axis) and blink-perturbed (y axis) conditions for each neuron. Slopes statistically significant different from zero for normal-only, blink-only, and both types of trials are shown in cyan, magenta, and black colors, respectively. Filled (open) circles denote neurons for which the slopes for normal and blink conditions were (not) statistically significantly different from each other. Hyphenated line indicates the unity relationship. B, Relationship between slope and R² values. Cyan represents normal trials. Magenta represents blink perturbation trials.

Figure 9.

Alternate models of neural control of saccades. A, One version of the traditional local feedback loop model. Key elements or processes include computing a desired displacement command (ΔE_d) from SC population activity, using a comparator (red summation symbol) to compute dynamic motor error (m.e.), and using a resettable neural integrator (RNI; red text) to convert the eye velocity signal (E′_EBN (t)) to current eye displacement (ΔE). Also, the EBN performs a nonlinear transform on motor error signal to determine eye velocity. B, Our revised conceptual model is void of the comparator and resettable neural integrator elements and therefore no longer computes dynamic motor error. The EBN output is a linear transform of the SC input. The eye velocity feedback signal (E′_EBN (t)) projects to the SC. The spatiotemporal pattern of population SC activity is pivotal in determining the instantaneous eye velocity. MN, Motoneuron.