Spatio-Temporal Patterning in Primary Motor Cortex at Movement Onset

Abstract

Voluntary movement initiation involves the engagement of large populations of motor cortical neurons around movement onset. Despite knowledge of the temporal dynamics that lead to movement, the spatial structure of these dynamics across the cortical surface remains unknown. In data from 4 rhesus macaques, we show that the timing of attenuation of beta frequency local field potential oscillations, a correlate of locally activated cortex, forms a spatial gradient across primary motor cortex (MI). We show that these spatio-temporal dynamics are recapitulated in the engagement order of ensembles of MI neurons. We demonstrate that these patterns are unique to movement onset and suggest that movement initiation requires a precise spatio-temporal sequential activation of neurons in MI.

Keywords: local field potentials; movement initiation; sequential engagement; spatio-temporal patterning.

Figure 1.

Experimental setup and beta attenuation analysis. ( A ). Animals were trained to perform an instructed-delay center-out reaching task. The animals used a 2-link robotic exoskeleton to control the position of a cursor on a screen projected above their arm. ( B ). LFPs were filtered into the beta frequency range and aligned to movement onset (MO) for every trial. The beta oscillation of one electrode's LFP (gray trace) and its amplitude (black trace) in arbitrary units (au) are shown for 3 different example trials from animal Rs. ( C ). Beta amplitudes from the electrode in B were averaged across all trials including all movement directions to estimate the trial-averaged beta amplitude aligned to movement onset (black trace indicates average amplitude, gray area indicates ±2 standard errors of the mean [SEM]). ( D ). LFPs were simultaneously recorded across multiple sites in MI during the experiment. ( E ). Trial-averaged beta amplitude profiles for 3 different electrodes (color corresponding to the sites highlighted in ( D ) are shown [mean ± 2 SEM]). The time at which the beta amplitude passed an attenuation threshold (horizontal black dashed line) was estimated for every electrode. Note that beta activity on each electrode does not simultaneously pass the attenuation threshold ( E inset). Data are from animal Rs.

Figure 2.

Array placement and beta attenuation maps. ( A ). Line drawings depicting placement of the multielectrode arrays in the upper limb area of MI in 4 monkeys. The central and arcuate sulci are indicated (CS and AS, respectively). Data from monkeys V and Rj have been flipped vertically to normalize the location of the CS and AS across animals. ( B ). Heat maps depicting the timings of attenuation in beta amplitude across all electrodes on each array relative to movement onset (time = 0). We fit a linear model to describe the spatial progression of BATs from earlier to later. This BAO is indicated by a black arrow whose magnitude is proportional to the model's goodness of fit. ( C ). We performed a spatial shuffle analysis to test for the significance of the BAO. The spatial location of BATs was shuffled one million times. For each shuffle, we computed the BAO of the shuffled data and its goodness of fit, R ² . The distribution of R ² values for each monkey from shuffled data (gray curve) is shown, as well as the R ² statistic observed in actual data (blue vertical line). The BAO was highly significant in all animals. ( D ). The consistency of the BAO was assessed with a bootstrap analysis. For each iteration of the bootstrap (1000 total), we randomly partitioned each dataset into halves. A BAO was estimated from the data in each half, and the angle between them was measured. The distribution of angular differences (blue bars) was compared with a null distribution of angular differences estimated from spatially shuffled data (gray bars) using the same shuffling procedure as ( C ). We found strong evidence that the angular differences we observed between BAOs estimated on subsets of trials were significantly less than what would be expected under chance (Kolmogorov–Smirnov test, P indicated on figure).

Figure 3.

Unit spiking analysis. ( A ). Raster plot of unit spiking activity on 3 electrodes corresponding to those in Figure  1 D. Each row in the raster indicates spiking activity on one trial aligned to movement onset. Color indicates the direction of movement. ( B ). Unit spiking activity was convolved with a Gaussian kernel and averaged across trials. Trial-averaged firing rates ± 1 SEM are shown for each movement direction. ( C ). Time-resolved entropy of the target direction probability distribution conditioned on the firing rates in ( B ). The dip in entropy reflects the directionally selective modulation of the neuron and is used to estimate its modulation time (vertical black line). Note that UMT follows the same pattern as BATs in Figure  1 E. ( D–F ). Three additional units that show significant modulation during movement preparation following the same conventions as ( A–C ) except data are aligned to the instruction cue. In both epochs, we have attempted to show units that are modulated early (left column), intermediately (center), or late (right) in their respective task epochs. During execution, the spatio-temporal sequence of unit modulation is consistent with the spatio-temporal sequence of beta attenuation; however, this consistency is absent during motor preparation.

Figure 4.

Relationship of beta attenuation to unit modulation. ( A ). We performed an analysis to test whether units were sequentially engaged along the BAO. We compared the UMT of each cell to its position on the BAO and found a significant linear relationship across animals (see main text for statistics). Here, UMTs are relative to movement onset, that is, movement onset occurs at t = 0. ( B ). Raw heat maps of UMT relative to movement onset as a function of their spatial location on the electrode array. The black arrow indicates the UMO for each dataset (length proportional to goodness of fit as in Fig.  2 ). Concentric circles indicate UMTs for multiple units on a given electrode. Anatomical coordinates are the same as Figure  2 . ( C ). We compared the similarity of the BAO and UMO in each animal and found that they were closely aligned during movement execution. ( D–E ). Same as ( A–B ) except all unit activity is relative to the instruction cue. During preparation, there is no significant relationship between the BAO and unit modulation ( D ), and UMT exhibit no significant spatial gradient ( E ).