Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information

AbstractSkillful, voluntary movements are underpinned by computations performed by networks of interconnected neurons in the primary motor cortex (M1). Computations are reflected by patterns of coactivity between neurons. Using pairwise spike time statistics, coactivity can be summarized as a functional network (FN). Here, we show that the structure of FNs constructed from an instructed-delay reach task in nonhuman primates is behaviorally specific: Low-dimensional embedding and graph alignment scores show that FNs constructed from closer target reach directions are also closer in network space. Using short intervals across a trial, we constructed temporal FNs and found that temporal FNs traverse a low-dimensional subspace in a reach-specific trajectory. Alignment scores show that FNs become separable and correspondingly decodable shortly after the Instruction cue. Finally, we observe that reciprocal connections in FNs transiently decrease following the Instruction cue, consistent with the hypothesis that information external to the recorded population temporarily alters the structure of the network at this moment

Dynamic Structure of Functional Networks During Reaching

Individual neurons are interconnected, resulting in coordinated patterns of activity and emergent population dynamics that underlie complex behavior. I investigated the time-varying co-activity of neural populations, summarized as functional networks (FN), and their relation to motor behavior. First, I found that the structure of FNs in the primary motor cortex of macaques performing an instructed-delay reaching task was specific to the instructed reach, and that FNs constructed from trials with closer reach directions are also closer in network space. I extended this analysis by computing co-activity within a short interval across time to construct temporal FNs. This revealed that reach-specific differences in FNs emerge shortly after instruction and, consequently, become decodable for reach direction. In fact, FNs provide an additional source of information about behavior beyond what is carried by firing rates alone. Next, in the sensorimotor cortex of a marmoset performing a self-initiated virtual prey capture task, FNs at specific task events (such as trial start, target presentation, etc.) can be discriminated based on its nearest neighbors using either a low-dimensional metric (distance in an embedding space) or a network alignment score. Partitioning temporal FNs based on structure showed FN states that corresponded to specific behaviors such as arm extension or licking, and that successful trials involve a stereotyped sequence of these states. These results suggest that FNs can provide novel insight on the statistical regularities of neural co-activity that produce neural population dynamics during voluntary goal-directed reaching behavio