Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information

Skillful, 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.

It remains unclear how motor cortical neurons flexibly perform the computations necessary to generate movement. We hypothesized that neuronal coactivity contains movement information, and its dynamics can reveal how the population switches computations during a task. We quantified coactivity as a functional network (FN) with single neurons as nodes and population coactivity as directed weighted edges. We also constructed FNs within short epochs across a trial to determine when coactivity begins to carry information and to investigate the dynamic structure of these interactions. Following the Instruction cue, reciprocal connections in FNs transiently decrease, and shortly after, FNs become maximally decodable for reach direction.