Bimbi, M. | bioRxiv Science

bioRxiv2025

Frontal cortex encodes action goals and social context in freely moving and socially interacting macaques

Classic studies of goal-directed behavior in primates have investigated frontal cortical circuits under restraint conditions, leaving their role in natural social contexts largely unknown. We wirelessly recorded neural activity from freely moving macaques to study how frontal areas encode actions during naturalistic interactions. Neurons in ventral premotor and ventrolateral prefrontal cortex distinguished identical motor acts, such as grasping, depending on whether they occurred during foraging or grooming, indicating sensitivity to social meaning. Some units were selectively tuned to socially directed goals, while population analyses showed both regions flexibly integrated motor plans with social context. These results provide direct evidence that these regions, long considered key motor hubs, also encode the social dimension of action, redefining current models of frontal lobe function. One-Sentence SummaryFreely-moving, social-interacting macaques reveal new properties of frontal circuits in encoding motor goals and social context.

neuroscience↗

bioRxiv2022

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

neuroscience↗