Timescales of Local and Cross-Area Interactions during Neuroprosthetic Learning

Abstract

How does the brain integrate signals with different timescales to drive purposeful actions? Brain-machine interfaces (BMIs) offer a powerful tool to causally test how distributed neural networks achieve specific neural patterns. During neuroprosthetic learning, actuator movements are causally linked to primary motor cortex (M1) neurons, i.e., “direct” neurons that project to the decoder and whose firing is required to successfully perform the task. However, it is unknown how such direct M1 neurons interact with both “indirect” local (in M1 but not part of the decoder) and across area neural populations (e.g., in premotor cortex/M2), all of which are embedded in complex biological recurrent networks. Here, we trained male rats to perform a M1-BMI task and simultaneously recorded the activity of indirect neurons in both M2 and M1. We found that both M2 and M1 indirect neuron populations could be used to predict the activity of the direct neurons (i.e., “BMI-potent activity”). Interestingly, compared with M1 indirect activity, M2 neural activity was correlated with BMI-potent activity across a longer set of time lags, and the timescale of population activity patterns evolved more slowly. M2 units also predicted the activity of both M1 direct and indirect neural populations, suggesting that M2 population dynamics provide a continuous modulatory influence on M1 activity as a whole, rather than a moment-by-moment influence solely on neurons most relevant to a task. Together, our results indicate that longer timescale M2 activity provides modulatory influence over extended time lags on shorter-timescale control signals in M1.SIGNIFICANCE STATEMENTA central question in the study of motor control is whether primary motor cortex (M1) and premotor cortex (M2) interact through task-specific subpopulations of neurons, or whether tasks engage broader correlated networks. Brain-machine interfaces (BMIs) are powerful tools to study cross-area interactions. Here, we performed simultaneous recordings of M1 and M2 in a BMI task using a subpopulation of M1 neurons (direct neurons). We found that activity outside of direct neurons in M1 and M2 was predictive of M1-BMI task activity, and that M2 activity evolved at slower timescales than M1. These findings suggest that M2 provides a continuous modulatory influence on M1 as a whole, supporting a model of interactions through broad correlated networks rather than task-specific neural subpopulations.

Cortico-cerebellar coordination facilitates neuroprosthetic control

“…This, with other recent work, leads us to conclude that M1 task–relevant cells multiplex signals locally ( 11 , 44 ) as well as from distant-area cerebellar activity ( 62 ). Our regression of M1 BMI potent space activity also indicated that cerebellum TR i units showed a broader timescale influence of cerebellum on M1 TR d activity [this is different from the shorter timescale influences seen between M1 TR d and M1 TR i units ( 44 )]. Such broader influence of cerebellar activity may also be related to coordination in the larger motor network.…”

“…Upon a simple comparison of latency of M1 and cerebellar spiking activities’ peaks, we observed that cerebellum TR i activity tended to peak before M1 TR d during early trials [time to peak for M1 activity: 238.01 ± 6.42 ms; and time to peak for cerebellum activity: 236.82 ± 6.48 ms, mixed-effects model: t (646) = −0.07, P = 0.93] and late trials [time to peak for M1 activity: 239.82 ± 8.26 ms and time to peak for cerebellum activity: 211.16 ± 15.12 ms, mixed-effects model: t (646) = −1.87, P = 0.06]. This then lead us to develop a GLM to determine the relationship between cerebellum indirect activity and M1 activity ( 44 ).…”

“…We use GLM, using the MATLAB function fitglm , to predict M1 BMI potent space activity from cerebellum TR i ’ s. The function created generalized linear regression models with linear model specifications (containing an intercept and linear term for each predictor) and fitted using a normal distribution for the response variable. For predicting M1 TR d activity from cerebellum TR i ’s, BMI potent space activity was calculated as the difference between summed M1 TR d + activity and summed M1 TR d − activity (+/− being positive or negative unit weight associated TR d ’s), which was used as the response variable ( 44 ). Predictors were binned firing rates of cerebellum TR i units, where each neuron appeared more than once with variable time lags ranging from–50 to +50 ms relative to the BMI-potent activity.…”