Werner-Reiss, U. | bioRxiv Science

bioRxiv2019

Phase-specific microstimulation in brain-machine interface setting differentially modulates beta oscillations and affects behavior

It is widely accepted that beta-band oscillations play a role in sensorimotor behavior. To further explore this role, we developed a novel hybrid platform to combine operant conditioning and phase-specific intracortical microstimulation (ICMS). We trained monkeys, implanted with 96 electrodes arrays in motor cortex, to volitionally enhance local field potential (LFP) beta-band (20-30Hz) activity at selected sites using a brain-machine interface (BMI). We demonstrate that beta oscillations of LFP and single-unit spiking activity increased dramatically with BMI training, and that pre-movement Beta-power was anti-correlated with task performance. We also show that phase-specific ICMS modulated the power and phase of oscillations, shifting local networks between oscillatory and non-oscillatory states. Furthermore, ICMS induced phase-dependent effects in animal reaction times and success rates. These findings contribute to unraveling of the functional role of cortical oscillations, and to future development of clinical tools for ameliorating abnormal neuronal activities in brain diseases.

neuroscience↗

bioRxiv2019

Neuronal activity and learning in local cortical networks are modulated by the action-perception state

During sensorimotor learning, neuronal networks change to optimize the associations between action and perception. In this study, we examine how the brain harnesses neuronal patterns that correspond to the current action-perception state during learning. To this end, we recorded activity from motor cortex while monkeys either performed a familiar motor task (movement-state) or learned to control the firing rate of a target neuron using a brain-machine interface (BMI-state). Before learning, monkeys were placed in an observation-state, where no action was required. We found that neuronal patterns during the BMI-state were markedly different from the movement-state patterns. BMI-state patterns were initially similar to those in the observation-state and evolved to produce an increase in the firing rate of the target neuron. The overall activity of the non-target neurons remained similar after learning, suggesting that excitatory-inhibitory balance was maintained. Indeed, a novel neural-level reinforcement-learning network model operating in a chaotic regime of balanced excitation and inhibition predicts our results in detail. We conclude that during BMI learning, the brain can adapt patterns corresponding to the current action-perception state to gain rewards. Moreover, our results show that we can predict activity changes that occur during learning based on the pre-learning activity. This new finding may serve as a key step toward clinical brain-machine interface applications to modify impaired brain activity.

neuroscience↗