246 Deciphering Spatiotemporal Patterns in the Motor Cortex Using a High-Density Micro-Electrocorticography Array

INTRODUCTION: Understanding dynamic neural activity in the motor cortex during movement is crucial for neurological treatment, rehabilitation, and brain-computer interfaces. The relationship between spatially distributed brain activity and precise arm and hand movement control is key to understanding motor function. However, observing these dynamics has been challenging due to limitations in array size, contact density, and clinical accessibility to the motor cortex. METHODS: Our study employs a high-density 1024-contact cortical surface array in a 2x2 cm area to overcome spatial resolution limitations, providing an unprecedented view of spatial dynamics across the motor cortex during and after movement. Under IRB approval, the array was stereotactically positioned over the hand knob of the motor cortex in a patient undergoing thalamic deep brain stimulation (DBS) for essential tremor. In the DBS-OFF condition, the patient performed hand gestures while inertial sensors and a piezoelectric glove recorded arm and hand kinematics. We applied the Hilbert Transform to extract band-limited magnitude and phase components in distinct frequency bands (delta, theta, alpha, beta, gamma) during the motor task. For each frequency band, we computed spatial properties to determine signal propagation characteristics along the array, including phase gradients, divergence, and curl of the spatial signal. RESULTS: We observed prominent beta spirals during rest, which were suppressed during movement. Relative to rest, beta spirals also reversed their rotational direction and radiated outward from a focal point on the array. Alpha and theta bands shared similar dynamics. Delta activity, however, increased during movement, manifesting as spirals and transient hotspots. CONCLUSIONS: High-resolution micro-electrocorticography over the motor cortex reveals complex spatiotemporal dynamics related to hand and arm movement. Understanding these dynamics and their relationship to real-time movement control is crucial for comprehending motor cortex computations and restoring functions using brain-computer interfaces.