Abstract
Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed “in the wild.” Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with an important difference: there was considerable neural variability across subjects and events. We modeled inter-event variability using ten behavioral and environmental features; the most important features explaining this variability were reach angle and recording day. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during spontaneous movements and contributes to our understanding of long-term naturalistic behavior.
Competing Interest Statement
The authors have declared no competing interest.
