Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states

In analyzing the neural correlates of naturalistic and unstructured behaviors, features of neural activity that are ignored in a trial-based experimental paradigm can be more fully studied and investigated. Here, we analyze neural activity from two patients using electrocorticography (ECoG) and stereo-electroencephalography (sEEG) recordings, and reveal that multiple neural signal characteristics exist that discriminate between unstructured and naturalistic behavioral states such as “engaging in dialogue” and “using electronics”. Using the high gamma amplitude as an estimate of neuronal firing rate, we demonstrate that behavioral states in a naturalistic setting are discriminable based on long-term mean shifts, variance shifts, and differences in the specific neural activity’s covariance structure. Both the rapid and slow changes in high gamma band activity separate unstructured behavioral states. We also use Gaussian process factor analysis (GPFA) to show the existence of salient spatiotemporal features with variable smoothness in time. Further, we demonstrate that both temporally smooth and stochastic spatiotemporal activity can be used to differentiate unstructured behavioral states. This is the first attempt to elucidate how different neural signal features contain information about behavioral states collected outside the conventional experimental paradigm.Author summary: Systems neuroscience research generally relies on the experimental trial-based paradigm to reveal how the brain works. While this methodology has proved feasible and fruitful for decades, there is a need to move toward studies that leverage unstructured and naturalistic behaviors to reveal the statistical and dynamic structure of neural activity when it is not constrained to a controlled experimental setting. Here we employ a data-driven approach that shows how various signal features of high gamma band activity recorded from electrocorticography (ECoG) and stereo-electroencephalography (sEEG) can differentiate naturalistic behavioral states. These signal features include both static and dynamic aspects of the spatiotemporal neural activity. Dynamic spatiotemporal patterns extracted from high gamma band activity span multiple time scales, have a global-brain spatial representation, and better fit the data in comparison to non-dynamic approaches. These patterns individually and collectively contain valuable information differentiating between naturalistic behavioral states. This work shows that neural activity in a naturalistic setting has multiple axes of variability that must be taken into consideration in the study of the neural basis of unstructured behaviors.