TY - JOUR
T1 - Spatiotemporal dynamics of human high gamma discriminate naturalistic behavioral states
AU - Alasfour, Abdulwahab
AU - Gabriel, Paolo
AU - Jiang, Xi
AU - Shamie, Isaac
AU - Melloni, Lucia
AU - Thesen, Thomas
AU - Dugan, Patricia
AU - Friedman, Daniel
AU - Doyle, Werner
AU - Devinsky, Orin
AU - Gonda, David
AU - Sattar, Shifteh
AU - Wang, Sonya
AU - Halgren, Eric
AU - Gilja, Vikash
N1 - Publisher Copyright:
© 2022 Alasfour et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1010401
DO - 10.1371/journal.pcbi.1010401
M3 - Article
C2 - 35939509
AN - SCOPUS:85136100683
SN - 1553-734X
VL - 18
JO - PLoS computational biology
JF - PLoS computational biology
IS - 8
M1 - e1010401
ER -