In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 8 ( 2022-8-8), p. e1010401-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010401
DOI:
10.1371/journal.pcbi.1010401.g001
DOI:
10.1371/journal.pcbi.1010401.g002
DOI:
10.1371/journal.pcbi.1010401.g003
DOI:
10.1371/journal.pcbi.1010401.g004
DOI:
10.1371/journal.pcbi.1010401.g005
DOI:
10.1371/journal.pcbi.1010401.g006
DOI:
10.1371/journal.pcbi.1010401.g007
DOI:
10.1371/journal.pcbi.1010401.g008
DOI:
10.1371/journal.pcbi.1010401.g009
DOI:
10.1371/journal.pcbi.1010401.t001
DOI:
10.1371/journal.pcbi.1010401.t002
DOI:
10.1371/journal.pcbi.1010401.s001
DOI:
10.1371/journal.pcbi.1010401.s002
DOI:
10.1371/journal.pcbi.1010401.s003
DOI:
10.1371/journal.pcbi.1010401.s004
DOI:
10.1371/journal.pcbi.1010401.s005
DOI:
10.1371/journal.pcbi.1010401.s006
DOI:
10.1371/journal.pcbi.1010401.s007
DOI:
10.1371/journal.pcbi.1010401.s008
DOI:
10.1371/journal.pcbi.1010401.s009
DOI:
10.1371/journal.pcbi.1010401.r001
DOI:
10.1371/journal.pcbi.1010401.r002
DOI:
10.1371/journal.pcbi.1010401.r003
DOI:
10.1371/journal.pcbi.1010401.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6