A Gaussian Process Model of Human Electrocorticographic Data.
Gaussian process regression
electrocorticography (ECoG)
epilepsy
intracranial electroencephalography (iEEG)
local field potential (LFP)
maximum likelihood estimation
Journal
Cerebral cortex (New York, N.Y. : 1991)
ISSN: 1460-2199
Titre abrégé: Cereb Cortex
Pays: United States
ID NLM: 9110718
Informations de publication
Date de publication:
03 09 2020
03 09 2020
Historique:
received:
13
03
2020
revised:
15
04
2020
accepted:
16
04
2020
pubmed:
5
6
2020
medline:
21
12
2021
entrez:
5
6
2020
Statut:
ppublish
Résumé
We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people's brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual's brain: given recordings from a limited set of locations in that individual's brain, along with the observed spatial correlations learned from other people's recordings, how much can be inferred about ongoing activity at other locations throughout that individual's brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.
Identifiants
pubmed: 32495832
pii: 5851264
doi: 10.1093/cercor/bhaa115
pmc: PMC7472198
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
5333-5345Subventions
Organisme : NINDS NIH HHS
ID : K23 NS110962
Pays : United States
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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