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Basis profile curve identification to understand electrical stimulation effects in human brain networks

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dc.contributor.authorMiller, Kai J.-
dc.contributor.authorMueller, Klaus-Robert-
dc.contributor.authorHermes, Dora-
dc.contributor.authorvan Vugt, Marieke Karlijn-
dc.contributor.authorMarinazzo, Daniele-
dc.contributor.authorvan Vugt, Marieke Karlijn-
dc.contributor.authorMarinazzo, Daniele-
dc.contributor.authorvan Vugt, Marieke Karlijn-
dc.contributor.authorMarinazzo, Daniele-
dc.date.accessioned2022-02-24T02:40:34Z-
dc.date.available2022-02-24T02:40:34Z-
dc.date.created2022-02-15-
dc.date.issued2021-09-
dc.identifier.issn1553-734X-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/136690-
dc.description.abstractBrain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique basis profile curves (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherPUBLIC LIBRARY SCIENCE-
dc.subjectEVOKED-POTENTIALS-
dc.subjectRESPONSES-
dc.subjectCORTEX-
dc.titleBasis profile curve identification to understand electrical stimulation effects in human brain networks-
dc.typeArticle-
dc.contributor.affiliatedAuthorMueller, Klaus-Robert-
dc.identifier.doi10.1371/journal.pcbi.1008710-
dc.identifier.scopusid2-s2.0-85114443187-
dc.identifier.wosid000724165700001-
dc.identifier.bibliographicCitationPLOS COMPUTATIONAL BIOLOGY, v.17, no.9-
dc.relation.isPartOfPLOS COMPUTATIONAL BIOLOGY-
dc.citation.titlePLOS COMPUTATIONAL BIOLOGY-
dc.citation.volume17-
dc.citation.number9-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiochemistry & Molecular Biology-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryBiochemical Research Methods-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.subject.keywordPlusEVOKED-POTENTIALS-
dc.subject.keywordPlusRESPONSES-
dc.subject.keywordPlusCORTEX-
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