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Dynamic Causal Modeling of Hippocampal Activity Measured via Mesoscopic Voltage-Sensitive Dye Imaging

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dc.contributor.author박해정-
dc.date.accessioned2020-06-17T00:47:07Z-
dc.date.available2020-06-17T00:47:07Z-
dc.date.issued2020-06-
dc.identifier.issn1053-8119-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/176120-
dc.description.abstractThe aim of this paper is to present a dynamic causal modeling (DCM) framework for hippocampal activity measured via voltage-sensitive dye imaging (VSDI). We propose a DCM model of the hippocampus that summarizes interactions between the hilus, CA3 and CA1 regions. The activity of each region is governed via a neuronal mass model with two inhibitory and one/two excitatory neuronal populations, which can be linked to measurement VSDI by scaling neuronal activity. To optimize the model structure for the hippocampus, we propose two Bayesian schemes: Bayesian hyperparameter optimization to estimate the unknown electrophysiological properties necessary for constructing a mesoscopic hippocampus model; and Bayesian model reduction to determine the parameterization of neural properties, and to test and include potential connections (morphologically inferred without direct evidence yet) in the model by evaluating group-level model evidence. The proposed method was applied to model spatiotemporal patterns of accumulative responses to consecutive stimuli in separate groups of wild-type mice and epileptic aristaless-related homeobox gene (Arx) conditional knock-out mutant mice (Arx-/+;Dlx5/6CRE-IRES-GFP) in order to identify group differences in the effective connectivity within the hippocampus. The causal role of each group-differing connectivity in generating mutant-like responses was further tested. The group-level analysis identified altered intra- and inter-regional effective connectivity, some of which are crucial for explaining mutant-like responses. The modelling results for the hippocampal activity suggest the plausibility of the proposed mesoscopic hippocampus model and the usefulness of utilizing the Bayesian framework for model construction in the mesoscale modeling of neural interactions using DCM.-
dc.description.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherAcademic Press-
dc.relation.isPartOfNEUROIMAGE-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleDynamic Causal Modeling of Hippocampal Activity Measured via Mesoscopic Voltage-Sensitive Dye Imaging-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Nuclear Medicine (핵의학교실)-
dc.contributor.googleauthorJiyoung Kang-
dc.contributor.googleauthorKyesam Jung-
dc.contributor.googleauthorJinseok Eo-
dc.contributor.googleauthorJunho Son-
dc.contributor.googleauthorHae-Jeong Park-
dc.identifier.doi10.1016/j.neuroimage.2020.116755-
dc.contributor.localIdA01730-
dc.relation.journalcodeJ02332-
dc.identifier.eissn1095-9572-
dc.identifier.pmid32199955-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1053811920302421-
dc.subject.keywordComputational modeling-
dc.subject.keywordDynamic causal modeling-
dc.subject.keywordEffective connectivity-
dc.subject.keywordHippocampus-
dc.subject.keywordNeural population analysis-
dc.subject.keywordVoltage sensitive dye image-
dc.contributor.alternativeNamePark, Hae Jeong-
dc.contributor.affiliatedAuthor박해정-
dc.citation.volume213-
dc.citation.startPage116755-
dc.identifier.bibliographicCitationNEUROIMAGE, Vol.213 : 116755, 2020-06-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Nuclear Medicine (핵의학교실) > 1. Journal Papers

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