Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148912
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dc.contributor.authorKashyap, Rajanen_US
dc.contributor.authorBhattacharjee, Sagarikaen_US
dc.contributor.authorYeo, Thomas B. T.en_US
dc.contributor.authorChen, Annabel Shen-Hsingen_US
dc.date.accessioned2021-05-10T06:07:03Z-
dc.date.available2021-05-10T06:07:03Z-
dc.date.issued2019-
dc.identifier.citationKashyap, R., Bhattacharjee, S., Yeo, T. B. T. & Chen, A. S. (2019). Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality. Human Brain Mapping, 41(5), 1261-1273. https://dx.doi.org/10.1002/hbm.24873en_US
dc.identifier.issn1065-9471en_US
dc.identifier.other0000-0002-5967-2173-
dc.identifier.other0000-0002-1540-5516-
dc.identifier.urihttps://hdl.handle.net/10356/148912-
dc.description.abstractPatterns in resting-state fMRI (rs-fMRI) are widely used to characterize the trait effects of brain function. In this aspect, multiple rs-fMRI scans from single subjects can provide interesting clues about the rs-fMRI patterns, though scan-to-scan variability pose challenges. Therefore, rs-fMRI's are either concatenated or the functional connectivity is averaged. This leads to loss of information. Here, we use an alternative way to extract the rs-fMRI features that are common across all the scans by applying common-and-orthogonal-basis-extraction (COBE) technique. To address this, we employed rs-fMRI of 788 subjects from the human connectome project and estimated the common-COBE-component of each subject from the four rs-fMRI runs. Since the common-COBE-component is specific to a subject, the pattern was used to classify the subjects based on the similarity/dissimilarity of the features. The subset of subjects (n = 107) with maximal-COBE-dissimilarity (MCD) was extracted and the remaining subjects (n = 681) formed the COBE-similarity (CS) group. The distribution of weights of the common-COBE-component for the two groups across rs-fMRI networks and subcortical regions was evaluated. We found the weights in the default mode network to be lower in the MCD compared to the CS. We compared the scores of 69 behavioral measures and found six behaviors related to the use of marijuana, illicit drugs, alcohol, and tobacco; and including a measure of antisocial personality to differentiate the two groups. Gender differences were also significant. Altogether the findings suggested that subtypes exist even in healthy control population, and comparison studies (case vs. control) need to be mindful of it.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Medical Research Council (NMRC)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationCBRG/0088/20 15en_US
dc.relationNTU-SUGen_US
dc.relation.ispartofHuman Brain Mappingen_US
dc.rights© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. This is an open access article unde r the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.en_US
dc.subjectSocial sciences::Psychologyen_US
dc.titleMaximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personalityen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Social Sciencesen_US
dc.contributor.schoolLee Kong Chian School of Medicine (LKCMedicine)en_US
dc.contributor.researchCentre for Research and Development in Learning (CRADLE)en_US
dc.identifier.doi10.1002/hbm.24873-
dc.description.versionPublished versionen_US
dc.identifier.pmid31773817-
dc.identifier.scopus2-s2.0-85075720156-
dc.identifier.issue5en_US
dc.identifier.volume41en_US
dc.identifier.spage1261en_US
dc.identifier.epage1273en_US
dc.subject.keywordsAlcoholen_US
dc.subject.keywordsAntisocial Personality Problemsen_US
dc.description.acknowledgementSingapore National Research Foundation (NRF)Fellowship; NUS YIA; Singapore NMRC, Grant/Award Number: CBRG/0088/20 15; NUS SOMAspiration Fund, Grant/Award Number:R185000271720; National University of SingaporeStrategic Research, Grant/Award Number:DPRT/944/09/14; Nanyang TechnologicalUniversity Start-Up Grant (NTU-SUG).en_US
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