Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality

Patterns 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'...

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Main Authors: Kashyap, Rajan, Bhattacharjee, Sagarika, Yeo, Thomas B. T., Chen, Annabel Shen-Hsing
Other Authors: School of Social Sciences
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/148912
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spelling sg-ntu-dr.10356-1489122023-03-05T15:35:01Z Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality Kashyap, Rajan Bhattacharjee, Sagarika Yeo, Thomas B. T. Chen, Annabel Shen-Hsing School of Social Sciences Lee Kong Chian School of Medicine (LKCMedicine) Centre for Research and Development in Learning (CRADLE) Social sciences::Psychology Alcohol Antisocial Personality Problems Patterns 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. Nanyang Technological University National Medical Research Council (NMRC) National Research Foundation (NRF) Published version Singapore 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). 2021-05-10T06:07:03Z 2021-05-10T06:07:03Z 2019 Journal Article Kashyap, 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.24873 1065-9471 0000-0002-5967-2173 0000-0002-1540-5516 https://hdl.handle.net/10356/148912 10.1002/hbm.24873 31773817 2-s2.0-85075720156 5 41 1261 1273 en CBRG/0088/20 15 NTU-SUG Human Brain Mapping © 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. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Social sciences::Psychology
Alcohol
Antisocial Personality Problems
spellingShingle Social sciences::Psychology
Alcohol
Antisocial Personality Problems
Kashyap, Rajan
Bhattacharjee, Sagarika
Yeo, Thomas B. T.
Chen, Annabel Shen-Hsing
Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
description Patterns 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.
author2 School of Social Sciences
author_facet School of Social Sciences
Kashyap, Rajan
Bhattacharjee, Sagarika
Yeo, Thomas B. T.
Chen, Annabel Shen-Hsing
format Article
author Kashyap, Rajan
Bhattacharjee, Sagarika
Yeo, Thomas B. T.
Chen, Annabel Shen-Hsing
author_sort Kashyap, Rajan
title Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
title_short Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
title_full Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
title_fullStr Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
title_full_unstemmed Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
title_sort maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
publishDate 2021
url https://hdl.handle.net/10356/148912
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