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A Set of Functional Brain Networks for the Comprehensive Evaluation of Human Characteristics

Authors
Sung, Yul-WanKawachi, YousukeChoi, Uk-SuKang, DaehunAbe, ChihiroOtomo, YukiOgawa, Seiji
Issue Date
14-Mar-2018
Publisher
FRONTIERS MEDIA SA
Keywords
resting-state fMRI; functional network; neuronal plasticity; human characteristics; psychometric parameters
Citation
FRONTIERS IN NEUROSCIENCE, v.12
Journal Title
FRONTIERS IN NEUROSCIENCE
Volume
12
URI
https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/3970
DOI
10.3389/fnins.2018.00149
ISSN
1662-453X
Abstract
Many human characteristics must be evaluated to comprehensively understand an individual, and measurements of the corresponding cognition/behavior are required. Brain imaging by functional MRI (fMRI) has been widely used to examine brain function related to human cognition/behavior. However, few aspects of cognition/behavior of individuals or experimental groups can be examined through task-based fMRI. Recently, resting state fMRI (rs-fMRI) signals have been shown to represent functional infrastructure in the brain that is highly involved in processing information related to cognition/behavior. Using rs-fMRI may allow diverse information about the brain through a single MRI scan to be obtained, as rs-fMRI does not require stimulus tasks. In this study, we attempted to identify a set of functional networks representing cognition/behavior that are related to a wide variety of human characteristics and to evaluate these characteristics using rs-fMRI data. If possible, these findings would support the potential of rs-fMRI to provide diverse information about the brain. We used resting-state fMRI and a set of 130 psychometric parameters that cover most human characteristics, including those related to intelligence and emotional quotients and social ability/skill. We identified 163 brain regions by VBM analysis using regression analysis with 130 psychometric parameters. Next, using a 163 x 163 correlation matrix, we identified functional networks related to 111 of the 130 psychometric parameters. Finally, we made an 8-class support vector machine classifiers corresponding to these 111 functional networks. Our results demonstrate that rs-fMRI signals contain intrinsic information about brain function related to cognition/behaviors and that this set of 111 networks/classifiers can be used to comprehensively evaluate human characteristics.
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