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  • Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task

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    Author(s)
    Krohne, Laerke Gebser
    Wang, Yi
    Hinrich, Jesper L
    Moerup, Morten
    Chan, Raymond CK
    Madsen, Kristoffer H
    Griffith University Author(s)
    Chan, Raymond
    Year published
    2019
    Metadata
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    Abstract
    Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia‐spectrum ...
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    Previous studies have suggested that the degree of social anhedonia reflects the vulnerability for developing schizophrenia. However, only few studies have investigated how functional network changes are related to social anhedonia. The aim of this fMRI study was to classify subjects according to their degree of social anhedonia using supervised machine learning. More specifically, we extracted both spatial and temporal network features during a social cognition task from 70 subjects, and used support vector machines for classification. Since impairment in social cognition is well established in schizophrenia‐spectrum disorders, the subjects performed a comic strip task designed to specifically probe theory of mind (ToM) and empathy processing. Features representing both temporal (time series) and network dynamics were extracted using task activation maps, seed region analysis, independent component analysis (ICA), and a newly developed multi‐subject archetypal analysis (MSAA), which here aimed to further bridge aspects of both seed region analysis and decomposition by incorporating a spotlight approach.We found significant classification of subjects with elevated levels of social anhedonia when using the times series extracted using MSAA, indicating that temporal dynamics carry important information for classification of social anhedonia. Interestingly, we found that the same time series yielded the highest classification performance in a task classification of the ToM condition. Finally, the spatial network corresponding to that time series included both prefrontal and temporal‐parietal regions as well as insula activity, which previously have been related schizotypy and the development of schizophrenia.
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    Journal Title
    Human Brain Mapping
    DOI
    https://doi.org/10.1002/hbm.24751
    Copyright Statement
    © 2019 Wiley Periodicals Inc. This is the peer reviewed version of the following article: Classification of social anhedonia using temporal and spatial network features from a social cognition fMRI task, Human Brain Mapping, which has been published in final form at 10.1002/hbm.24751. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-828039.html)
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Neurosciences
    Cognitive Sciences
    archetypical analysis
    decomposition
    functional connectivity
    social anhedonia
    support vector classification
    Publication URI
    http://hdl.handle.net/10072/386699
    Collection
    • Journal articles

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