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

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  3. Transcranial direct current stimulation over the sensory-motor regions inhibits gamma synchrony

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  4. Trait Openness and serotonin 2A receptors in healthy volunteers: A positron emission tomography study

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  1. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data

    Research output: Contribution to journalJournal articleResearchpeer-review

  2. Accessibility of cortical regions to focal TES: Dependence on spatial position, safety, and practical constraints

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  3. Electric field simulations for transcranial brain stimulation using FEM: an efficient implementation and error analysis

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  4. No trace of phase: Corticomotor excitability is not tuned by phase of pericentral mu-rhythm

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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.

Original languageEnglish
JournalHuman Brain Mapping
Volume40
Issue number17
Pages (from-to)4965-4981
Number of pages17
ISSN1065-9471
DOIs
Publication statusPublished - 1 Dec 2019

    Research areas

  • archetypical analysis, decomposition, functional connectivity, social anhedonia, support vector classification

ID: 57797888