Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach.
Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological limitations in previous studies, a consistent pic...
| Publicado en: | Cognitive, Affective & Behavioral Neuroscience Vol. 23; no. 4; pp. 1095 - 1113 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169750575&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169750575 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15307026 NA4 jtl: Cognitive, Affective & Behavioral Neuroscience issn: 15307026 maglogo: N pubinfo: dt: Aug2023 vid: 23 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169750575 162726275 10.3758/s13415-023-01076-6 169750575 ppf: 1095 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach. aug: au: Ghomroudi, Parisa Ahmadi Scaltritti, Michele Grecucci, Alessandro affil: Clinical and Affective Neuroscience Lab, Department of Psychology and Cognitive Sciences – DiPSCo, University of Trento, Rovereto, Italy sug: ab: Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological limitations in previous studies, a consistent picture of the neural correlates related to the individual differences in their habitual use remains elusive. To address these issues, the present study applied a combination of unsupervised and supervised machine learning algorithms to the structural MRI scans of 128 individuals. First, unsupervised machine learning was used to separate the brain into naturally grouping grey matter circuits. Then, supervised machine learning was applied to predict individual differences in the use of different strategies of emotion regulation. Two predictive models, including structural brain features and psychological ones, were tested. Results showed that a temporo-parahippocampal-orbitofrontal network successfully predicted the individual differences in the use of reappraisal. Differently, insular and fronto-temporo-cerebellar networks successfully predicted suppression. In both predictive models, anxiety, the opposite strategy, and specific emotional intelligence factors played a role in predicting the use of reappraisal and suppression. This work provides new insights regarding the decoding of individual differences from structural features and other psychologically relevant variables while extending previous observations on the neural bases of emotion regulation strategies. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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