Connectome-based prediction of marital quality in husbands' processing of spousal interactions

Marital quality may decrease during the early years of marriage. Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cr...

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Published in:Social cognitive and affective neuroscience 2022-12, Vol.17 (12), p.1055-1067
Main Authors: Ma, Shan-Shan, Zhang, Jin-Tao, Song, Kun-Ru, Zhao, Rui, Fang, Ren-Hui, Wang, Luo-Bin, Yao, Shu-Ting, Hu, Yi-Fan, Jiang, Xin-Ying, Potenza, Marc N, Fang, Xiao-Yi
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Language:English
Subjects:
Connectome
Emotions
Humans
Husband and wife
Machine learning
Marriage - psychology
Married women
Neural networks
Spouses - psychology
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cited_by cdi_FETCH-LOGICAL-c402t-354c554225d0f0d24ff627f5696298b43e62876a3c4910d5c2c994268091284e3
cites cdi_FETCH-LOGICAL-c402t-354c554225d0f0d24ff627f5696298b43e62876a3c4910d5c2c994268091284e3
container_end_page 1067
container_issue 12
container_start_page 1055
container_title Social cognitive and affective neuroscience
container_volume 17
creator Ma, Shan-Shan
Zhang, Jin-Tao
Song, Kun-Ru
Zhao, Rui
Fang, Ren-Hui
Wang, Luo-Bin
Yao, Shu-Ting
Hu, Yi-Fan
Jiang, Xin-Ying
Potenza, Marc N
Fang, Xiao-Yi
description Marital quality may decrease during the early years of marriage. Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cross-sectionally and prospectively, neural responses during marital interactions may provide insight into neural bases underlying marital well-being. The current study applies connectome-based predictive modeling, a recently developed machine-learning approach, to functional magnetic resonance imaging (fMRI) data from both partners of 25 early-stage Chinese couples to examine whether an individual's unique pattern of brain functional connectivity (FC) when responding to spousal interactive behaviors can reliably predict their own and their partners' marital quality after 13 months. Results revealed that husbands' FC involving multiple large networks, when responding to their spousal interactive behaviors, significantly predicted their own and their wives' marital quality, and this predictability showed gender specificity. Brain connectivity patterns responding to general emotional stimuli and during the resting state were not significantly predictive. This study demonstrates that husbands' differences in large-scale neural networks during marital interactions may contribute to their variability in marital quality and highlights gender-related differences. The findings lay a foundation for identifying reliable neuroimaging biomarkers for developing interventions for marital quality early in marriages.
doi_str_mv 10.1093/scan/nsac034
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Establishing models predicting individualized marital quality may help develop timely and effective interventions to maintain or improve marital quality. Given that marital interactions have an important impact on marital well-being cross-sectionally and prospectively, neural responses during marital interactions may provide insight into neural bases underlying marital well-being. The current study applies connectome-based predictive modeling, a recently developed machine-learning approach, to functional magnetic resonance imaging (fMRI) data from both partners of 25 early-stage Chinese couples to examine whether an individual's unique pattern of brain functional connectivity (FC) when responding to spousal interactive behaviors can reliably predict their own and their partners' marital quality after 13 months. 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subjects Connectome
Emotions
Humans
Husband and wife
Machine learning
Marriage - psychology
Married women
Neural networks
Spouses - psychology
title Connectome-based prediction of marital quality in husbands' processing of spousal interactions
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