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Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning

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Title
Identifying subgroups of eating behavior traits unrelated to obesity using functional connectivity and feature representation learning
Author(s)
Hyoungshin Choi; Kyoungseob Byeon; Jong-eun Lee; Seok-Jun Hong; Bo-yong Park; Hyunjin Park
Publication Date
2024-01
Journal
Human Brain Mapping, v.45, no.1
Publisher
John Wiley & Sons Inc.
Abstract
Eating behavior is highly heterogeneous across individuals and cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors measured by a self-assessment instrument using 424 healthy adults (mean +/- standard deviation [SD] age = 47.07 +/- 18.89 years; 67% female). We generated low-dimensional representations of functional connectivity using resting-state functional magnetic resonance imaging and estimated latent features using the feature representation capabilities of an autoencoder by nonlinearly compressing the functional connectivity information. The clustering approaches applied to latent features identified three distinct subgroups. The subgroups exhibited different levels of hunger traits, while their body mass indices were comparable. The results were replicated in an independent dataset consisting of 212 participants (mean +/- SD age = 38.97 +/- 19.80 years; 35% female). The model interpretation technique of integrated gradients revealed that the between-group differences in the integrated gradient maps were associated with functional reorganization in heteromodal association and limbic cortices and reward-related subcortical structures such as the accumbens, amygdala, and caudate. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity. We systematically investigated the heterogeneity of eating behavior traits of healthy adults using unsupervised machine learning and functional connectivity. We identified three distinct subgroups showing different eating behavior traits independent of the degree of obesity.image
URI
https://pr.ibs.re.kr/handle/8788114/14910
DOI
10.1002/hbm.26581
ISSN
1065-9471
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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