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

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 diffe

SUBMITTER: Choi H 

PROVIDER: S-EPMC10789215 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature