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<p>Obesity is associated with intrinsic functional reorganization within the brain. However, limited research has utilized resting-state functional connectome models to predict body mass index (BMI) and explore the relationship between BMI-related resting-state functional connectivity (rsFC) and behavioral performance. Least absolute shrinkage and selection operator (LASSO) regression models were developed using the HCP500 dataset (440 subjects) to identify BMI-related rsFC patterns and predict BMI values. The model demonstrating the strongest predictive power was validated on the HCP900 dataset (309 subjects). Additional validation was performed using the HCP1200 (182 subjects), NKI (102 subjects), and MPI-LEMON (151 subjects) datasets. We examined the relationship between BMI-related rsFC sets and performance on the Dimensional Change Card Sort and Delay Discounting tests. Predicted BMI values were significantly correlated with actual BMI values across the HCP1200 and NKI datasets (HCP1200: <i>r</i> = 0.52, <i>p</i> = 8E-14, MAE = 3.30; NKI: <i>r</i> = 0.35, <i>p</i> = 0.0002, MAE = 4.17). The identified BMI-related rsFC sets encompassed brain circuits involved in hemostatic control, executive function, salience processing, motor planning, reward processing, and visual perception. Notably, these rsFC fingerprintings significantly accounted for scores on the delay discounting task. Our findings demonstrate that BMI can be predicted using a functional connectome-based model. Additionally, the identified BMI-related rsFC fingerprintings effectively explained scores on delay discounting tasks, providing new insights into the neural mechanisms associated with overweight and obesity.</p>

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Functional connectome fingerprinting related to BMI and its association with impulsivity

  • 期刊论文

  • 发表日期：

    2025年9月24日

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  • Jizheng Zhao,
  • Hongxing Ning,
  • Jiahui Qiao,
  • Feng Yan

摘要

Obesity is associated with intrinsic functional reorganization within the brain. However, limited research has utilized resting-state functional connectome models to predict body mass index (BMI) and explore the relationship between BMI-related resting-state functional connectivity (rsFC) and behavioral performance. Least absolute shrinkage and selection operator (LASSO) regression models were developed using the HCP500 dataset (440 subjects) to identify BMI-related rsFC patterns and predict BMI values. The model demonstrating the strongest predictive power was validated on the HCP900 dataset (309 subjects). Additional validation was performed using the HCP1200 (182 subjects), NKI (102 subjects), and MPI-LEMON (151 subjects) datasets. We examined the relationship between BMI-related rsFC sets and performance on the Dimensional Change Card Sort and Delay Discounting tests. Predicted BMI values were significantly correlated with actual BMI values across the HCP1200 and NKI datasets (HCP1200: r = 0.52, p = 8E-14, MAE = 3.30; NKI: r = 0.35, p = 0.0002, MAE = 4.17). The identified BMI-related rsFC sets encompassed brain circuits involved in hemostatic control, executive function, salience processing, motor planning, reward processing, and visual perception. Notably, these rsFC fingerprintings significantly accounted for scores on the delay discounting task. Our findings demonstrate that BMI can be predicted using a functional connectome-based model. Additionally, the identified BMI-related rsFC fingerprintings effectively explained scores on delay discounting tasks, providing new insights into the neural mechanisms associated with overweight and obesity.