Abstract
Background
Prior obesity neuroimaging studies used univariate methods and small samples, limiting reproducibility. Employing a large-scale dataset and a two-stage machine learning framework, we identified neuroanatomical signatures of obesity and evaluated their population-level associations with delay discounting impulsivity
Methods
We enrolled 243 young adults with obesity and 475 healthy-weight controls from the Human Connectome Project S1200 dataset. Cortical surface area, cortical thickness, and subcortical gray matter volume were extracted using surface-based morphometry. Support vector machine (SVM) classifiers discriminated obesity from healthy weight based on neuroanatomical features, with interpretability assessed via SHapley Additive exPlanations (SHAP). Partial correlations evaluated associations between the 15 SVM-selected features and the area under the curve (AUC) of delay discounting (DD) in the full sample. Normative modeling examined if the obesity group deviated from brain-structure-based expectations of AUC-DD.
Results
The neuroanatomical-only SVM achieved a receiver operating characteristic (ROC-AUC) of 0.648; adding demographic and cognitive covariates improved it to 0.740. SHAP highlighted the superior parietal, entorhinal, medial orbitofrontal, posterior cingulate, and rostral anterior cingulate cortices. Ten of the top 15 features were significantly associated with AUC-DD, and all in the expected direction. The obesity group showed systematically higher DD impulsivity than predicted impulsivity by brain structures alone.
Conclusions
Distributed neuroanatomical features in prefrontal, medial temporal, cingulate, and parietal cortices discriminated obesity and showed convergent population-level associations with delay discounting impulsivity across group, correlational, and normative analyses
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Data availability
All data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University in St. Louis. The authors are grateful to the Human Connectome Project for open access to its data. The HCP S1200 data are publicly available at https://www.humanconnectome.org/. Analysis code is available at https://github.com/HuiXu-Eric/Identification-of-critical-brain-rigions-for-HCP-s1200.
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Acknowledgements
We thank the Human Connectome Project for sharing this dataset for this study
Funding
This study was funded by Philosophy and Social Sciences Foundation of Zhejiang Province (26NDJC115YB), and Basic Research Fund Grant Projects of Wenzhou Medical University (KYYW202541)
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Author notes
These authors contributed equally: Guanjinghui Xu, Junhao He, Junjie Zhao
Authors and Affiliations
School of Mental Health, Wenzhou Medical University, Wenzhou, China
Guanjinghui Xu, Junhao He, Junjie Zhao & Yang Wang
The Affiliated Kangning Hospital of Wenzhou Medical University, Zhejiang Provincial Clinical Research Center for Mental Health, Wenzhou, China
Hui Xu
Huzhou Third Municipal Hospital, the Affiliated Hospital of Wenzhou Medical University, Huzhou, China
Hui Xu
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Contributions
GX: Conceptualization, Methodology, Data curation, Writing—reviewing and editing; JH: Conceptualization, Methodology, Data curation, Writing—original draft; JZ: Conceptualization, Methodology, Data curation, Writing—original draft; YW: Methodology, Writing—original draft; HX: Conceptualization, Methodology, Data curation, Formal analysis, Software, Visualization, Investigation, Supervision, Funding, Writing—original draft, Writing—reviewing and editing
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Competing interests
The authors declare no competing interests
Ethical approval
This study was a secondary analysis of de-identified, publicly available data from the Human Connectome Project (HCP) S1200 release. Ethical approval for the original HCP data collection was obtained by the HCP consortium from the Institutional Review Board at Washington University in St. Louis (IRB #201204036). Ethical approval for this secondary analysis was obtained from the Institutional Review Board of Wenzhou Medical University
Informed consent
All participants provided written informed consent as part of the original HCP protocol approved by the Washington University IRB. No additional consent procedures were conducted for this secondary analysis. Ethics approval and consent to participate. This study was approved and consented by the Ethics Committee of Wenzhou Medical University in accordance with the Declaration of Helsinki (approval number: 2026100)
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Xu, G., He, J., Zhao, J. et al. Identification of critical brain regions for young adults with obesity and their relationships with impulsivity using machine learning based on neuroanatomical features.
Int J Obes (2026). https://doi.org/10.1038/s41366-026-02184-2
Received:09 March 2026
Revised:22 July 2026
Accepted:31 July 2026
Published:11 August 2026
Version of record:11 August 2026
DOI
:https://doi.org/10.1038/s41366-026-02184-2
