Abstract
Background
Obesity is a major health concern linked to chronic conditions such as diabetes and cardiovascular disease. However, most neurological studies have focused on specific metabolic states, limiting understanding of how brain function changes from fasting to satiety. Furthermore, hypothesis-driven approaches may introduce bias and fail to capture complex neural interactions. This study aimed to identify brain connectivity patterns associated with obesity across different metabolic states using a data-driven approach.
Methods
Electroencephalography data were collected from 30 women with obesity and 30 women without obesity over a four-hour period encompassing fasting and post-meal states. All subjects were aged 20 to 65 years. Functional connectivity was calculated from source-localized signals, and a machine learning framework incorporating a feature selection method was applied to identify the most discriminative connectivity features between groups.
Results
Here we show that six connectivity features classify obesity with 95% accuracy across metabolic states. Reduced connectivity are observed within food-reward processing regions in the obese group, with the dorsal anterior cingulate cortex emerging as a central hub. This pattern reflects a persistent alteration in energy prediction and craving regulation that is independent of metabolic state.
Conclusions
These findings demonstrate that disrupted brain connectivity is a fundamental characteristic of obesity. The results highlight the dorsal anterior cingulate cortex as a key region underlying maladaptive reward processing and suggest that targeting this area through neuromodulation therapies may offer a promising intervention for obesity treatment.
Plain Language Summary
Obesity is a growing health issue that affects both the body and the brain. In this study, we wanted to understand how brain activity differs between people with and without obesity, and whether these differences change from fasting to after eating. We recorded brain signals from women with and without obesity while they moved from hunger to fullness. Using computer-based analysis, we found specific patterns of brain communication that could accurately distinguish the two groups. People with obesity showed weaker connections in brain areas related to food reward and self-control, especially in a region called the anterior cingulate cortex. These results suggest that changes in brain connectivity may underlie overeating and could guide future brain-based treatments for obesity.
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Data availability
The datasets generated during this study, including anonymized EEG recordings, anthropometric data, and behavioural measures, are available upon reasonable request due to ethical and privacy restrictions. Access is granted for non-commercial, academic purposes only, subject to approval and a signed data use agreement. Requests should be directed to the corresponding author (yuan.yue@otago.ac.nz). Requests will be reviewed and responded to within four weeks. Use of the data is restricted to the approved research purpose, and redistribution to third parties is not permitted. Source data for Figs. 2–4, 6, and 7 are available on Figshare (https://doi.org/10.6084/m9.figshare.30351280.v3)92.
Code availability
The code used in this study, along with mock datasets, is publicly available at https://github.com/yuan410/ObesityMeta/tree/main. Detailed instructions for running the code are provided in the README file within the repository. The code is also archived on Figshare (https://doi.org/10.6084/.m9.figshare.30351418.v1)93.
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We thank all participants who contributed to this study. This research received no external funding.
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Conceptualization: Y.Y., P.M., D.D.R., J.D., D.B.A., M.H.; Methodology: Y.Y., J.D., D.D.R., P.M., D.B.A., M.H., S.R.; Data collection and processing: P.M., S.R., Y.Y.; Data analysis: Y.Y.; Writing—first draft: Y.Y., P.M., D.D.R.; Writing—review and editing: Y.Y., P.M., D.D.R., J.D., D.B.A., M.H. Code implementation: Y.Y., D.A.C. All authors have critically reviewed and approved the final version of the manuscript.
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Yue, Y., Manning, P., De Ridder, D. et al. Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states. Commun Med (2026). https://doi.org/10.1038/s43856-026-01518-5
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DOI: https://doi.org/10.1038/s43856-026-01518-5
