What question did this study set out to answer?

To identify abnormal functional connectivity in obesity across various metabolic states using machine learning techniques.

March 13, 2026Open Access

Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states

Key Points

To identify abnormal functional connectivity in obesity across various metabolic states using machine learning techniques.
Utilized machine learning algorithms to analyze brain connectivity patterns.
Focused on individuals with varying metabolic states related to obesity.
Identified key brain regions involved in reward processing.
Disrupted connectivity is a characteristic feature of obesity.
The dorsal anterior cingulate cortex is highlighted as crucial in maladaptive reward processing.
Neuromodulation therapies targeting this area may provide effective obesity treatment options.

Abstract

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.

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Authors

Yuan Yue

P. J. Manning

Dirk De Ridder

University of Otago

Journals

Communications Medicine

Actions

Institutions

University of Otago

References and Citations

Connected Papers

Building similarity graph...

Analyzing shared references across papers

Machine learning-based identification of abnormal functional connectivity in obesity across different metabolic states

Key Points

Abstract

Citation Network

Connected Papers

Discussion

Authors

Journals

Actions

Institutions

References and Citations

Citation Network

Connected Papers

Discussion

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