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