Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention.
Ofek FinkelsteinGidon LevakovAlon KaplanHila ZelichaAnat Yaskolka MeirEhud RinottGal TsabanAnja Veronica WitteMatthias BlüherMichael StumvollIlan ShelefIris ShaiTammy Riklin RavivGalia AvidanPublished in: Human brain mapping (2024)
Obesity is associated with negative effects on the brain. We exploit Artificial Intelligence (AI) tools to explore whether differences in clinical measurements following lifestyle interventions in overweight population could be reflected in brain morphology. In the DIRECT-PLUS clinical trial, participants with criterion for metabolic syndrome underwent an 18-month lifestyle intervention. Structural brain MRIs were acquired before and after the intervention. We utilized an ensemble learning framework to predict Body-Mass Index (BMI) scores, which correspond to adiposity-related clinical measurements from brain MRIs. We revealed that patient-specific reduction in BMI predictions was associated with actual weight loss and was significantly higher in active diet groups compared to a control group. Moreover, explainable AI (XAI) maps highlighted brain regions contributing to BMI predictions that were distinct from regions associated with age prediction. Our DIRECT-PLUS analysis results imply that predicted BMI and its reduction are unique neural biomarkers for obesity-related brain modifications and weight loss.
Keyphrases
- weight loss
- body mass index
- metabolic syndrome
- artificial intelligence
- weight gain
- white matter
- resting state
- bariatric surgery
- physical activity
- deep learning
- randomized controlled trial
- clinical trial
- functional connectivity
- roux en y gastric bypass
- machine learning
- cardiovascular disease
- magnetic resonance imaging
- magnetic resonance
- multiple sclerosis
- gastric bypass
- study protocol
- open label