| Title: | Deep learning-based BMI inference from structural brain MRI reflects brain alterations following lifestyle intervention |
| Journal: | Human Brain Mapping |
| Published: | 15 Feb 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38375968/ |
| DOI: | https://doi.org/10.1002/hbm.26595 |
| URL: | http://dx.doi.org/10.1002/hbm.26595 |
Publication 8731
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Abstract
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.</p>
14 Authors
- Ofek Finkelstein
- Gidon Levakov
- Alon Kaplan
- Hila Zelicha
- Anat Yaskolka Meir
- Ehud Rinott
- Gal Tsaban
- Anja Veronica Witte
- Matthias Blüher
- Michael Stumvoll
- Ilan Shelef
- Iris Shai
- Tammy Riklin Raviv
- Galia Avidan
Enabling scientific discoveries that improve human health