Finkelstein Ofek, Levakov Gidon, Kaplan Alon, Zelicha Hila, Meir Anat Yaskolka, Rinott Ehud, Tsaban Gal, Witte Anja Veronica, Blüher Matthias, Stumvoll Michael, Shelef Ilan, Shai Iris, Riklin Raviv Tammy, Avidan Galia
Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
The Health & Nutrition Innovative International Research Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel.
Hum Brain Mapp. 2024 Feb 15;45(3):e26595. doi: 10.1002/hbm.26595.
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.
肥胖与对大脑的负面影响有关。我们利用人工智能（AI）工具来探究超重人群生活方式干预后临床测量值的差异是否能在脑形态学上得到体现。在DIRECT-PLUS临床试验中，符合代谢综合征标准的参与者接受了为期18个月的生活方式干预。在干预前后采集了脑部结构MRI。我们利用一个集成学习框架来预测体重指数（BMI）分数，该分数对应于从脑部MRI得出的与肥胖相关的临床测量值。我们发现，特定患者BMI预测值的降低与实际体重减轻相关，并且与对照组相比，积极饮食组的降低幅度显著更高。此外，可解释人工智能（XAI）图谱突出显示了对BMI预测有贡献的脑区，这些脑区与与年龄预测相关的脑区不同。我们的DIRECT-PLUS分析结果表明，预测的BMI及其降低是肥胖相关脑改变和体重减轻的独特神经生物标志物。
