Deep learning using structural MRI massively improves prediction accuracy of body mass index

Abstract

Obesity is a major public health problem globally and there is considerable interest in the neural mechanisms in food overconsumption. Artificial intelligence (AI), particularly machine learning, has shown promise in characterizing links between brain morphometry and obesity. In 1106 adults, compared to other forms of machine learning, deep learning using 3D convolutional neural networks (3D-CNN) dramatically improves prediction of body mass index (BMI). The 3D-CNN model robustly predicted BMI (R2 =.325), outperforming random forest, elastic net, and tabnet models (R2s<.07) in a lockbox sample. Explainable AI analyses revealed the specific brain regions implicated and these regions were moderately associated with delay discounting, fluid cognition, gait speed, dexterity, and alcohol use. Collectively, these findings reveal the value of deep learning for understanding of the neural basis and motivational processes in the neurobiology of obesity.