Prior studies show that obesity is associated with accelerated brain aging and specific patterns of brain atrophy. Finerscale mapping of the effects of obesity on the brain would help to understand how it promotes or interacts with disease effects, but so far, the influence of the obesity on finer-scale maps of anatomy remains unclear. In this study, we propose a deep transfer learning network based on Optimal Mass Transport (OMTNet) to classify individuals with normal versus overweight/obese body mass index (BMI) using vertex-wise brain shape metrics extracted from structural MRI scans from the UK Biobank study. First, an area-preserving mapping was used to project 3D brain surface meshes onto 2D planar meshes. Vertex-wise maps of brain metrics such as cortical thickness were mapped into 2D planar images for each brain surface extracted from each person’s MRI scan. Second, several popular networks pretrained on the ImageNet database, i.e., VGG19, ResNet152 and DenseNet201, were used for transfer learning of brain shape metrics. We combined all shape metrics and generated a metric ensemble classification, and then combined all three networks and generated a network ensemble classification. The results reveal that transfer learning always outperforms direct learning, and we obtained accuracies of 65.6±0.7% and 62.7±0.7% for transfer and direct learning in the network ensemble classification, respectively. Moreover, surface area and cortical thickness, especially in the left hemisphere, consistently achieved the highest classification accuracies, together with subcortical shape metrics. The findings suggest a significant and classifiable influence of obesity on brain shape. Our proposed OMTNet method may offer a powerful transfer learning framework that can be extended to other vertex-wise brain structural and functional imaging measures.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with atypical cortical maturation leading to a deficiency in social cognition and language. Numerous studies have attempted to classify ASD using brain measurements such as cortical thickness, surface area, or volume with promising results. However, the underpowered sample sizes of these studies limit external validity and generalizability at the population level. Large scale collaborations such as Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) or the Autism Brain Imaging Data Exchange (ABIDE) aim to bring together like-minded scientists to further improve investigations into brain disorders. To the best of our knowledge, this study represents the largest classification analysis for detection of ASD vs. healthy age and sex matched controls using cortical thickness brain parcellations and intracranial volume normalized surface area and subcortical volumes. We were able to increase classification accuracy overall from 56% to 60% and for females only by 6%. These novel findings using Evolving Partitions to Improve Connectomics (EPIC) underscore the importance of large-scale data-driven approaches and collaborations in the discovery of brain disorders.