Sex differences in the brain: Divergent results from traditional machine learning and convolutional networks
Leo Brueggeman, Taylor Thomas, Tanner Koomar, Brady Hoskins, Jacob Michaelson
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Neuroimaging research has begun adopting deep learning to model structural differences in the brain. This is a break from previous approaches that rely on derived features from brain MRI, such as regional thicknesses or volumes. To date, most studies employ either deep learning based models or traditional machine learning volume based models. Because of this split, it is unclear which approach is yielding better predictive performance or if the two approaches will lead to different neuroanatomical conclusions, potentially even when applied to the same datasets. In the present study, we carry out the largest single study of sex differences in the brain using 21,390 UK Biobank T1-weighted brain MRIs analyzed through both traditional and 3D convolutional neural network models. Through comparing performances, we find that 3D-CNNs outperform traditional machine learning models using volumetric features. Through comparing regions highlighted by both approaches, we find poor overlap in conclusions derived from traditional machine learning and 3D-CNN based models. In summary, we find that 3D-CNNs show exceptional predictive performance, but may highlight neuroanatomical regions different from what would be found by volume-based approaches.