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Deep learning algorithms for predicting neuroimaging data have shownconsiderable promise in various applications. Prior work has demonstrated thatdeep learning models that take advantage of the data's 3D structure canoutperform standard machine learning on several learning tasks. However, mostprior research in this area has focused on neuroimaging data from adults.Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a largelongitudinal development study, we examine structural MRI data to predictgender and identify gender-related changes in brain structure. Resultsdemonstrate that gender prediction accuracy is exceptionally high (>97%) withtraining epochs >200 and that this accuracy increases with age. Brain regionsidentified as the most discriminative in the task under study includepredominantly frontal areas and the temporal lobe. When evaluating genderpredictive changes specific to a two-year increase in age, a broader set ofvisual, cingulate, and insular regions are revealed. Our findings show a robustgender-related structural brain change pattern, even over a small age range.This suggests that it might be possible to study how the brain changes duringadolescence by looking at how these changes are related to different behavioraland environmental factors.

Prediction of Gender from Longitudinal MRI data via Deep Learning on  Adolescent Data Reveals Unique Patterns Associated with Brain Structure and  Change over a Two-year Period