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

TL;DR

This study uses deep learning on longitudinal adolescent MRI data to predict gender with high accuracy, revealing distinct brain structural patterns and changes over two years, especially in frontal, temporal, and insular regions.

Contribution

It demonstrates the effectiveness of deep learning in predicting gender from adolescent brain MRI data and identifies specific brain regions associated with gender and developmental changes.

Findings

Gender prediction accuracy exceeds 97%

Brain regions like frontal and temporal lobes are most discriminative

Structural brain changes related to gender increase with age

Abstract

Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks. However, most prior research in this area has focused on neuroimaging data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a large longitudinal development study, we examine structural MRI data to predict gender and identify gender-related changes in brain structure. Results demonstrate that gender prediction accuracy is exceptionally high (>97%) with training epochs >200 and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal areas and the temporal lobe. When evaluating gender predictive changes specific to a two-year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Our findings show a robust gender-related structural brain change pattern, even over a small age range. This suggests that it might be possible to study how the brain changes during adolescence by looking at how these changes are related to different behavioral and environmental factors.