Machine learning approaches provide convenient autonomous object classification in medical imaging domains. This study examines the utility of convolutional neural networks in predicting individual differences in cognition from the resting-state functional connectome. We observed significant contributions from the subcortical areas (including hippocampus) and their interactions with the cortical default mode network to the training progress. Our results demonstrate that an AI-based model can predict an individual's EM scores.
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