Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets

摘要

We present a novel framework using 3D convolutional neural networks to predict residualized fluid intelligence scores in the MIC-CAI 2019 Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge datasets. Using gray matter segmentations from T1-weighted MRI volumes as inputs, our framework identified several cortical and subcortical brain regions where the predicted errors were lower than random guessing in the validation set (mean squared error = 71.5252), and our final outcomes (mean squared error = 70.5787 in the validation set, 92.7407 in the test set) were comprised of the median scores predicted from these regions.