Cortical and subcortical contributions to predicting intelligence using 3D ConvNets

Zou, Yukai, Jang, Ikbeom, Reese, Timothy G, Yao, Jinxia, Zhu, Wenbin and Rispoli, Joseph V (2019) Cortical and subcortical contributions to predicting intelligence using 3D ConvNets. Pohl, Kilian M, Thompson, Wesley K, Adeli, Ehsan and Linguraru, Marius George (eds.) In Adolescent Brain Cognitive Development Neurocognitive Prediction. Springer. pp. 176-185 .

Abstract

We present a novel framework using 3D convolutional neural networks to predict residualized fluid intelligence scores in the MICCAI 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.

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Contributors

Author: Yukai Zou

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