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2019 | OriginalPaper | Chapter

Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets

Authors : Yukai Zou, Ikbeom Jang, Timothy G. Reese, Jinxia Yao, Wenbin Zhu, Joseph V. Rispoli

Published in: Adolescent Brain Cognitive Development Neurocognitive Prediction

Publisher: Springer International Publishing

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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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previous chapter Adolescent Fluid Intelligence Prediction from Regional Brain Volumes and Cortical Curvatures Using BlockPC-XGBoost

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Title: Cortical and Subcortical Contributions to Predicting Intelligence Using 3D ConvNets
Authors: Yukai Zou
Ikbeom Jang
Timothy G. Reese
Jinxia Yao
Wenbin Zhu
Joseph V. Rispoli
Publisher: Springer International Publishing
Book: Adolescent Brain Cognitive Development Neurocognitive Prediction
Print ISBN: 978-3-030-31900-7

Electronic ISBN: 978-3-030-31901-4

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-31901-4_21

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