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

• Published in: Adolescent Brain Cognitive Development Neurocognitive Prediction Vol. 11791; pp. 167 - 175
• Main Authors: Li, Tengfei, Wang, Xifeng, Luo, Tianyou, Yang, Yue, Zhao, Bingxin, Yang, Liuqing, Zhu, Ziliang, Zhu, Hongtu
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: ABCD; Block PCA; Fluid intelligence; XGBoost
• ISBN: 3030319008; 9783030319007
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-31901-4_20

From the ABCD dataset, we discover that besides the gray matter volume of cortical regions, other measures such as the mean cortical curvature, white matter volume and subcortical volume exhibit additional capabilities in the prediction of the pre-residulized fluid intelligence scores for adolescents. The MSE and R-square on validation dataset are improved from 70.65 and 0.0175 to 69.39 and 0.0350, respectively, comparing with using mostly the grey matter volume provided by the challenge organizer. Specifically, by employing a BlockPC-XGBoost framework we discover the following predictors in reducing the MSE on validation set: the gray matter volume of right posterior cingulate gyrus and left caudate nucleus, the entorhinal white matter volume of the left hemisphere, the number of detected surface holes, the globus pallidus volume, the mean curvatures of precentral gyrus, postcentral gyrus and Banks of Superior Temporal Sulcus.