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Predicting Individualized Intelligence Quotient Scores Using Brainnetome-Atlas Based Functional Connectivity
Rongtao Jiang; Shile Qi; Yuhui Du; Weizheng Yan; Vince D. Calhoun; Tianzi Jiang; Sui Jing(隋婧)
2017
Conference Name2017 IEEE International Workshop on Machine Learning for Signal Processing
Conference Date2017/9/25-28
Conference PlaceTokyo,Japan
Abstract
Variation in several brain regions and neural parameters is associated with intelligence. In this study, we adopted functional connectivity (FC) based on Brainnetome-atlas to predict the intelligence quotient (IQ) scores quantitatively with a prediction framework incorporating advanced feature selection and regression methods. We compared prediction performance of five regression models and evaluated the effectiveness of feature selection. The best prediction performance was achieved by ReliefF+LASSO, by which correlations of r=0.72 and r=0.46 between prediction and true values were obtained for 174 female and 186 male subjects respectively in a leave-one-out-cross-validation, suggesting that for female subjects, a better prediction of IQ scores can be achieved using precise FCs. Further, weight analysis revealed the most predictive FCs and the relevant regions. Results support the hypothesis that intelligence is characterized by interaction between multiple brain regions, especially the parieto-frontal integration theory implicated areas. This study facilitates our understanding of the biological basis of intelligence by individualized prediction.
KeywordIndividualized Prediction Intelligence Quotient Functional Connectivity Brainnetomr Atlas Sparse
Indexed BySCI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20783
Collection脑网络组研究中心
Corresponding AuthorSui Jing(隋婧)
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Rongtao Jiang,Shile Qi,Yuhui Du,et al. Predicting Individualized Intelligence Quotient Scores Using Brainnetome-Atlas Based Functional Connectivity[C],2017.
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