|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)|
|会议名称||2017 IEEE International Workshop on Machine Learning for Signal Processing|
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.
|关键词||Individualized Prediction Intelligence Quotient Functional Connectivity Brainnetomr Atlas Sparse|
|作者单位||Institute of Automation, Chinese Academy of Sciences|
|Rongtao Jiang,Shile Qi,Yuhui Du,et al. Predicting Individualized Intelligence Quotient Scores Using Brainnetome-Atlas Based Functional Connectivity[C],2017.|
|2017_MLSP_Jiang_IQ-P（5025KB）||会议论文||开放获取||CC BY-NC-SA||浏览 下载|