TY - GEN
T1 - Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity
AU - Jiang, Rongtao
AU - Qi, Shile
AU - Du, Yuhui
AU - Yan, Weizheng
AU - Calhoun, Vince D.
AU - Jiang, Tianzi
AU - Sui, Jing
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/5
Y1 - 2017/12/5
N2 - 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.
AB - 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.
KW - Brainnetomr atlas
KW - Functional connectivity
KW - Individualized prediction
KW - Intelligence quotient
KW - Sparse learning
UR - http://www.scopus.com/inward/record.url?scp=85042298322&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042298322&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2017.8168150
DO - 10.1109/MLSP.2017.8168150
M3 - Conference contribution
AN - SCOPUS:85042298322
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
SP - 1
EP - 6
BT - 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
A2 - Ueda, Naonori
A2 - Chien, Jen-Tzung
A2 - Matsui, Tomoko
A2 - Larsen, Jan
A2 - Watanabe, Shinji
PB - IEEE Computer Society
T2 - 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
Y2 - 25 September 2017 through 28 September 2017
ER -