Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity

Rongtao Jiang, Shile Qi, Yuhui Du, Weizheng Yan, Vince D. Calhoun, Tianzi Jiang, Jing Sui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings
EditorsNaonori Ueda, Jen-Tzung Chien, Tomoko Matsui, Jan Larsen, Shinji Watanabe
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Electronic)9781509063413
DOIs
StatePublished - Dec 5 2017
Externally publishedYes
Event2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Tokyo, Japan
Duration: Sep 25 2017Sep 28 2017

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2017-September
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017
CountryJapan
CityTokyo
Period9/25/179/28/17

Keywords

  • Brainnetomr atlas
  • Functional connectivity
  • Individualized prediction
  • Intelligence quotient
  • Sparse learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Jiang, R., Qi, S., Du, Y., Yan, W., Calhoun, V. D., Jiang, T., & Sui, J. (2017). Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity. In N. Ueda, J-T. Chien, T. Matsui, J. Larsen, & S. Watanabe (Eds.), 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings (pp. 1-6). (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/MLSP.2017.8168150