Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets

Connectome-based predictive modeling (CPM; Finn et al., 2015; Shen et al., 2017) was recently developed to predict individual differences in traits and behaviors, including fluid intelligence (Finn et al., 2015) and sustained attention (Rosenberg et al., 2016a), from functional brain connectivity (F...

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Fla.), 2018-02, Vol.167, p.11-22
Main Authors:	Yoo, Kwangsun, Rosenberg, Monica D., Hsu, Wei-Ting, Zhang, Sheng, Li, Chiang-Shan R., Scheinost, Dustin, Constable, R. Todd, Chun, Marvin M.
Format:	Article
Language:	English
Subjects:	Adult Algorithms Attention Attention - physiology Attention Deficit Disorder with Hyperactivity - diagnostic imaging Attention Deficit Disorder with Hyperactivity - physiopathology Attention deficit hyperactivity disorder Attention task Behavior Brain - diagnostic imaging Brain - physiology Brain mapping Cognitive ability Connectome - methods Connectome - standards Connectome - statistics & numerical data Datasets as Topic Discordance Executive Function - physiology Functional connectivity Functional magnetic resonance imaging Humans Intelligence Magnetic Resonance Imaging - methods Magnetic Resonance Imaging - standards Magnetic Resonance Imaging - statistics & numerical data Memory Methylphenidate Models, Statistical Neural networks Noise Partial least square regression Predictive model Principal components analysis Psychomotor Performance - physiology Regression analysis Reproducibility of Results Statistical analysis Studies Synchronization Young Adult
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