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...

Full description

Saved in:

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:	eng
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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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 (FC) measured with fMRI. Here, using the CPM framework, we compared the predictive power of three different measures of FC (Pearson's correlation, accordance, and discordance) and two different prediction algorithms (linear and partial least square [PLS] regression) for attention function. Accordance and discordance are recently proposed FC measures that respectively track in-phase synchronization and out-of-phase anti-correlation (Meskaldji et al., 2015). We defined connectome-based models using task-based or resting-state FC data, and tested the effects of (1) functional connectivity measure and (2) feature-selection/prediction algorithm on individualized attention predictions. Models were internally validated in a training dataset using leave-one-subject-out cross-validation, and externally validated with three independent datasets. The training dataset included fMRI data collected while participants performed a sustained attention task and rested (N = 25; Rosenberg et al., 2016a). The validation datasets included: 1) data collected during performance of a stop-signal task and at rest (N = 83, including 19 participants who were administered methylphenidate prior to scanning; Farr et al., 2014a; Rosenberg et al., 2016b), 2) data collected during Attention Network Task performance and rest (N = 41, Rosenberg et al., in press), and 3) resting-state data and ADHD symptom severity from the ADHD-200 Consortium (N = 113; Rosenberg et al., 2016a). Models defined using all combinations of functional connectivity measure (Pearson's correlation, accordance, and discordance) and prediction algorithm (linear and PLS regression) predicted attentional abilities, with correlations between predicted and observed measures of attention as high as 0.9 for internal validation, and 0.6 for external validation (all p's
ISSN:	1053-8119 1095-9572