Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity

• Published in: NeuroImage (Orlando, Fla.) Vol. 270; p. 119946
• Main Authors: Zhao, Weiqi, Makowski, Carolina, Hagler, Donald J., Garavan, Hugh P., Thompson, Wesley K., Greene, Deanna J., Jernigan, Terry L., Dale, Anders M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2023; Elsevier Limited; Elsevier
• Subjects: Adolescent; Behavior; Behavioral differences; Behavioral inhibition; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Cardiac stress tests; Cognitive ability; Cognitive development; Estimates; Functional connectivity; Functional magnetic resonance imaging; Humans; Individuality; Linear Models; Magnetic Resonance Imaging - methods; Medical imaging; Neural networks; Neuroimaging; Paradigms; Predictions; Predictive modeling; Time series; Functional connectivity; Behavioral inhibition; Predictive modeling; Cognitive development; Behavioral differences
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2023.119946

•Functional connectivity (FC) patterns derived from fMRI tasks outperform resting-state FC at predicting individual differences in a measure of cognitive task performance and a task-derived behavioral inhibition measure.•The improvement in behavioral prediction afforded by fMRI tasks over resting-state is largely associated with the FC of the task model fit.•FC of the task model fit and task design model parameters contain shared and unique behavioral prediction power. Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.