How Tasks Change Whole-Brain Functional Organization to Reveal Brain-Phenotype Relationships
Abigail S. Greene,
Siyuan Gao,
Stephanie Noble,
Dustin Scheinost,
R. Todd Constable
Affiliations
Abigail S. Greene
Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; MD/PhD Program, Yale School of Medicine, New Haven, CT, USA; Corresponding author
Siyuan Gao
Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA
Stephanie Noble
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
Dustin Scheinost
Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; The Child Study Center, Yale School of Medicine, New Haven, CT, USA
R. Todd Constable
Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA; Department of Biomedical Engineering, Yale School of Engineering & Applied Science, New Haven, CT, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA; Corresponding author
Summary: Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing seven tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. To further characterize these predictive FC changes, we develop and apply an inter-subject PPI analysis. We find that moderate, but not high, task-induced consistency of the blood-oxygen-level-dependent (BOLD) signal across individuals is useful for prediction. Together, these findings demonstrate that in-scanner tasks have distributed, phenotypically relevant effects on brain functional organization, and they offer a framework to leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.