Shared and unique brain network features predict cognition, personality and mental health in childhood

Abstract

The manner through which individual differences in brain network organization track population-level behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, the focus of most studies on single behavioral traits has come at the expense of capturing broader relationships across behaviors. Here, we utilized a large-scale dataset of 1858 typically developing children to estimate whole-brain functional network organization that is predictive of individual differences in cognition, impulsivity-related personality, and mental health during rest and task states. Predictive network features were distinct across the broad behavioral domains: cognition, personality and mental health. On the other hand, traits within each behavioral domain were predicted by highly similar network features. This is surprising given decades of research emphasizing that distinct brain networks support different mental processes. Although tasks are known to modulate the functional connectome, we found that predictive network features were similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood, yet are unique to different behavioral domains. 12 Siemens Prisma fit 79 J 18 GE Discovery MR750 73 J 21 Siemens Prisma fit/Prisma 76 J

Towards Reproducible Brain-Wide Association Studies

“…3c). The SVR association between RSFC and cognitive ability was the largest replicated brain-wide effect across all measures and is consistent with other recent large-scale prediction efforts 31,32 . Similarly, CCA associations between RSFC and the NIH Toolbox fit on the full discovery sample achieved significant out-of-sample replication (r=0.22, null r=0.09, p<0.01; Fig.…”

Generalizable prediction of stimulus-independent, task-unrelated thought from functional brain networks

“…Though prediction effect sizes were modest, they were in line with typical functional networkbased predictions of self-report outcomes in large samples 46 and were striking given the various differences among datasets (e.g. online task/experience sampling versus rs-fMRI of varying duration as well as different MRI scanners/protocols, populations, and outcome measures).…”

“…This provided evidence that our functional network model was predictive of SITUT within novel individuals at the within-dataset level (i.e., establishing internal validation) and with an overall effect size that was on par with that typically found for functional connectivity-based prediction of self-report outcomes. 46 The edges contributing to the model (hereafter referred to as "SITUT-CPM" masks) included 258 and 139 edges, respectively, positively and negatively associated with SITUT.…”

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

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