Dissociation of reliability, heritability, and predictivity in coarse- and fine-scale functional connectomes during development

Abstract

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine-scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9 - 10 years; n = 1,115 individuals, both sexes, 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition. Significance statementYears of human magnetic resonance imaging (MRI) research demonstrate that individual variability in resting-state functional connectivity relates to genetics and cognition. However, the various spatial scales where individual differences in connectivity could occur have yet to be considered in childhood brain- behavior association studies. Here, we use novel machine learning approaches to examine the reliability, heritability, and behavioral relevance of different spatial scales of the resting-state functional connectome during childhood. We show that broad features of the connectome are strongly related to heritability, whereas fine details are more reliable and strongly associated with neurocognitive performance. These data indicate that reliable, heritable, and behaviorally-relevant individual differences exist at dissociable scales of the functional connectome.