Decades of human magnetic resonance imaging (MRI) research demonstrate that variance in neuroimaging phenotypes, including functional connectivity, relate to genetics1–5 and predict cognitive traits6–9. The functional connectome affords information transmission through the brain at various spatial scales, from global oscillations between broad cortical regions to fine-scale connections that underlie specific information processing10,11. In adults, while both the coarse- and fine-scale functional connectomes predict cognition6,12–14, the fine-scale connectome predicts twice as much cognitive variance15. Yet, past brain-wide association studies, particularly using large developmental samples, have limited their focus to the coarse connectome to understand the neural underpinnings of individual differences in cognition8,9,16–18. We studied resting-state fMRI in 1,115 children (including 389 twin pairs) and used functional alignment to afford access to individual differences in the fine-scale connectome10,19,20. We found that even though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale connectome, they are less heritable. This surprising result indicates that genetically-determined versus experience-dependent factors in brain development have dissociable effects on these two spatial scales of the connectome. We show further that both connectome scales equally predict a more heritable trait (general cognitive ability) in childhood, but only the fine scale effectively predicts a more experience-driven trait (learning/memory). As such, the developing functional connectome resembles a LEGOⓇ set: the specific pieces a child has parameterizes what they will eventually build, but even when given identical sets, two children with unique experiences will build different creations.

Pediatric obesity is a major public health concern. Genetic susceptibility and increased availability of energy-dense food are known risk factors for obesity. However, the extent to which these factors jointly bias behavior and neural circuitry towards increased adiposity in children remains unclear. While undergoing fMRI, 108 children (ages 5-11y) performed a food-specific go/no-go task. Participants were instructed to either respond (“go”) or inhibit responding (“no-go”) to images of food or toys. Half of the runs depicted high-calorie foods (e.g., pizza) whereas the other half depicted low-calorie foods (e.g., salad). Children were also genotyped for a DNA polymorphism associated with energy intake and obesity (FTO rs9939609) to examine the influence of obesity risk on behavioral and brain responses to food. Participants demonstrated differences in behavioral sensitivity to high- and low-calorie food images depending on task demands. Participants were slower but more accurate at detecting high- (relative to low-) calorie foods when responding to a neutral stimulus (i.e., toys) and worse at detecting toys when responding to high-calorie foods. Inhibition failures were accompanied by salience network activity (anterior insula, dorsal anterior cingulate cortex), which was driven by false alarms to food images. Children at a greater genetic risk for obesity (dose-dependent model of the FTO genotype) demonstrated pronounced brain and behavioral relationships such that genetic risk was associated with heightened sensitivity to high-calorie food images and increased anterior insula activity. These findings suggest that high-calorie foods may be particularly salient to children at risk for developing eating habits that promote obesity.