Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity-a "connectotype." How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.
PubMed: MeSH publication types
- Journal Article