Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach

Abstract Recent evidence suggests that the functional connectome is stable at different time scales and unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by the high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. We found that a visual (0.41) and the medial frontal (0.35) functional networks were the most heritable, while the subcortical-cerebellum (28.6%) and the medial frontal (21.1%) networks were the most accurate on twin pair identification. Taken together, our findings suggest that twin identification accuracy does not necessarily relate to the heritability of a given functional network, indicating that heritability estimation and connectome fingerprinting are both required to study the influence of genetic factors on the functional organization of human brain at the level of large-scale networks.