Heritability of individualized cortical network topography

• Published in: Proceedings of the National Academy of Sciences - PNAS 2021-03, Vol.118 (9), p.1-9
• Main Authors: Anderson, Kevin M., Ge, Tian, Kong, Ru, Patrick, Lauren M., Spreng, R. Nathan, Sabuncu, Mert R., Yeo, B. T. Thomas, Holmes, Avram J.
• Format: Article
• Language: English
• Subjects: Biological Sciences; Brain Mapping - methods; Cerebral Cortex - metabolism; Connectome; Humans; Magnetic Resonance Imaging; Models, Theoretical; Social Sciences
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2016271118

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h²: M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h²: M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h²-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.