OHBM 2022 | 176 | Talk | Rui Shen | Generative network models for neurodevelopment in infants with… скачать в хорошем качестве

OHBM 2022 | 176 | Talk | Rui Shen | Generative network models for neurodevelopment in infants with…

Title: Generative network models for neurodevelopment in infants with and without familial risk for autism. Session: Talk Speaker: Rui Shen Connectivity abnormality has been widely characterized in autistic children and adolescents, but what mechanisms drive network alterations, and when network divergence arises, are unknown. Here we present a longitudinal study of structural networks over the first two years of life in 369 infants at high and low familial risk for autism. We utilize generative network models (GNMs) to explore possible wiring rules in early development and their associations with behavior scores.We showed that the matching index model can be used to describe early development irrespective of the risk and diagnostic status. Varying the parameters could alter the organizational properties of brain networks. We observed decreased magnitude of η (less spatial constraint) and decreased γ (less homophily attractiveness) in HR+ compared to HR- and LR-, which may be associated with longer fiber length (and brain enlargement) and reduced modularity in autism. Our results suggested that atypical wiring patterns in autism can emerge around 12 months and are associated with early motor and social behaviors.