A generative network model of neurodevelopmental diversity in structural brain organization
Danyal Akarca, Petra E. Vértes, Edward T. Bullmore, the CALM team, Kate Baker, Susan E. Gathercole, Joni Holmes, Rogier Kievit, Tom Manly, Joe Bathelt, Marc Bennett, Giacomo Bignardi, Sarah Bishop, Erica Bottacin, Lara Bridge, Diandra Brkić, Annie Bryant, Sally Butterfield, Elizabeth M. Byrne, Gemma Crickmore, Edwin S. Dalmaijer, Fánchea Daly, Tina Emery, Laura Forde, Grace Franckel, Delia Fuhrmann, Andrew Gadie, Sara Gharooni, Jacalyn Guy, Erin Hawkins, Agnieszka Jaroslawska, Sara Joeghan, Amy R. Johnson, Jonathan Spencer Jones, Silvana Mareva, Elise Ng‐Cordell, Sinéad O’Brien, Cliodhna O’Leary, Joseph P. Rennie, Ivan L. Simpson-Kent, Roma Šiugždaitė, Tess A. Smith, Stephani Uh, Maria Vedechkina, Francesca Woolgar, Natalia Zdorovtsova, Mengya Zhang, Duncan E. Astle
Abstract
The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.