A generative network model of neurodevelopmental diversity in structural brain organization

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.

Homophilic wiring principles underpin neuronal network topologyin vitro

“…This explanation would suggest that probabilistic network wiring becomes less determined in any condition that decreases specificity between neurons. Interestingly, previous GNM work at the whole-brain scale has shown that lower magnitude wiring parameters are associated with poorer cognitive scores 46 , age 46,47 and a diagnosis of Schizophrenia 45,48 . This may suggest convergent evidence for how developmental randomness, intrinsic to how developing parts interact with each other, may influence functional outcomes 100 .…”

“…Previous studies employing generative models of human macroscopic structural brain organization have shown that generative rules based on homophilic attachment mechanisms can achieve very good model fits [45][46][47]49 (although see 50 ). But what do homophily-based wiring rules essentially entail?…”

“…In the present study, we make a distinction between simulated networks which mirror the statistical distributions of observed networks and those which mirror the topological organization of those statistics. The former can be accessed via a previously used energy equation 46,47 whereby the model fit is given by the "worst" of the four KS distances assessed, given by Equation 2. KS is the Kolmogorov-Smirnov statistic between the observed and simulated networks at the particular η and γ combination used to construct the simulation, defined by the network degree k, clustering coefficient c, betweenness centrality b and Euclidean edge length d. Notably, the KS distance between two vectors simply considers their statistical distributions.…”

Edges in brain networks: Contributions to models of structure and function

“…For network neuroscience, null models broadly fall into two categories. Generative null models describe the placement or formation of edges between nodes, often based on simple rules that after repeated application, can form complex topology ( Akarca et al, 2021 ; Vertes et al, 2012 ). Rewiring null models alter the given topology of a network by swapping or reweighting edges, according to specified constraints or rules ( Kaiser & Hilgetag, 2006 ; Roberts et al, 2016 ).…”

Benchmarking functional connectivity by the structure and geometry of the human brain

“…A natural extension of sgFC is in the domain of spatially embedded null models that generate surrogate structural or functional networks to benchmark the presence of specific network attributes (Esfahlani, Bertolero, Bassett, & Betzel, 2020;Roberts et al, 2016). Moreover, sgFC may also serve as a quality function for generative models of connectivity Journal: NETWORK NEUROSCIENCE / Title: Benchmarking functional connectivity by the structure and geometry of the human brain Authors: Zhen-Qi Liu, Richard F. Betzel, Bratislav Misic (Akarca, Vértes, Bullmore, & Astle, 2021;Betzel et al, 2016;Oldham et al, 2021;Shinn et al, 2021;Vértes et al, 2012). Finally, we envision sgFC as the basis for more sophisticated network communication models that consider spatial proximity as a constraint for routing signals (Seguin, Razi, & Zalesky, 2019;Seguin, van den Heuvel, & Zalesky, 2018;Vázquez-Rodríguez, Liu, Hagmann, & Misic, 2020b).…”