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.
Highlights
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
Replicating previous work, we find that our simulated networks, optimized via the statistical properties included in the energy Eq (2) via homophily generative mechanisms, accurately capture these properties in observed networks[24,25,36]
We present visualizations for subject one. b Costs (Di,j) are static, while values (Ki,j) dynamically update according to the matching rule, which enables the computation of wiring probability (Pi,j). c The mean and standard deviation for each subject of their edge-wise parameterized costs, d parameterized values and e wiring probabilities. f Histograms of each subject’s coefficient of variation (CV) showing that subjects are more variable in their value-updating compared to costs, leading to large wiring probability variability. g Regional patterning of sample-averaged nodal parameterized costs and values, showing highly “valuable” patterning in the left temporal lobe and “cheap” regions generally occupying medial aspects of the cortex
Summary
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. We: (1) tested which topological features should be valued in the wiring trade-off to produce highly accurate individual child connectomes; (2) tested how small changes in these parameters alter the organizational properties of the resulting networks; (3) established relationships between these different wiring parameters and cognitive outcomes; (4) identified genes with expression profiles that were spatially co-located with those topological features; and (5) established the biological pathways that are enriched in these gene lists Together, this provides a computational framework that mathematically specifies the formation of a network over time, captures individual differences in brain organization and cognition, and incorporates the genetic and biological pathways that likely constrain network formation
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