A generative network model of neurodevelopmental diversity in structural brain organization

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.


Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection. Methods n/a Involved in the study

ChIP-seq
Flow cytometry
The datasets supporting the current study have not been deposited in a public repository because of restrictions imposed by NHS ethical approval, but are available from the corresponding author on request. Requests for access can be made by research-based institutions for academic purposes. A response can be expected within at least one week. Unidentifiable simulated data can be found at https://osf.io/h9px4/?view_only=984260dcff444b59819961ece9c724ec.
The CALM cohort contains N=967 total children (N=805 referred; N=162 unreferred). Of these, N=299 undertook MRI scanning of which N=279 had usable MRI data. N=270 of these had cognitive data available and formed the cohort used in this analysis.
For the majority of the study, the total CALM cohort of N=270 children were used. For the gene expression analysis, one subject was excluded as they were the only subject to have a positive gamma scalar, causing the subject to be a large outlier in the analysis. This left a sample of N=269 for this section alone.
Generative network models are innately stochastic. As a result, identical results are unlikely to be obtained upon individual model runs. However, sufficient model attempts (such as our parameter selection protocol; see Methods) allow for replication testing of key statistical findings. All attempts at internal replication, where possible, were successful. Independent replication attempts were carried out twice for each of the two cohorts examined.
Randomization is not relevant to the present study. This is because subjects were not allocated into experimental groups.
Blinding is not relevant to the present study. This is because subjects were not allocated into experimental groups. Magnetic resonance imaging data were acquired at the MRC Cognition and Brain Sciences Unit in Cambridge, on the Siemens 3 T Prisma-fit system (Siemens Healthcare) using a 32!channel quadrature head coil. N=299 CALM children underwent MRI scanning. 20 scans were not useable due to excessive motion (>3 mm movement during the diffusion sequence estimated through FSL eddy), leaving an MRI sample of N=279 children. T1!weighted volume scans were acquired using a whole brain coverage 3D Magnetization Prepared Rapid Acquisition Gradient Echo (MP RAGE) sequence acquired using 1 mm isometric image resolution. Echo time was 2.98ms, and repetition time was 2,250ms. Diffusion scans were acquired using echo!planar diffusion!weighted images with an isotropic set of 68 noncollinear directions, using a weighting factor of b = 1,000s × mm"2, interleaved with 4 T2!weighted (b = 0) volume. Whole brain coverage was obtained with 60 contiguous axial slices and isometric image resolution of 2 mm. Echo time was 90ms and repetition time was 8500ms.