P227 Only a matter of time: developmental heterochronicity captures network properties of the human connectome
Stuart Oldham*1,Francesco Poli2, Duncan Astle2, Gareth Ball1
1 Murdoch Children’s Research Institute, Melbourne, Australia 2 Cambridge University, Cambridge, UK
*Email: stuart.oldham@mcri.edu.au
Brain network organization is shaped by a trade-off between connection costs and functional benefits [1]. Computational generative network models have found this trade-off explains many, but not all, network properties [2,3]. During gestation, brain development proceeds according to spatiotemporal patterns defined by morphogen gradients [4]. Cortical areas display heterochronicity, differential timing of key developmental events, which induces spatial patterns that persist in later life as smoothly varying gradients in cytoarchitecture, neuronal connectivity, and functional activation [4,5]. Therefore, we developed a new computational model to assess how heterochronicity may constrain the formation of cortical connectivity. Developmental timing was modeled along a unimodal gradient, originating from one node per hemisphere (Fig. 1A). Nodes were sequentially 'activated' over model timesteps based on their geodesic distance from the origin, with the timing/heterochronicity of activation controlled by the parameter τ, and the connection probability between active nodes governed by their wiring-cost η (Fig. 1B-C). The summed probabilities across timesteps used to generate a density matched network (Fig. 1C). The model was run for each origin, optimizing parameters to maximize model fit, which was the degree correlation ρ with empirical network (a group consensus structural connectivity brain network [2]), a feature generative models struggle to capture [2,3]. Spatial gradients modeling heterochronicity from the frontal cortex yielded the highest degree correlations (max ρ=0.39). These same networks with the best degree correlation also captured key empirical topological features, including clustering, connection length, and primary connectivity gradient. However, they did not fully replicate modularity or connection overlap (Fig. 1D). Models from the best origins (ρ>0.25) outperformed previous leading approaches[2,3] (Fig. 1E) and achieved the best fits with strong heterochronicity (τ > 0.5) and minimal distance penalties (η ≈ 0; Fig. 1F). Models using only the heterochronicity term produced similar degree correlations (Fig. 1G), suggesting it alone can drive brain-like connectivity patterns. Here we demonstrate that constraining network connections to form along an anterior-posterior gradient is sufficient to capture topographical and topological connectomic features of empirical brain networks. These models also outperform past approaches [2,3]. The best-performing models imposed a heterochronous gradient that aligned with the rostral-caudal axis, a known major neurodevelopmental gradient[4,5], suggesting that early spatiotemporal patterning along this axis is key to shaping cortical connectivity. While our study examined single unimodal gradients, future studies could integrate multiple biologically informed gradients to better model network complexity. Our framework offers a flexible foundation for such extended work.
Figure 1. Fig. 1 (A) Geodesic distances from example origin (B) Heterochronicity/wiring-cost calculation (C) Model connection probability and network generation (D) Similarity to the empirical data on network features for each origin’s best fitting model (E) Comparison to previous models[2] (F) τ and η for each origin’s best fitting model (G) Best degree correlations for heterochronous-only models Acknowledgements S.O is supported by the Brain and Behavior Research Foundation (ID: 31471). G.B. was supported by the National Health and Medical Research Council (ID: 1194497). Research was supported by the Murdoch Children’s Research Institute, the Royal Children’s Hospital, Department of Paediatrics, The University of Melbourne and the Victorian Government’s Operational Infrastructure Support Program. References 1.https://doi.org/10.1038/nrn3214 2.https://doi.org/10.1101/2024.11.18.624192 3.https://doi.org/10.1126/sciadv.abm6127 4.https://doi.org/10.1016/j.neuron.2007.10.010
