Only a matter of time: developmental heterochronicity captures properties of the human connectome
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
1205
Submission Type:
Abstract Submission
Authors:
Stuart Oldham1, Francesco Poli2, Duncan Astle2, Gareth Ball1
Institutions:
1Murdoch Children's Research Institute, Melbourne, VIC, 2Cambridge University, Cambridge, United Kingdom
First Author:
Co-Author(s):
Introduction:
Brain network organization is constrained by a trade-off between the energetic cost of forming connections and the advantages they confer on network function[1]. Computational models have shown this cost-benefit trade-off can explain many-but not all-network properties[2,3]. During gestation, brain development proceeds according to a precise spatiotemporal pattern defined by a series of 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[5]. Therefore, heterochronicity may provide an additional constraint on the formation of cortical connectivity[6,7]. While the putative role of developmental timing on cortical wiring has been modelled abstractly[8,9], prior studies have not accounted for the brain's geometry, a critical factor in modeling the potential role of diffusing molecular gradients. To address this, we developed a framework to examine how heterochronicity and synchronicity across cortical areas can shape human brain networks.
Methods:
We defined brain networks using a high-resolution cortical parcellation (400 nodes). Developmental timing was modeled along a single unimodal gradient, originating from one starting node per hemisphere (Fig. 1A). Nodes were sequentially 'activated' based on their geodesic distance from the origin Tij(n) at timestep n (Fig. 1B). The connection probability at timestep n was defined as wij(n)=Tij(n)×Dij×Sij, where Dij is the scaled Euclidean distance (Fig. 1C) and Sij is nodal synchronicity (activation time proximity; Fig. 1D). After 100 timesteps, connection strength was defined as the sum of probabilities, thresholded to match the density of a group-averaged structural network (Fig. 1E). The model was run for each nodal origin, and the model parameters (τ, η, γ) were optimized to maximize the degree correlation of generated and empirical networks (Fig. 1F), a challenging feature for generative network models to capture[3].
Results:
Spatial gradients modeling heterochronicity originating in the frontal cortex produced the strongest positive degree sequence correlations (maximum ρ=0.43; Fig. 2A). These top-performing models also captured other topological properties, closely matching the empirical network's mean clustering, connection length, and primary connectivity gradient (Fig. 2B-D), but neither modularity nor connection overlap (Fig. 2E-F). The best models outperformed a spatial-only model, commonly used as a benchmark in generative network modeling studies (Fig. 2G)[2,3]. Models with strong heterochronicity and minimal distance penalty yielded the highest correlations (Fig. 2H; synchronicity had minimal impact on overall model fit). Running the model with only the heterochronicity term also produced similar degree correlations (Fig. 2I), suggesting this feature alone can replicate brain-like connectivity patterns.
Conclusions:
Here we demonstrate that constraining network connections to form along a simple anterior-posterior gradient is sufficient to capture several topographical and topological connectomic features of empirical structural brain networks. The direction of the best performing gradients aligned along a rostral-caudal axis, mirroring a major neurodevelopmental gradient and illustrating the importance of early spatiotemporal patterning on cortical connectivity[5]. While our study conducted an unbiased survey of single unimodal gradient directions with spatial origins distributed across the cortical surface, future studies could incorporate multiple biologically-informed spatial gradients to better capture complex topological and topographical network properties[10]. Our model provides a flexible framework that can be expanded to explore such possibilities.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Normal Development
Keywords:
Development
Other - Connectome; Network; Connectivity; Generative network model; Brain maps
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
2. Betzel, R. F. et al. Generative models of the human connectome. NeuroImage 124, 1054–1064 (2016).
3. Oldham, S. et al. Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity. Sci. Adv. 8, eabm6127 (2022).
4. O’Leary, D. D. M., Chou, S.-J. & Sahara, S. Area Patterning of the Mammalian Cortex. Neuron 56, 252–269 (2007).
5. Huntenburg, J. M., Bazin, P. L. & Margulies, D. S. Large-Scale Gradients in Human Cortical Organization. Trends in Cognitive Sciences 22, 21–31 (2018).
6. Kaiser, M. Mechanisms of Connectome Development. Trends in Cognitive Sciences 21, 703–717 (2017).
7. Oldham, S. & Fornito, A. The development of brain network hubs. Developmental Cognitive Neuroscience 36, 100607 (2019).
8. Goulas, A., Betzel, R. F. & Hilgetag, C. C. Spatiotemporal ontogeny of brain wiring. Science Advances 5, (2019).
9. Beul, S. F., Goulas, A. & Hilgetag, C. C. Comprehensive computational modelling of the development of mammalian cortical connectivity underlying an architectonic type principle. PLoS Computational Biology 14, 1–45 (2018).
10. Sansom, S. N. & Livesey, F. J. Gradients in the Brain: The Control of the Development of Form and Function in the Cerebral Cortex. Cold Spring Harbor Perspectives in Biology 1, a002519–a002519 (2009).