Heterochronicity shapes brain network formation.
5 affiliations
- 1Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom.
- 2Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, United Kingdom.
- 3Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital Melbourne, Melbourne VIC 3052, Australia.
- 4Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash Biomedical Imaging, Monash University, Clayton, Melbourne, VIC 3800, Australia.
- 5School of Academic Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, United Kingdom.
Abstract
Brain network formation unfolds on a nonuniform developmental timetable, with different cortical regions generating connections at different developmental phases. Generative network models (GNMs) aim to uncover the principles underpinning the organization of connectomes by creating synthetic networks according to simple computational rules. These models capture the connectome's topology, operationalized here as the cumulative distributions of network metrics (e.g., modularity, small-worldness, rich-club structure). However, GNMs typically ignore the differential timing of connectivity formation, and by omitting this temporal programme they often misplace topological features in physical space. Here, we add a heterochronous growth term to GNMs and use a model fitness function that weighs topology and topography equally. Topography refers to the spatial embedding of the network-the actual anatomical positions of the axonal tracts. With these advances, we can generate synthetic networks that more faithfully reproduce the spatial layout of diffusion-MRI connectomes from two independent adult cohorts. Compared with classical, temporally agnostic models, heterochronous simulations improve model fit, accurately locate cortical hubs and modules across individuals, and converge on a single caudal-to-rostral gradient of brain maturation. Overall, integrating heterochronicity makes GNMs more faithful to brain development, and allows for better identification of individual differences in network formation.
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BibTeX
@article{poli2026heterochronicity,
title = {{Heterochronicity shapes brain network formation.}},
author = {Francesco Poli and Stuart Oldham and Alexa Mousley and Edward T Bullmore and Petra E Vértes and Duncan E Astle},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = oct,
doi = {10.1073/pnas.2610400123},
url = {https://europepmc.org/article/MED/42863075},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Heterochronicity shapes brain network formation.
AU - Francesco Poli
AU - Stuart Oldham
AU - Alexa Mousley
AU - Edward T Bullmore
AU - Petra E Vértes
AU - Duncan E Astle
PY - 2026
DA - 2026/10/12
JO - Proceedings of the National Academy of Sciences of the United States of America
DO - 10.1073/pnas.2610400123
UR - https://europepmc.org/article/MED/42863075
AB - Brain network formation unfolds on a nonuniform developmental timetable, with different cortical regions generating connections at different developmental phases. Generative network models (GNMs) aim to uncover the principles underpinning the organization of connectomes by creating synthetic networks according to simple computational rules. These models capture the connectome's topology, operationalized here as the cumulative distributions of network metrics (e.g., modularity, small-worldness, rich-club structure). However, GNMs typically ignore the differential timing of connectivity formation, and by omitting this temporal programme they often misplace topological features in physical space. Here, we add a heterochronous growth term to GNMs and use a model fitness function that weighs topology and topography equally. Topography refers to the spatial embedding of the network-the actual anatomical positions of the axonal tracts. With these advances, we can generate synthetic networks that more faithfully reproduce the spatial layout of diffusion-MRI connectomes from two independent adult cohorts. Compared with classical, temporally agnostic models, heterochronous simulations improve model fit, accurately locate cortical hubs and modules across individuals, and converge on a single caudal-to-rostral gradient of brain maturation. Overall, integrating heterochronicity makes GNMs more faithful to brain development, and allows for better identification of individual differences in network formation.
SN - 0027-8424
SN - 1091-6490
ER -