A weighted generative model of the human connectome

Abstract

Probabilistic generative network models have offered an exciting window into the constraints governing the human connectome's organization. In particular, they have highlighted the economic context of network formation and the special roles that physical geometry and self-similarity likely play in determining the connectome's topology. However, a critical limitation of these models is that they do not consider the strength of anatomical connectivity between regions. This significantly limits their scope to answer neurobiological questions. The current work draws inspiration from the principle of redundancy reduction to develop a novel weighted generative network model. This weighted generative network model is a significant advance because it not only incorporates the theoretical advancements of previous models, but also has the ability to capture the dynamic strengthening or weakening of connections over time. Using a state-of-the-art Convex Optimization Modelling for Microstructure-Informed Tractography (COMMIT) approach, in a sample of children and adolescents (n = 88, aged 8 to 18 years), we show that this model can accurately approximate simultaneously the topology and edge-weights of the connectome (specifically, the MRI signal fraction attributed to axonal projections). We achieve this at both sparse and dense connectome densities. Generative model fits are comparable to, and in many cases better than, published findings simulating topology in the absence of weights. Our findings have implications for future research by providing new avenues for exploring normative developmental trends, models of neural computation and wider conceptual implications of the economics of connectomics supporting human functioning.

The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity

“…It is important to note that while networks with weaker constraints have lower absolute communicability, this may not necessarily be a disadvantage. Indeed, these relatively random networks are more likely to support efficient communication because the communication paths across the network tend to be less redundant ( 42 ). We show this trade-off between communicability and efficiency within our simulations in SI Appendix , Fig.…”

“…The models used here currently only approximate the topology of binary networks. New models that also modulate connection strength over time, in addition to connection formation, may be better positioned to capture developmental stochasticity ( 42 ). Similarly, models that vary rules and/or parameters across space may offer a path to examining the differential contribution of stochasticity to the connectivity of different brain regions.…”

Developmental differences in canonical cortical networks: insights from microstructure-informed tractography

“…Future work characterising the developing connectome using biologically meaningful mathematical models of brain connections are promising (Akarca et al, 2023; Seguin et al, 2023). Combining task-based or resting-state fMRI with microstructure-informed connectomes may better elucidate structure-function coupling across the developing brain (Suárez et al, 2020).…”

“…Future work characterising the developing connectome using biologically meaningful mathematical models of brain connections are promising (Akarca et al, 2023;Seguin et al, 2023).…”

“…Finally, new frontiers in characterising the developing connectome using biologically meaningful mathematical models of brain connections are promising (Akarca et al, 2023;Seguin et al, 2023). Recent updates to the COMMIT framework offer the opportunity to incorporate additional imaging contrasts, such as myelin-sensitive contrasts, leading to improved delineation of anatomically accurate whole-brain tractography (Leppert et al, 2023;.…”