Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity

Abstract

The complex connectivity of nervous systems is thought to have been shaped by competitive selection pressures to minimize wiring costs and support adaptive function. Accordingly, recent modeling work indicates that stochastic processes, shaped by putative trade-offs between the cost and value of each connection, can successfully reproduce many topological properties of macroscale human connectomes measured with diffusion magnetic resonance imaging. Here, we derive a new formalism that more accurately captures the competing pressures of wiring cost minimization and topological complexity. We further show that model performance can be improved by accounting for developmental changes in brain geometry and associated wiring costs, and by using interregional transcriptional or microstructural similarity rather than topological wiring rules. However, all models struggled to capture topographical (i.e., spatial) network properties. Our findings highlight an important role for genetics in shaping macroscale brain connectivity and indicate that stochastic models offer an incomplete account of connectome organization.

Trade-offs among cost, integration, and segregation in the human connectome

“…Second, the suboptimality implies that current trade-off models could not adequately capture all the features of the human brain network. Developmental constraints (Akarca et al, 2021;Nicosia, Vértes, Schafer, Latora, & Bullmore, 2013;Oldham et al, 2022) and other more specific constraints (e.g., cytoarchitectonic and genetic constraints; Arnatkeviciute et al, 2021) may be needed to complement the current framework.…”

“…Third, our examination of trade-off models was mainly based on diffusion MRI data of the empirical human brain and did not link to other empirical neurobiological phenomena. In the future, combining multimodal imaging data and linking the neurobiological measures (e.g., T1/T2 ratio; Oldham et al, 2022) with synthetic networks could provide converging evidence of the trade-off principle in the human connectome. Fourth, with the consideration of reliability and high computational load of the MOEA approach, we used the classical and reliable AAL atlas for node definition in the current study.…”

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

“…Recently, one particularly successful type of wiring rule has been homophily -where nodes preferentially wire with other nodes that are similar to themselves in terms of their shared connectivity. These rules have been shown to very effectively simulate the statistical topology of both empirical structural and functional connectivity, across scales and species (Akarca et al, 2021(Akarca et al, , 2022Betzel et al, 2016;Carozza et al, 2022;Oldham et al, 2022;Vértes et al, 2012). While it has been observed that homophily resonates with Hebbian-like mechanisms (Akarca et al, 2022;Goulas et al, 2019;Vértes et al, 2012) it still remains unclear how or why such rules would be implemented in neurobiological networks.…”

“…where 𝐷 2,4 represents the Euclidean distance between nodes i and j (as outlined above), and Ki,j reflects some topological value in forming a connection. We tested 13 established Ki,j wiring rules that have been studied elsewhere extensively (Akarca et al, 2021(Akarca et al, , 2022Betzel et al, 2016;Carozza et al, 2022;Oldham et al, 2022)…”

Analysis and Optimization of Network Properties for Bionic Topology Hopfield Neural Network Using Gaussian-Distributed Small-World Rewiring Method

“…Rüdiger et al [23] also reported that the long-range connections of small-world networks may make the network unstable, supporting frequent supercritical mutations. Ercsey-Ravasz et al [24,25] uncover a rule that the probability that two neurons are connected declines exponentially as a function of the distance between them. This important principle is termed "the exponential distance rule".…”