Abstract
Brain network connections rewire adaptively in response to neural activity. Adaptive rewiring may be understood as a process which, at its every step, is aimed at optimizing the efficiency of signal diffusion. In evolving model networks, this amounts to creating shortcut connections in regions with high diffusion and pruning where diffusion is low. Adaptive rewiring leads over time to topologies akin to brain anatomy: small worlds with rich club and modular or centralized structures. We continue our investigation of adaptive rewiring by focusing on three desiderata: specificity of evolving model network architectures, robustness of dynamically maintained architectures, and flexibility of network evolution to stochastically deviate from specificity and robustness. Our adaptive rewiring model simulations show that specificity and robustness characterize alternative modes of network operation, controlled by a single parameter, the rewiring interval. Small control parameter shifts across a critical transition zone allow switching between the two modes. Adaptive rewiring exhibits greater flexibility for skewed, lognormal connection weight distributions than for normally distributed ones. The results qualify adaptive rewiring as a key principle of self-organized complexity in network architectures, in particular of those that characterize the variety of functional architectures in the brain.
Highlights
From gestation to termination, the brain continuously undergoes adaptive rewiring; structural changes that shape, maintain, and provide flexibility to function
We described adaptive rewiring in a graph-theoretical framework as adding shortcut connections between nodes with strong functional connectivity while pruning connections with weak functional connectivity (Gong and van Leeuwen, 2003, 2004; van den Berg and van Leeuwen, 2004; Rubinov et al, 2009; Jarman et al, 2014; Papadopoulos et al, 2017; Hellrigel et al, 2019)
We showed that adaptive rewiring leads to networks that are small worlds for all combinations of τ and prandom, save the degenerative cases (τ close to 0; prandom = 1) (Jarman et al, 2017; Rentzeperis and van Leeuwen, 2020)
Summary
The brain continuously undergoes adaptive rewiring; structural changes that shape, maintain, and provide flexibility to function. We described adaptive rewiring in a graph-theoretical framework as adding shortcut connections between nodes with strong functional connectivity while pruning connections with weak functional connectivity (Gong and van Leeuwen, 2003, 2004; van den Berg and van Leeuwen, 2004; Rubinov et al, 2009; Jarman et al, 2014; Papadopoulos et al, 2017; Hellrigel et al, 2019) Whereas those models considered functional connectivity in oscillatory activity, some adaptive rewiring models (Jarman et al, 2017; Rentzeperis and van Leeuwen, 2020) are based on a broader, more abstract notion of neural activity. This motivates our choice of adopting heat diffusion to represent neural mass activity in our current model networks
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