Fragmentation: loss of global coherence or breakdown of modularity in functional brain architecture?

Berg, Daan van den; Gong, Pulin; Breakspear, Michael; Leeuwen, Cees van

doi:10.3389/fnsys.2012.00020

Cited by 37 publications

(32 citation statements)

References 49 publications

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“…Our findings thus highlight the global impact of stroke, despite its relatively focal damage. This novel finding in stroke is consistent with studies in other neurological diseases, such as schizophrenia, where imbalances between local and global dynamics, specifically a breakdown of local structure and a shift toward global dynamics have been suggested ( 68 ).…”

Section: Discussionsupporting

confidence: 90%

The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke

et al. 2015

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There currently remains considerable variability in stroke survivor recovery. To address this, developing individualized treatment has become an important goal in stroke treatment. As a first step, it is necessary to determine brain dynamics associated with stroke and recovery. While recent methods have made strides in this direction, we still lack physiological biomarkers. The Virtual Brain (TVB) is a novel application for modeling brain dynamics that simulates an individual’s brain activity by integrating their own neuroimaging data with local biophysical models. Here, we give a detailed description of the TVB modeling process and explore model parameters associated with stroke. In order to establish a parallel between this new type of modeling and those currently in use, in this work we establish an association between a specific TVB parameter (long-range coupling) that increases after stroke with metrics derived from graph analysis. We used TVB to simulate the individual BOLD signals for 20 patients with stroke and 10 healthy controls. We performed graph analysis on their structural connectivity matrices calculating degree centrality, betweenness centrality, and global efficiency. Linear regression analysis demonstrated that long-range coupling is negatively correlated with global efficiency (P = 0.038), but is not correlated with degree centrality or betweenness centrality. Our results suggest that the larger influence of local dynamics seen through the long-range coupling parameter is closely associated with a decreased efficiency of the system. We thus propose that the increase in the long-range parameter in TVB (indicating a bias toward local over global dynamics) is deleterious because it reduces communication as suggested by the decrease in efficiency. The new model platform TVB hence provides a novel perspective to understanding biophysical parameters responsible for global brain dynamics after stroke, allowing the design of focused therapeutic interventions.

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Section: Discussionsupporting

confidence: 90%

The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke

et al. 2015

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“…In previous adaptive rewiring models, functional connectivity was represented as synchronous activity 9,10,12,13,21,44,45 of linearly coupled nonlinear oscillators 46 or, somewhat more realistically, by spiking model neuron synchrony 47,48 . While these representations more closely resemble artificial neural networks than the current graph diffusion model, their shortcomings are twofold.…”

Section: Discussionmentioning

confidence: 99%

Adaptive rewiring evolves brain-like structure in weighted networks

Rentzeperis

Leeuwen

2020

Sci Rep

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Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally-and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure.From neuronal synapses to white matter tracts 1 , brain anatomical networks are characterized by the structural properties of small-worldness 2,3 , modularity 4,5 and rich club organization 6,7 . The pervasiveness of these properties raises the question whether they result from a common principle 5,8 . We proposed that these properties are the product of adaptive rewiring 9-13, for a review14 . Adaptive rewiring captures a crucial property of how the brain's anatomical network is shaped over time. Whereas the mechanisms that shape the brain network show great variety, as they encompass brain growth 15 , development, as well as learning 16,for a review , they are alike in their common dependency on the network's functional connectivity, i.e. the statistical dependencies between the nodes' activities 12,17 . Adaptive rewiring formalizes this dependency in terms of graph theory, as it encompasses adding shortcut links to network regions with intense functional connectivity while pruning underused ones. These dynamical rewirings could be regarded as adaptive network optimization to function 18 .Whereas adaptive rewiring represents the dependency of structural connectivity on network activity, the reverse, viz. the dependency of activity on structural connectivity, has been the focus of intense investigation. Recently, it was proposed that a simple, linear equation can predict brain activity from anatomical connectivity 19 . The proposal describes traffic on a fixed anatomical network in terms of random walks. This allows the amount of traffic to be stochastically approximated in terms of diffusion on the graph. Jarman and colleagues 11 adopted a similar principle in an adaptive rewiring model. They represented the amount of flow transferred between nodes by a heat kernel. During each rewiring step, an edge wit...

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“…In our models, all G have |V| = 300 and |E| = 5200, a connectivity density providing robust evolution of small-world structure with uniform parameter setting (van den Berg et al, 2012). Model structure is initialized by randomly assigning 5200 × 2 = 10400…”

Section: Parameter Setting and Initializationmentioning

confidence: 99%

Adaptive Rewiring in Non-Uniform Coupled Oscillators

Haqiqatkhah¹,

Leeuwen²

2020

Preprint

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Structural plasticity of the brain can be represented in highly simplified form as adaptive rewiring; the relay of connections according to the spontaneous dynamic synchronization in network activity. Adaptive rewiring, over time, leads from initial random networks to complex networks, i.e. networks with modular small-world structures and a rich club effect. Adaptive rewiring has only been studied, however, in networks of identical oscillators with uniform or random coupling strengths. To implement information processing functions (e.g., stimulus selection or memory storage), it is necessary to consider symmetry-breaking perturbations of oscillator amplitudes and coupling strengths. We studied whether non-uniformities in amplitude or connection strength could operate in tandem with adaptive rewiring. Throughout network evolution, either amplitude or connection strength of a subset of oscillators were kept different from the rest. In these extreme conditions, subsets might become isolated from the rest of the network or otherwise interfere with development of network complexity. Whereas these subsets form distinctive structural and functional communities, this does not interfere with emergence of the characteristic network complexity. These results suggest that adaptive rewiring is robust against, and can operate in, information processing networks.

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Fragmentation: loss of global coherence or breakdown of modularity in functional brain architecture?

Cited by 37 publications

References 49 publications

The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke

The Virtual Brain: Modeling Biological Correlates of Recovery after Chronic Stroke

Adaptive rewiring evolves brain-like structure in weighted networks

Adaptive Rewiring in Non-Uniform Coupled Oscillators

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