Spreading dynamics on spatially constrained complex brain networks

O’Dea, Reuben D.; Crofts, Jonathan J.; Kaiser, Marcus

doi:10.1098/rsif.2013.0016

Cited by 31 publications

(35 citation statements)

References 40 publications

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“…We simulated network spreading dynamics using a family of linear threshold models (LTMs) that describe how local perturbations trigger global cascades (Granovetter, 1978;O'Dea et al, 2013). The models simulate how multiple exposures to some perturbation from the neighborhood of a node cause the node itself to adopt an active state (Watts, 2002).…”

Section: Resultsmentioning

confidence: 99%

“…By deliberately abstracting away microscopic details such as neuronal signaling, simple models emphasize the emergence of global patterns from the interactions among individual neural elements and allow us to articulate and quantify the behavior of the system as a whole (Raj et al, 2012;Stam et al, 2015;O'Dea et al, 2013;Mi si c et al, 2014aMi si c et al, , 2014bMessé et al, 2015). This approach is complementary to traditional modeling paradigms in computational neuroscience, which aim to reduce large populations of spiking neurons to a distribution of states across time (Deco et al, 2008;Ritter et al, 2013).…”

Section: The Role Of Simple Modelsmentioning

confidence: 96%

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Cooperative and Competitive Spreading Dynamics on the Human Connectome

Mišić

Betzel

Nematzadeh

et al. 2015

Neuron

346

373

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Increasingly detailed data on the network topology of neural circuits create a need for theoretical principles that explain how these networks shape neural communication. Here we use a model of cascade spreading to reveal architectural features of human brain networks that facilitate spreading. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we investigate scenarios where perturbations initiated at seed nodes result in global cascades that interact either cooperatively or competitively. We find that hub regions and a backbone of pathways facilitate early spreading, while the shortest path structure of the connectome enables cooperative effects, accelerating the spread of cascades. Finally, competing cascades become integrated by converging on polysensory associative areas. These findings show that the organizational principles of brain networks shape global communication and facilitate integrative function.

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Section: Resultsmentioning

confidence: 99%

Section: The Role Of Simple Modelsmentioning

confidence: 96%

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Mišić

Betzel

Nematzadeh

et al. 2015

Neuron

346

373

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show abstract

“…In other words, perfect routing predicts no relationship between search information or path transitivity and the strength of FC. The fact that we observe such a relationship implies that neuronal interactions during spontaneous or resting-brain dynamics are not fully accounted for by perfect routing models and instead suggest diffusion or spreading dynamics (35,38) or "greedy routing" strategies (37) as potential candidate models for brain network communication. Specifically, our results demonstrate that the embedding of shortest paths within the network plays an additional important role, in particular the (weighted) degree sequence of the path and the availability of detours.…”

Section: Discussionmentioning

confidence: 99%

Resting-brain functional connectivity predicted by analytic measures of network communication

Goñi

Heuvel

Avena-Koenigsberger

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

581

707

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The complex relationship between structural and functional connectivity, as measured by noninvasive imaging of the human brain, poses many unresolved challenges and open questions. Here, we apply analytic measures of network communication to the structural connectivity of the human brain and explore the capacity of these measures to predict resting-state functional connectivity across three independently acquired datasets. We focus on the layout of shortest paths across the network and on two communication measures-search information and path transitivitywhich account for how these paths are embedded in the rest of the network. Search information is an existing measure of information needed to access or trace shortest paths; we introduce path transitivity to measure the density of local detours along the shortest path. We find that both search information and path transitivity predict the strength of functional connectivity among both connected and unconnected node pairs. They do so at levels that match or significantly exceed path length measures, Euclidean distance, as well as computational models of neural dynamics. This capacity suggests that dynamic couplings due to interactions among neural elements in brain networks are substantially influenced by the broader network context adjacent to the shortest communication pathways.connectome | graph theory | network theory | brain connectivity T he topology and dynamics of brain networks are a central focus of the emerging field of connectomics (1). A growing number of studies of human brain networks carried out with modern noninvasive neuroimaging methods have begun to characterize the architecture of structural networks (2-4), as well as spatially distributed components (5-7) and time-varying dynamics (8) of functional networks. Although structural connectivity (SC) is inferred from diffusion imaging and tractography, functional connectivity (FC) is generally derived from pairwise correlations of time series recorded during "resting" brain activity, measured with functional magnetic resonance imaging (fMRI). Both networks define a multiplex system (9) in which the SC level shapes or imposes constraints on the FC level. Indeed, mounting evidence indicates that SC and FC are robustly related. Numerous studies have documented strong and significant correlations between the strengths of structural and functional connections at whole-brain (2, 10-13) and mesoscopic scales (14), as well as acute changes in FC after perturbation of SC (15).Although there is ample evidence documenting statistical relationships between SC and FC, the causal role of SC in shaping whole-brain patterns of FC is still only incompletely understood. There are numerous reports of strong FC among brain regions that are not directly structurally connected, an effect that has been ascribed to signal propagation along one or more indirect structural paths (11), or to network-wide contextual influence (16). The present paper builds on two interrelated premises. First, if SC plays a major causal role...

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“…While characterizing relatively stable architecture, studies have gradually emphasized the importance of the dynamics of functional network architectures 39,25,54 . However, very few studies have satisfied the following criteria: (1) millisecond temporal resolution, (2) treating the whole-brain as one system, (3) inclusion of structural constraints, and (4) exclusion of computational demands of localized electrical activities.…”

Section: Introductionmentioning

confidence: 99%

Communicability systematically explains transmission speed in a cortical macro-connectome

Shimono

Hatano

2017

Preprint

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Global dynamics in the brain can be captured using fMRI, MEG, or electrocorticography (ECoG), but models are often restricted by anatomical constraints. Complementary single-/multi-unit recordings have described local fast temporal dynamics. However, because of anatomical constraints, global fast temporal dynamics remain incompletely understood. Therefore, we compared temporal aspects of cross-area propagations of single-unit recordings and ECoG, and investigated their anatomical bases. First, we demonstrated how both evoked and spontaneous ECoGs can accurately predict latencies of single-unit recordings. Next, we estimated the propagation velocity (1.0-1.5 m/s) from brain-wide data and found that it was fairly stable among different conscious levels. We also found that the anatomical topology strongly predicted the latencies. Finally, Communicability, a novel graph-theoretic measure, could systematically capture the balance between shorter or longer pathways. These results demonstrate that macroconnectomic perspective is essential for evaluating detailed temporal dynamics in the brain. Author Summary (Not necessary here, just as a reference)This study produced four main findings: First, we demonstrated that ECoG signals could predict the timing of evoked electrical spikes of neurons elicited by visual stimuli. Second, we showed that spontaneous ECoG recorded under a blindfold condition (without any stimuli) could also predict the timing of visually evoked neuronal spikes. We also clarified that performance predictions from blindfold data are essentially supported by the constraints of structural paths. Third, we quantified the propagation velocity (conductance velocity) as 1.0-1.5 m/s, and found that the velocity was stable among different conscious levels. Fourth, Communicability successfully characterized the relative contributions of shorter and longer paths. This study represents an important contribution to the theoretical understanding of the brain in terms of connectomics, dynamical propagations, and multi-scale architectures.

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Spreading dynamics on spatially constrained complex brain networks

Cited by 31 publications

References 40 publications

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Resting-brain functional connectivity predicted by analytic measures of network communication

Communicability systematically explains transmission speed in a cortical macro-connectome

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