Cooperative and Competitive Spreading Dynamics on the Human Connectome

Mišić, Bratislav; Betzel, Richard F.; Nematzadeh, Azadeh; Goñi, Joaquín; Griffa, Alessandra; Hagmann, Patric; Flammini, Alessandro; Ahn, Yong‐Yeol; Sporns, Olaf

doi:10.1016/j.neuron.2015.05.035

Cited by 355 publications

(397 citation statements)

References 72 publications

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“…By contrast, the global measure of betweenness centrality showed minimal correlations to dynamics (potentially related to the fact that information transmission across brain networks may more closely follow an unguided, diffusion-like process rather than shortest paths 6,16,18 ). Weighted in-degree was significantly correlated to 229 rs-fMRI time-series properties (p corrected < 0:05, from a set of 6930 features), with partial Spearman correlation coefficients reaching up to jqj ¼ 0:58 (for linear autocorrelation at lag s ¼ 34 s).…”

Section: Discussionmentioning

confidence: 88%

“…14 These results are impressive given the known limitations of diffusion MRI in reconstructing anatomical brain connections. 7,15 The success of dynamical systems models, as well as simplified network spreading models, [16][17][18] in reproducing the correlation structure of inter-regional brain dynamics suggests that the structural connectome plays a key role in constraining brain dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Sethi

Zerbi

Wenderoth

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Brain dynamics are thought to unfold on a network determined by the pattern of axonal connections linking pairs of neuronal elements; the so-called connectome. Prior work has indicated that structural brain connectivity constrains pairwise correlations of brain dynamics ("functional connectivity"), but it is not known whether inter-regional axonal connectivity is related to the intrinsic dynamics of individual brain areas. Here we investigate this relationship using a weighted, directed mesoscale mouse connectome from the Allen Mouse Brain Connectivity Atlas and resting state functional MRI (rs-fMRI) time-series data measured in 184 brain regions in eighteen anesthetized mice. For each brain region, we measured degree, betweenness, and clustering coefficient from weighted and unweighted, and directed and undirected versions of the connectome. We then characterized the univariate rs-fMRI dynamics in each brain region by computing 6930 time-series properties using the time-series analysis toolbox, hctsa. After correcting for regional volume variations, strong and robust correlations between structural connectivity properties and rs-fMRI dynamics were found only when edge weights were accounted for, and were associated with variations in the autocorrelation properties of the rs-fMRI signal. The strongest relationships were found for weighted in-degree, which was positively correlated to the autocorrelation of fMRI time series at time lag Nervous systems are complex networks with a topology governed by the pattern of axonal connections linking distinct neural elements. Highly connected and topologically central elements are thought to play an important role in meditating the flow of information across different parts of the system. However, it is unclear how the intrinsic dynamics of a given neuronal population relates to the pattern of connections that population shares with other network nodes. In this work, we show that there is a strong and robust correlation between the structural connectivity properties of a brain region and its blood-oxygenation-level-dependent (BOLD) signal dynamics, as measured with resting-state fMRI (rs-fMRI) in the mouse. The strongest relationship is found with the total weight of incoming connections to a brain region, or weighted in-degree, which is associated with longer dynamical timescales of rs-fMRI dynamics. Our findings indicate that structural connection weights convey important information about neural activity, and that the aggregate strength of incoming projections to a brain region is closely related to its BOLD signal dynamics.

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Section: Discussionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Sethi

Zerbi

Wenderoth

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

View full text Add to dashboard Cite

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“…Alternative consensus-based thresholding methods may be able to better preserve distance effects between individuals and the consensus matrix (Misić et al, 2015). Physical connection lengths were approximated as the Euclidean (straight line) distance between regional center of masses (Betzel et al, 2016).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Brain network efficiency has been widely studied (Bullmore and Sporns, 2012) and is closely related to the concept of small-world networks (Latora and Marchiori, 2011) and network communication (Goñi et al, 2014;Misić et al, 2015). To compute efficiency, the shortest path lengths were first determined between all possible pairs of nodes.…”

Section: Network Efficiencymentioning

confidence: 99%

Connectome sensitivity or specificity: which is more important?

et al. 2016

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“…Empirical studies and computational models suggest that the topology of structural connections constrains the flow of neural signals across the network (Passingham et al 2002;Galán 2008;Honey et al 2009;Park and Friston 2013;Hermundstad et al 2013;Goñi et al 2014;Mišić et al 2015) and shapes the statistical dependencies among regional time courses of neuronal responses, generally captured in functional brain networks (Friston 2011). Furthermore, several studies have pointed out that the structural organization of the connectome optimizes the trade-off between network cost and competing functional demands, including efficient communication (Bullmore and Sporns 2012;Vértes et al 2012;Betzel et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Path ensembles and a tradeoff between communication efficiency and resilience in the human connectome

et al. 2016

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Computational analysis of communication efficiency of brain networks often relies on graph-theoretic measures based on the shortest paths between network nodes. Here, we explore a communication scheme that relaxes the assumption that information travels exclusively through optimally short paths. The scheme assumes that communication between a pair of brain regions may take place through a path ensemble comprising the k-shortest paths between those regions. To explore this approach, we map path ensembles in a set of anatomical brain networks derived from diffusion imaging and tractography. We show that while considering optimally short paths excludes a significant fraction of network connections from participating in communication, considering k-shortest path ensembles allows all connections in the network to contribute. Path ensembles enable us to assess the resilience of communication pathways between brain regions, by measuring the number of alternative, disjoint paths within the ensemble, and to compare generalized measures of path length and betweenness centrality to those that result when considering only the single shortest path between node pairs. Furthermore, we find a significant correlation, indicative of a trade-off, between communication efficiency and resilience of communication pathways in structural brain networks. Finally, we use k-shortest path ensembles to demonstrate hemispherical lateralization of efficiency and resilience.

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Cooperative and Competitive Spreading Dynamics on the Human Connectome

Cited by 355 publications

References 72 publications

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Structural connectome topology relates to regional BOLD signal dynamics in the mouse brain

Connectome sensitivity or specificity: which is more important?

Path ensembles and a tradeoff between communication efficiency and resilience in the human connectome

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