The minimum spanning tree: An unbiased method for brain network analysis

Tewarie, Prejaas; Dellen, Edwin van; Hillebrand, Arjan; Stam, Cornelis J.

doi:10.1016/j.neuroimage.2014.10.015

Cited by 308 publications

(462 citation statements)

References 51 publications

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“…This indicates that the majority of MST connections are similar in healthy adults, but individual connections may vary between subjects. We recently showed that MST characteristics reflect properties of the underlying network (Tewarie et al, 2015), and here we tested whether the MST could be used to characterize some key anatomical aspects of the human connectome. MST hubs showed overlap with regions described in literature as hub nodes and rich club nodes in conventional graph analysis (Gong et al, 2009; van den Heuvel and Sporns, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…Such approaches may be appropriate when aiming to map to connectome in more detail on a group level, and take into account cyclic aspects of the network, which are by definition discarded in the MST. Rather, the MST can be used to capture a backbone of the network that reflects global characteristics of organization of the full network, including hierarchical clustering and average local clustering (Tewarie et al, 2015; Yu et al, 2015). We speculate that the MST of the connectome reflects the most important highways for information processing in the human brain.…”

Section: Discussionmentioning

confidence: 99%

“…The MST of this occurrence matrix was computed, the MST occurrence , and the overlap between MST occurrence and MST ref was used to estimate the effect of outliers. Several characteristics of the MST of each subject and of MST ref were calculated to quantify network topology: diameter, leaf fraction, tree hierarchy and kappa, and maximum nodal degree and betweenness centrality (Boersma et al, 2012; Tewarie et al, 2015). Nodal characteristics betweenness centrality and degree were analyzed to describe the role of specific ROIs in the network.…”

Section: Methodsmentioning

confidence: 99%

“…It connects all the nodes in the network without forming cycles or loops. The MST is unaffected by the thresholding problem provided that the ranking of edge weights remains unaltered, while its characteristics can be interpreted along the lines of conventional graph measures (Stam et al, 2014; Tewarie, van Dellen, Hillebrand, & Stam, 2015). Importantly, the MST is a unique subset of connections provided that all connection weights are unique, which makes it possible to create an empirical reference network of the connections in a healthy human brain.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Minimum spanning tree analysis of the human connectome

Dellen

Sommer

Bohlken

et al. 2018

Human Brain Mapping

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One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion‐weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null‐model. The MST of individual subjects matched this reference MST for a mean 58%–88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so‐called rich club nodes (a subset of high‐degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical–subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimum spanning tree analysis of the human connectome

Dellen

Sommer

Bohlken

et al. 2018

Human Brain Mapping

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show abstract

“…The difference between functional connectivity and functional network organization is that functional connectivity refers to the strength of connections between brain regions, whereas functional network organization (topology) corresponds to the pattern of connections between regions in the brain. Specifically, we assess network organization by computing two network topology measures based on the minimum spanning tree (MST) (Stam et al, 2014; Tewarie et al, 2015b): diameter and leaf fraction. (c) Diameter is defined as the longest shortest path within the MST and (d) leaf fraction refers to the fraction of regions in the MST with only one connection.…”

Section: Methodsmentioning

confidence: 99%

Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study

Tewarie

Steenwijk

Brookes

et al. 2018

Human Brain Mapping

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To understand the heterogeneity of functional connectivity results reported in the literature, we analyzed the separate effects of grey and white matter damage on functional connectivity and networks in multiple sclerosis. For this, we employed a biophysical thalamo‐cortical model consisting of interconnected cortical and thalamic neuronal populations, informed and amended by empirical diffusion MRI tractography data, to simulate functional data that mimic neurophysiological signals. Grey matter degeneration was simulated by decreasing within population connections and white matter degeneration by lowering between population connections, based on lesion predilection sites in multiple sclerosis. For all simulations, functional connectivity and functional network organization are quantified by phase synchronization and network integration, respectively. Modeling results showed that both cortical and thalamic grey matter damage induced a global increase in functional connectivity, whereas white matter damage induced an initially increased connectivity followed by a global decrease. Both white and especially grey matter damage, however, induced a decrease in network integration. These empirically informed simulations show that specific topology and timing of structural damage are nontrivial aspects in explaining functional abnormalities in MS. Insufficient attention to these aspects likely explains contradictory findings in multiple sclerosis functional imaging studies so far.

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Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application

Li,

Jin,

et al. 2024

Advanced Science

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Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good‐to‐excellent test‐retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co‐expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co‐expression, and is associated with serotonergic system‐related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

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The minimum spanning tree: An unbiased method for brain network analysis

Cited by 308 publications

References 51 publications

Minimum spanning tree analysis of the human connectome

Minimum spanning tree analysis of the human connectome

Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application

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