Whole-brain anatomical networks: Does the choice of nodes matter?

Zalesky, Andrew; Fornito, Alexander; Harding, Ian H.; Cocchi, Luca; Yücel, Murat; Pantelis, Christos; Bullmore, Edward T.

doi:10.1016/j.neuroimage.2009.12.027

Cited by 1,104 publications

(999 citation statements)

References 61 publications

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“…The topology of a network depends not only on the definition of connections, but also on the definition of nodes, in this case the brain regions that are considered to be part of the connectome (Zalesky et al, 2010). It remains unclear how brain network analysis may be influenced by the use of atlases to define cortical regions, or the exclusion of subcortical brain regions.…”

Section: Introductionmentioning

confidence: 99%

“…We studied the effect of using different scanners at 1.5 T and 3 T, the effect of different scanning sites, and processing pipelines. The topology of a network depends not only on the definition of connections, but also on the definition of nodes (Zalesky et al, 2010). We therefore studied the effects of using different brain atlases for node definitions, and the exclusion of subcortical brain regions.…”

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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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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimum spanning tree analysis of the human connectome

Dellen

Sommer

Bohlken

et al. 2018

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

show abstract

“…An ANOVA test was performed to compare all the nine weighted functional networks with the weighted classical network. The calculation for both networks was restricted only to the nodes belonging to the network of interest due to the number of nodes of a network have an strong effect in resulting topology (Zalesky, Fornito, Harding, et al., 2010). Column 3 in Table 1 shows the number of non‐isolated nodes for each network.…”

Section: Methodsmentioning

confidence: 99%

A method for independent component graph analysis of resting‐state fMRI

et al. 2017

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IntroductionIndependent component analysis (ICA) has been extensively used for reducing task‐free BOLD fMRI recordings into spatial maps and their associated time‐courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non‐contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data.ObjectiveHere, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.MethodsFirst, ICA was performed at the single‐subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple‐template matching procedure and a subsequent component classification based on the network “neuronal” properties. Second, for each of the identified networks, the nodes were defined as 1,015 anatomically parcellated regions. Third, between‐node functional connectivity was established by building edge weights for each networks. Group‐level graph analysis was finally performed for each network and compared to the classical network.ResultsNetwork graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small‐worldness.ConclusionsThis novel approach permits us to take advantage of the well‐recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well‐established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength.

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“…First, the threshold is arbitrarily chosen to have a particular value. In a substantial part of the literature, the threshold that is used to transform the streamline distribution into a network is actually set to zero (Hag-mann et al, 2007(Hag-mann et al, , 2008Zalesky et al, 2010;Vaessen et al, 2010;Chung et al, 2011). However, probabilistic streamlining depends on the arbitrary number of samples that are drawn per voxel.…”

Section: Introductionmentioning

confidence: 99%

“…This implies that, as more samples are drawn, more brain regions are likely to eventually become connected given a threshold at zero. Alternatively, the number of streamlines can be interpreted as connection weight (Bassett et al, 2011;Zalesky et al, 2010;Robinson et al, 2010), or a relative threshold can be applied (Kaden et al, 2007). This way, the relative differences between connections remain respected.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference of structural brain networks

et al. 2013

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Structural brain networks are used to model white-matter connectivity between spatially segregated brain regions. The presence, location and orientation of these white matter tracts can be derived using diffusion-weighted magnetic resonance imaging in combination with probabilistic tractography. Unfortunately, as of yet, none of the existing approaches provide an undisputed way of inferring brain networks from the streamline distributions which tractography produces. Stateof-the-art methods rely on an arbitrary threshold or, alternatively, yield weighted results that are difficult to interpret. In this paper, we provide a generative model that explicitly describes how structural brain networks lead to observed streamline distributions. This allows us to draw principled conclusions about brain networks, which we validate using simultaneously acquired resting-state functional MRI data. Inference may be further informed by means of a prior which combines connectivity estimates from multiple subjects. Based on this prior, we obtain networks that significantly improve on the conventional approach.

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Whole-brain anatomical networks: Does the choice of nodes matter?

Cited by 1,104 publications

References 61 publications

Minimum spanning tree analysis of the human connectome

Minimum spanning tree analysis of the human connectome

A method for independent component graph analysis of resting‐state fMRI

Bayesian inference of structural brain networks

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