The hubs of the human connectome are generally implicated in the anatomy of brain disorders

Crossley, Nicolás; Mechelli, Andrea; Scott, Jessica; Carletti, Francesco; Fox, Peter T.; McGuire, Philip; Bullmore, Edward T.

doi:10.1093/brain/awu132

Cited by 1,003 publications

(997 citation statements)

References 80 publications

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“…This may be due to the overall decrease in network density across age, perhaps in combination with increased vulnerability due to high intrinsic neuronal activity of hub node regions (de Haan et al, 2012). Our findings are in line with previous simulation studies in human and animals, which have shown that computational lesioning of hub regions has stronger effects on structural and functional network topology than lesioning of random nodes (Alstott et al, 2009;Crossley et al, 2014;Kaiser et al, 2007).…”

Section: Discussionsupporting

confidence: 90%

“…In general, the aging brain can be characterized by reduced centrality of hub regions with a decrease in global efficiency and an increase in local network clustering. Similar changes in hub regions and subsequent effects on global efficiency have also been characterized in various neurological disorders (Crossley et al, 2014;Stam, 2014).…”

Section: Introductionsupporting

confidence: 56%

“…Hub-node lesioning resulted in lower GW NSP and higher hemispheric nodematches. These two trends increased with higher age, which may indicate that older people are more vulnerable to hubnode damage (possibly associated with degenerative disease such as Alzheimer's disease (Crossley et al, 2014)) than younger people. This may be due to the overall decrease in network density across age, perhaps in combination with increased vulnerability due to high intrinsic neuronal activity of hub node regions (de Haan et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

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Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

Sinke

Dijkhuizen

Caimo³

et al. 2016

NeuroImage

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a b s t r a c t a r t i c l e i n f oDescriptive neural network analyses have provided important insights into the organization of structural and functional networks in the human brain. However, these analyses have limitations for inter-subject or between-group comparisons in which network sizes and edge densities may differ, such as in studies on neurodevelopment or brain diseases. Furthermore, descriptive neural network analyses lack an appropriate generic null model and a unifying framework. These issues may be solved with an alternative framework based on a Bayesian generative modeling approach, i.e. Bayesian exponential random graph modeling (ERGM), which explains an observed network by the joint contribution of local network structures or features (for which we chose neurobiologically meaningful constructs such as connectedness, local clustering or global efficiency). We aimed to identify how these local network structures (or features) are evolving across the life-span, and how sensitive these features are to random and targeted lesions. To that aim we applied Bayesian exponential random graph modeling on structural networks derived from whole-brain diffusion tensor imaging-based tractography of 382 healthy adult subjects (age range: 20.2-86.2 years), with and without lesion simulations. Networks were successfully generated from four local network structures that resulted in excellent goodness-of-fit, i.e. measures of connectedness, local clustering, global efficiency and intrahemispheric connectivity. We found that local structures (i.e. connectedness, local clustering and global efficiency), which give rise to the global network topology, were stable even after lesion simulations across the lifespan, in contrast to overall descriptive network changes -e.g. lower network density and higher clustering -during aging, and despite clear effects of hub damage on network topologies. Our study demonstrates the potential of Bayesian generative modeling to characterize the underlying network structures that drive the brain's global network topology at different developmental stages and/or under pathological conditions.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionsupporting

confidence: 56%

Section: Discussionmentioning

confidence: 99%

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Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

Sinke

Dijkhuizen

Caimo³

et al. 2016

NeuroImage

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“…Another option is to reduce the number of tests by using measures such as the network‐based statistic (Zalesky, Fornito, & Bullmore, 2010), or to consider graph theoretical measures that produce node‐ or graph‐level values (Kaiser, 2011; Rubinov & Sporns, 2010). This approach has been used in several studies (Betzel et al., 2014; Crossley et al., 2014; Fornito et al., 2011; Fornito, Zalesky, Pantelis, & Bullmore, 2012; Zhou, Gennatas, Kramer, Miller, & Seeley, 2012), however, it fundamentally shifts the research focus from identification of relevant connections to the interpretation of measures that often do not have a known relation to neuro‐biology (Smith, 2012). Lastly, this study included individuals from the general population, rather than solely recruiting “typically developing” children from the community.…”

Section: Discussionmentioning

confidence: 99%

Differential patterns of age‐related cortical and subcortical functional connectivity in 6‐to‐10 year old children: A connectome‐wide association study

et al. 2018

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IntroductionTypical brain development is characterized by specific patterns of maturation of functional networks. Cortico‐cortical connectivity generally increases, whereas subcortico‐cortical connections often decrease. Little is known about connectivity changes amongst different subcortical regions in typical development.MethodsThis study examined age‐ and gender‐related differences in functional connectivity between and within cortical and subcortical regions using two different approaches. The participants included 411 six‐ to ten‐year‐old typically developing children sampled from the population‐based Generation R study. Functional connectomes were defined in native space using regions of interest from subject‐specific FreeSurfer segmentations. Connections were defined as: (a) the correlation between regional mean time‐series; and (b) the focal maximum of voxel‐wise correlations within FreeSurfer regions. The association of age and gender with each functional connection was determined using linear regression. The preprocessing included the exclusion of children with excessive head motion and scrubbing to reduce the influence of minor head motion during scanning.ResultsCortico‐cortical associations echoed previous findings that connectivity shifts from short to long‐range with age. Subcortico‐cortical associations with age were primarily negative in the focal network approach but were both positive and negative in the mean time‐series network approach. Between subcortical regions, age‐related associations were negative in both network approaches. Few connections had significant associations with gender.ConclusionsThe present study replicates previously reported age‐related patterns of connectivity in a relatively narrow age‐range of children. In addition, we extended these findings by demonstrating decreased connectivity within the subcortex with increasing age. Lastly, we show the utility of a more focal approach that challenges the spatial assumptions made by the traditional mean time series approach.

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“…A general backbone of cortico‐cortical highways has been described in the healthy human brain using graph theoretical analysis of diffusion weighted imaging tractography, which includes connections between a subset of hub nodes, the so‐called rich club (Hagmann et al, 2008; van den Heuvel, Kahn, Goni, & Sporns, 2012). Mounting evidence suggests that structural brain networks, particularly their backbone, are altered in neurological and psychiatric disorders, and heterogeneity in these alterations is evident between diseases (Bullmore and Sporns, 2009; Crossley et al, 2014; Stam, 2014). …”

Section: Introductionmentioning

confidence: 99%

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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The hubs of the human connectome are generally implicated in the anatomy of brain disorders

Cited by 1,003 publications

References 80 publications

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

Bayesian exponential random graph modeling of whole-brain structural networks across lifespan

Differential patterns of age‐related cortical and subcortical functional connectivity in 6‐to‐10 year old children: A connectome‐wide association study

Minimum spanning tree analysis of the human connectome

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