Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: The interplay of density, connectivity cost and life-time trajectory

Otte, Willem M.; Diessen, Eric van; Paul, Subhadip; Ramaswamy, Rajiv; Rallabandi, V. P. Subramanyam; Stam, Cornelis J.; Roy, Prasun K.

doi:10.1016/j.neuroimage.2015.01.011

Cited by 60 publications

(93 citation statements)

References 47 publications

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“…We found no gender effects on MST topology, which is in line with previous work (Otte et al, 2015). Several correlations between age and MST characteristics were found, especially kappa, and especially when cortical nodes only were considered.…”

Section: Discussionsupporting

confidence: 93%

“…While previous studies found an inverted U shape of network efficiency across the lifespan, this was not replicated with the MST approach. This finding is in line with a recent study by Otte et al (2015), who described how cortical MST characteristics based on diffusion imaging change across the lifespan. This study together with our analysis suggests that structural brain network efficiency changes across the lifespan may be explained by global changes in connection density rather than topological reorganization.…”

Section: Discussionsupporting

confidence: 92%

“…Several correlations between age and MST characteristics were found, especially kappa, and especially when cortical nodes only were considered. The global pattern that emerges from our analysis as well as previous work is that the resilience of the network to targeted attack is higher and stable during adulthood as compared to late adolescence and older age (Otte et al, 2015). While previous studies found an inverted U shape of network efficiency across the lifespan, this was not replicated with the MST approach.…”

Section: Discussionsupporting

confidence: 71%

“…Following the approach described by Otte et al (2015), we used a linear mixed model to determine MST changes across adulthood. Linear or quadratic age effects were explored using fixed factors of age or age 2 and sex.…”

Section: Methodsmentioning

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: Discussionsupporting

confidence: 93%

Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 71%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

“…the average shortest path length, maximum betweenness centrality or overall clustering coefficient) (Bullmore and Sporns, 2009) and/or network properties such as small-worldness, rich club connectedness (Bullmore and Sporns, 2012;Cao et al, 2014) and modularity (Rubinov and Sporns, 2010). In the past decade, multiple studies have shown that normal aging is associated with substantial alterations in NeuroImage 135 (2016) [79][80][81][82][83][84][85][86][87][88][89][90][91] structural Dennis et al, 2013;Gong et al, 2009;Hagmann et al, 2010;Lim et al, 2015;Montembeault et al, 2012;Otte et al, 2015;Wu et al, 2012;Zhu et al, 2012) and functional (Achard and Bullmore, 2007;Andrews-Hanna et al, 2007;Betzel et al, 2014;Meier et al, 2012;Meunier et al, 2009;Nathan Spreng and Schacter, 2012;Wang et al, 2012) brain networks. Some of these studies focused on specific age categories: childhood to adulthood (Dennis et al, 2013;Hagmann et al, 2010) or young and older adults (e.g.…”

Section: Introductionmentioning

confidence: 99%

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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Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

Cole

2018

BioEssays

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The lack of specificity in neuroimaging studies of neurological and psychiatric diseases suggests that these different diseases have more in common than is generally considered. Potentially, features that are secondary effects of different pathological processes may share common neurobiological underpinnings. Intriguingly, many of these mechanisms are also observed in studies of normal (i.e., non-pathological) brain ageing. Different brain diseases may be causing premature or accelerated ageing to the brain, an idea that is supported by a line of "brain ageing" research that combines neuroimaging data with machine learning analysis. In reviewing this field, I conclude that such observations could have important implications, suggesting that we should shift experimental paradigm: away from characterizing the average case-control brain differences resulting from a disease toward methods that place individuals in their age-appropriate context. This will also lead naturally to clinical applications, whereby neuroimaging can contribute to a personalized-medicine approach to improve brain health.

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Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: The interplay of density, connectivity cost and life-time trajectory

Cited by 60 publications

References 47 publications

Minimum spanning tree analysis of the human connectome

Minimum spanning tree analysis of the human connectome

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

Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

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