Elsevier

NeuroImage

Volume 109, 1 April 2015, Pages 171-189
NeuroImage

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

https://doi.org/10.1016/j.neuroimage.2015.01.011Get rights and content

Highlights

  • •

    Altered network density in the elderly compromises lifespan network analysis.

  • •

    Focusing on the critical structural network backbone circumvents this problem.

  • •

    We characterized minimum spanning tree backbones in 382 healthy subjects.

  • •

    Tree-based metrics showed linear and non-linear trajectories across adulthood.

  • •

    Trajectories are in close accordance with previous histopathological data.

Abstract

The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely compromises the usefulness of network analysis to study the aging brain. We successfully circumvented this problem by focusing on the critical structural network backbone, using a robust tree representation. Whole-brain networks' minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.

Introduction

Characterizing the functional and structural changes in the brain across the human lifespan is invaluable to increase our understanding of the associated decline in executive functions and memory during aging, even in the absence of disease (Burke and Barnes, 2006). Age-related declines might arise from alterations in specific brain regions or from modification in the interplay of structural and functional connectivity between distinct regions (Andrews-Hanna et al., 2007, Bishop et al., 2010).

A method to characterize the structural and functional organization of the brain is network analysis (Bullmore and Sporns, 2009). It provides quantitative information on the topological properties of networks, including economical small-world properties, highly connected hubs, and modularity (Rubinov and Sporns, 2010). Network analysis has greatly contributed to the knowledge on the network organization across the lifespan (Uddin et al., 2010, Cao et al., 2014). Normal aging corresponds with alterations in topology both in functional (Achard and Bullmore, 2007, Meunier et al., 2009, Meier et al., 2012, Spreng and Schacter, 2012, Wang et al., 2012) and structural brain networks (Gong et al., 2009, Hagmann et al., 2010, Montembeault et al., 2012, Wu et al., 2012, Zhu et al., 2012, Dennis et al., 2013). These alterations can be summarized as reduced global efficiency, increased local network clustering and reduced centrality of hub regions in the elderly.

Despite the potential of network analysis to quantify topological modifications, the methodology is significantly hampered in the comparison of networks with different densities and degree distributions (Van Wijk et al., 2010, Fornito et al., 2013). This is due to the topological metrics being highly dependent on the percentage of connections available in a network and the number of connections per network node (Stam et al., 2014). These limitations critically undermine the comparability of network topology across the human lifespan as densities are known to substantially alter with increasing age (Gong et al., 2009, Hagmann et al., 2010, Dennis et al., 2013). Possible reasons are reduced white matter volume and accelerated decrease in white matter integrity measures, such as fractional anisotropy, in senescence (Westlye et al., 2010). Furthermore, the neocortical neuronal density massively diminishes between late childhood and old age, marked by white matter degradation with axonal atrophy of up to 15% of all myelinated fibers in old aged healthy persons (Salat, 2011).

The effect of density and degree on topological characterization is not solved using common correction approaches such as using weighted graphs or metric normalization based on null models (Rubinov and Sporns, 2010, Van Wijk et al., 2010). A solution to circumvent these comparability issues due to differences in density and degree distribution is offered by the minimum spanning tree (MST) method (Stam et al., 2014). The MST is the backbone of a network mostly comprising the strongest connections. This facilitates network comparisons without normalization or standardization steps. The usefulness of the MST method in the comparison of healthy and diseased groups has recently been shown in capturing subtle developmental brain network changes (Stam et al., 2014).

We determined whole-brain network topological metrics during adulthood using structural networks obtained from diffusion magnetic resonance imaging in 382 healthy subjects in the age-range of 20 to 86 years. We characterized the age-related changes in MST metrics and compared those with whole-brain network metrics obtained from classical network analysis. We hypothesized that we would be able to detect: i) decreased network density in the elderly, ii) significant age-related changes in the MST and classical network metrics and iii) a large effect of network density on classical network metrics, in contrast to the MST metrics.

Section snippets

Dataset

Standardized high quality, diffusion tensor imaging (DTI) and T1-weighted datasets were obtained from the freely available ‘Information extraction from images’ database (http://biomedic.doc.ic.ac.uk/brain-development/). DTI is an MRI technique that enables the characterization of directional water diffusion in white matter bundles. It is used to map structural connectivity between remote brain regions using streamline fiber tracking. We included scans from 382 healthy adults recruited from the

Local network properties

Averaged original networks and corresponding MSTs (summarized in three age ranges) are similar in terms of overall edge location (Fig. 2). In the original networks the most important hub-nodes include the superior division of the lateral occipital cortex, the precentral gyrus, the frontal pole, the insular cortex, the precuneous cortex, the posterior division of the cingulate gyrus and the occipital pole, all in both hemispheres. The ranking differs between the three age ranges (Fig. 3).

We find

Discussion

We characterized structural brain network alterations across human adulthood in healthy subjects. Previous studies have provided valuable insights on this topic (Achard and Bullmore, 2007, Gong et al., 2009, Meunier et al., 2009, Hagmann et al., 2010, Meier et al., 2012, Montembeault et al., 2012, Spreng and Schacter, 2012, Wang et al., 2012, Wu et al., 2012, Zhu et al., 2012, Dennis et al., 2013) but were prone to suffer from methodological limitations inherent to the classical network

Contributors

WMO, EvD and PKR were involved in designing of the study and model building. WMO, RR, SP and VPSR performed the analysis. All authors contributed to the data interpretation, additional analysis and drafting of the manuscript.

Acknowledgments

WMO and PKR were supported by University of Utrecht Visiting Fellowship Grants (grant numbers: 2013–2014 and 2011–2012, respectively) and by grants from the National Knowledge Network, Office of the Principal Scientific Adviser, Government of India. SP was supported by the Dept. of Biotechnology, Govt. of India and a University of Utrecht Short Stay PhD Fellowship grant. VPSR and RR were supported by the Perception Engineering Program of the Dept. of Information Technology, Govt. of India.

References (52)

  • D. Meunier et al.

    Age-related changes in modular organization of human brain functional networks

    Neuroimage

    (2009)
  • M. Montembeault et al.

    The impact of aging on gray matter structural covariance networks

    Neuroimage

    (2012)
  • M. Reisert et al.

    Global fiber reconstruction becomes practical

    Neuroimage

    (2011)
  • M. Rubinov et al.

    Complex network measures of brain connectivity: uses and interpretations

    Neuroimage

    (2010)
  • M. Rubinov et al.

    Weight-conserving characterization of complex functional brain networks

    Neuroimage

    (2011)
  • C.J. Stam et al.

    The organization of physiological brain networks

    Clin. Neurophysiol.

    (2012)
  • C.J. Stam et al.

    The trees and the forest: characterization of complex brain networks with minimum spanning trees

    Int. J. Psychophysiol.

    (2014)
  • W. Zhu et al.

    Changing topological patterns in normal aging using large-scale structural networks

    Neurobiol. Aging

    (2012)
  • S. Achard et al.

    Efficiency and cost of economical brain functional networks

    PLoS Comput. Biol.

    (2007)
  • A. Alexander-Bloch et al.

    Imaging structural co-variance between human brain regions

    Nat. Rev. Neurosci.

    (2013)
  • C. Beaulieu

    The basis of anisotropic water diffusion in the nervous system — a technical review

    NMR Biomed.

    (2002)
  • N.A. Bishop et al.

    Neural mechanisms of ageing and cognitive decline

    Nature

    (2010)
  • E. Bullmore et al.

    Complex brain networks: graph theoretical analysis of structural and functional systems

    Nat. Rev. Neurosci.

    (2009)
  • S.N. Burke et al.

    Neural plasticity in the ageing brain

    Nat. Rev. Neurosci.

    (2006)
  • E.W. Dijkstra

    A note on two problems in connexion with graphs

    Numer. Math.

    (1959)
  • F.U. Fischer et al.

    Association of structural global brain network properties with intelligence in normal aging

    PLoS ONE

    (2014)

    • Cited by (46)

    • Altered brain network topology during speech tracking in developmental dyslexia

      2022, NeuroImage

    • Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia

      2022, Neurobiology of Aging

    • Genetic influence on ageing-related changes in resting-state brain functional networks in healthy adults: A systematic review

      2020, Neuroscience and Biobehavioral Reviews
      Citation Excerpt :

      Resting-state functional connectivity (RSFC) measures are commonly derived from resting-state functional magnetic resonance imaging (rs-fMRI) data, which examine the synchronisation of neural activity between regions by measuring the blood oxygen level-dependent (BOLD) signal fluctuations that occur at low frequencies (<0.1 Hz), where participants are scanned in the absence of a stimulus or task (Lv et al., 2018; Rosazza and Minati, 2011). Characterising resting-state functional changes in the ageing brain may increase our understanding of age-associated cognitive changes, even in the absence of disease (Burke and Barnes, 2006; Otte et al., 2015) Many studies have explored the effect of age on RSFC either by using region of interest-based correlations (i.e. seed-based) or data-driven reduction techniques (i.e. independent component analysis - ICA), or whole brain approaches (i.e. graph theoretical analysis) (Grayson and Fair, 2017; Lee et al., 2013).

    • fMRI network correlates of predisposing risk factors for delirium: A cross-sectional study

      2020, NeuroImage: Clinical
      Citation Excerpt :

      The association of the individual risk factors on the five outcome measures (i.e. global functional connectivity strength, MST diameter, MST leaf fraction, PCC-DLPFC left and PCC-DLPFC right connectivity strength) were analyzed in separate linear regression models. As age, gender and IQ can be considered as confounders for delirium and network outcomes, we adjusted for center, age (if age was not the determinant), gender and IQ in the analyses (Marcantonio, 2017; Otte et al., 2015; Stumme et al., 2020; van Dellen et al., 2018). The associations of all seven risk factors combined on the five outcome measures, adjusted for center, gender and IQ, were studied with three different multivariable linear regression models.

    • The power of language: Functional brain network topology of deaf and hearing in relation to sign language experience

      2019, Hearing Research
      Citation Excerpt :

      Network backbones are robustly and efficiently operationalized by the minimum spanning tree (MST) (Stam et al., 2014; Tewarie et al., 2015). An increasing number of studies have shown the usefulness of MSTs in capturing subtle network changes in brain development and ageing (Boersma et al., 2013; Otte et al., 2015; Smit et al., 2016; Vourkas et al., 2014). MSTs have also been useful in characterizing multiple sclerosis, Alzheimer's disease and epilepsy (Engels et al., 2015; Tewarie et al., 2014; van Diessen et al., 2016, 2014; Sinke et al., 2018).

    • Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

      2019, NeuroImage: Clinical
      Citation Excerpt :

      Our literature search resulted in 24442 hits of which 126 studies met our inclusion criteria (Fig. A.2). These 126 publications described in total 151 cases on different predisposing risk factors, precipitating risk factors or delirium (i.e. if a publication explored more than one risk factor separately, we took the comparison of each risk factor as a separate case) (van Dellen et al., 2014; Zhu et al., 2012; Chen et al., 2011; Wu et al., 2013; Otte et al., 2015; Lim et al., 2015; Gong et al., 2009; Geerligs et al., 2015; Song et al., 2014; Ferreira et al., 2016; Cao et al., 2014; Chan et al., 2014; Onoda and Yamaguchi, 2013; Meunier et al., 2009; Liu et al., 2014a; Knyazev et al., 2015; Vecchio et al., 2014; Micheloyannis et al., 2009; Vysata et al., 2014; Smit et al., 2016; Yao et al., 2010; Phillips et al., 2015; Pereira et al., 2015; Li et al., 2012; Li et al., 2016; Wang et al., 2016a; Bai et al., 2012; Daianu et al., 2013; Morris et al., 2014; Shu et al., 2012; Vaessen et al., 2012; Tang et al., 2015; Zhao et al., 2017; Yi et al., 2015; Minati et al., 2014; Chang et al., 2016; Yu et al., 2015; Baggio et al., 2014; Wang et al., 2013; Xiang et al., 2013; Brier et al., 2013; Kim et al., 2015; Sun et al., 2014; Liu et al., 2012; Sang et al., 2018; Sanabria-Diaz et al., 2013; Seo et al., 2013; Zeng et al., 2015; Frantzidis et al., 2014; Koenig et al., 2005; Pineda-Pardo et al., 2014; Gomez et al., 2009; López-Sanz et al., 2017; Achard et al., 2012; Crone et al., 2014; van Montfort et al., 2018; Chen et al., 2018a; Brandt et al., 2019; Numan et al., 2017; He et al., 2008; Liu et al., 2014b; Tijms et al., 2013b; John et al., 2017; Wang et al., 2016b; Lo et al., 2010; Reijmer et al., 2013; Agosta et al., 2013; Sanz-Arigita et al., 2010; Zhao et al., 2012; Supekar et al., 2008; Qin et al., 2015; Peraza et al., 2015; Filippi et al., 2017; de Haan et al., 2009; Stam et al., 2007; van Dellen et al., 2015; Afshari and Jalili, 2017; Berendse et al., 2008; De Haan et al., 2012; Ajilore et al., 2014a; Lim et al., 2013; Singh et al., 2013; Lee et al., 2018; Chen et al., 2016a; Mak et al., 2016; Korgaonkar et al., 2014; Ajilore et al., 2014b; Qin et al., 2014; Charlton et al., 2015; Nigro et al., 2015; Long et al., 2015; Chen et al., 2016b; Bohr et al., 2013; Meng et al., 2014; Lord et al., 2012; Luo et al., 2015; Zhang et al., 2011; Jin et al., 2011; Ye et al., 2015; Wang et al., 2016c; Ye et al., 2016; Leuchter et al., 2012; Shim et al., 2018; Zhang et al., 2014; Jao et al., 2015; Li et al., 2014; Kim et al., 2014a; Xu et al., 2016; Zhang et al., 2018; Wang et al., 2015; Ma et al., 2015; Monti et al., 2013; Hashmi et al., 2017; Lee et al., 2013; Blain-Moraes et al., 2017; Shi et al., 2013; Guo et al., 2014; Kim et al., 2014b; Caeyenberghs et al., 2014; Messé et al., 2013; Han et al., 2014; van der Horn et al., 2017; Maestú et al., 2010; Shu et al., 2009; Bola et al., 2014; Wang et al., 2012). For a detailed overview of included studies, investigated risk factors, measurement techniques, outcomes and quality scores see Table A.3.

    View all citing articles on Scopus
    View full text
    Copyright © 2015 Elsevier Inc. All rights reserved.