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Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines

  • Author Footnotes
    1 JWM and SJR contributed equally to this work.
    James W. Madole
    Correspondence
    Address correspondence to James W. Madole, M.A.
    Footnotes
    1 JWM and SJR contributed equally to this work.
    Affiliations
    Department of Psychology, University of Texas at Austin, Austin, Texas
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  • Author Footnotes
    1 JWM and SJR contributed equally to this work.
    Stuart J. Ritchie
    Footnotes
    1 JWM and SJR contributed equally to this work.
    Affiliations
    Social, Genetic and Developmental Psychiatry Centre, King’s College London, London, United Kingdom
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  • Simon R. Cox
    Affiliations
    Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom

    Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
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  • Colin R. Buchanan
    Affiliations
    Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom

    Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
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  • Maria Valdés Hernández
    Affiliations
    Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

    Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
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  • Susana Muñoz Maniega
    Affiliations
    Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

    Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
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  • Joanna M. Wardlaw
    Affiliations
    Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

    Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
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  • Mathew A. Harris
    Affiliations
    Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
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  • Mark E. Bastin
    Affiliations
    Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom

    Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
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  • Ian J. Deary
    Affiliations
    Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom

    Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
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  • Elliot M. Tucker-Drob
    Affiliations
    Department of Psychology, University of Texas at Austin, Austin, Texas

    Population Research Center, University of Texas at Austin, Austin, Texas
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  • Author Footnotes
    1 JWM and SJR contributed equally to this work.
Open AccessPublished:June 15, 2020DOI:https://doi.org/10.1016/j.biopsych.2020.06.010

      Abstract

      Background

      Aging-related cognitive decline is a primary risk factor for Alzheimer’s disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias.

      Methods

      Using structural and diffusion brain magnetic resonance imaging data from the UK Biobank (n = 8185; age range, 45–78 years), we examined aging of regional gray matter volumes (nodes) and white matter structural connectivity (edges) within 9 well-characterized networks of interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (n = 534; all 73 years of age), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function before and after controlling for early-life cognitive ability.

      Results

      In the UK Biobank, age differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (|rnodes| = .420; |redges| = .583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In the Lothian Birth Cohort 1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the central executive network were disproportionately predictive of late-life cognitive function relative to the network’s small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability.

      Conclusions

      These results implicate global dimensions of variation in the human structural connectome in aging-related cognitive decline. The central executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments.

      Keywords

      Nonclinical variation in cognitive decline is a primary risk factor for Alzheimer’s disease and related dementias (
      • Park H.L.
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      A systematic review of cognitive decline in the general elderly population.
      ,
      • Tucker-Drob E.M.
      Cognitive aging and dementia: A life-span perspective.
      ). These declines have consequences both for individuals, who may be less able to perform important everyday functions (
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      Mild cognitive impairment and deficits in instrumental activities of daily living: A systematic review.
      ,
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      Neurocognitive functions and everyday functions change together in old age.
      ), and for aging societies, whose workforce productivity and social and medical resources may be prematurely exhausted (
      • Comas-Herrera A.
      • Wittenberg R.
      • Pickard L.
      • Knapp M.
      Cognitive impairment in older people: Future demand for long-term care services and the associated costs.
      ). Delineating the neurodegenerative processes underlying aging-related cognitive decline may crucially advance our ability to detect and ultimately prevent or mitigate aging-related cognitive impairments.
      The human brain exhibits widespread structural changes with aging (
      • Fjell A.M.
      • Walhovd K.B.
      Structural brain changes in aging: Courses, causes and cognitive consequences.
      ), the patterning of which is only partly documented (
      • Cox S.R.
      • Ritchie S.J.
      • Tucker-Drob E.M.
      • Liewald D.C.
      • Hagenaars S.P.
      • Davies G.
      • et al.
      Ageing and brain white matter structure in 3,513 UK Biobank participants.
      ). Measures of whole- and regional-brain volumes (
      • Fjell A.M.
      • McEvoy L.
      • Holland D.
      • Dale A.M.
      • Walhovd K.B.
      What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus.
      ,
      • Pietschnig J.
      • Penke L.
      • Wicherts J.M.
      • Zeiler M.
      • Voracek M.
      Meta-analysis of associations between human brain volume and intelligence differences: How strong are they and what do they mean?.
      ,
      • Gignac G.E.
      • Bates T.C.
      Brain volume and intelligence: The moderating role of intelligence measurement quality.
      ) and tract-level white matter microstructure (
      • Haász J.
      • Westlye E.T.
      • Fjaer S.
      • Espeseth T.
      • Lundervold A.
      • Lundervold A.J.
      General fluid-type intelligence is related to indices of white matter structure in middle-aged and old adults.
      ,
      • Kochunov P.
      • Williamson D.E.
      • Lancaster J.
      • Fox P.
      • Cornell J.
      • Blangero J.
      • Glahn D.C.
      Fractional anisotropy of water diffusion in cerebral white matter across the lifespan.
      ,
      • Penke L.
      • Maniega S.M.
      • Bastin M.E.
      • Valdés Hernández M.C.
      • Murray C.
      • Royle N.A.
      • et al.
      Brain white matter tract integrity as a neural foundation for general intelligence.
      ,
      • Cox S.R.
      • Ritchie S.J.
      • Fawns-Ritchie C.
      • Tucker-Drob E.M.
      • Deary I.J.
      Structural brain imaging correlates of general intelligence in UK Biobank.
      ) have been linked to cognitive function and age-related cognitive decline. It is not yet known which aging-related changes in brain structure underscore adult cognitive functioning. Following the best practices for predictive modeling (
      • Yarkoni T.
      • Westfall J.
      Choosing prediction over explanation in psychology: Lessons from machine learning.
      ,
      • Poldrack R.A.
      • Huckins G.
      • Varoquaux G.
      Establishment of best practices for evidence for prediction: A review.
      ), we used a cross-cohort magnetic resonance imaging (MRI) approach to identify elements of brain morphometry and interregional white matter connectivity that show sensitivity to aging and are relevant to late-life cognitive function.
      We modeled each participant’s brain as a macroscale connectome: a network of discrete gray matter regions (nodes) that are connected by bundles of myelinated white matter fibers (edges) (
      • Sporns O.
      The human connectome: A complex network.
      ). Guided by research spanning multiple brain imaging and mapping modalities (e.g., structural MRI, task-related functional MRI, resting-state MRI, lesion-based mapping), we investigated 9 well-characterized networks of interest (NOIs) within the structural connectome implicated in a variety of cognitive (
      • Fox M.D.
      • Snyder A.Z.
      • Vincent J.L.
      • Corbetta M.
      • Van Essen D.C.
      • Raichle M.E.
      The human brain is intrinsically organized into dynamic, anticorrelated functional networks.
      ,
      • Dosenbach N.U.F.
      • Fair D.A.
      • Miezin F.M.
      • Cohen A.L.
      • Wenger K.K.
      • Dosenbach R.A.T.
      • et al.
      Distinct brain networks for adaptive and stable task control in humans.
      ), affective (
      • Catani M.
      • Dell’acqua F.
      • Thiebaut de Schotten M.
      A revised limbic system model for memory, emotion and behaviour.
      ,
      • Rolls E.T.
      Limbic systems for emotion and for memory, but no single limbic system.
      ), psychomotor (
      • Alexander G.E.
      • DeLong M.R.
      • Strick P.L.
      Parallel organization of functionally segregated circuits linking basal ganglia and cortex.
      ,
      • Wang D.
      • Buckner R.L.
      • Fox M.D.
      • Holt D.J.
      • Holmes A.J.
      • Stoecklein S.
      • et al.
      Parcellating cortical functional networks in individuals.
      ), and homeostatic (
      • Menon V.
      Salience network.
      ,
      • Ham T.
      • Leff A.
      • de Boissezon X.
      • Joffe A.
      • Sharp D.J.
      Cognitive control and the salience network: An investigation of error processing and effective connectivity.
      ) processes. We hypothesized that the networks previously implicated in general cognitive function [e.g., Parieto-Frontal Integration Theory (P-FIT) (
      • Jung R.E.
      • Haier R.J.
      The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence.
      ,
      • Basten U.
      • Hilger K.
      • Fiebach C.J.
      Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence.
      )] would show more pronounced associations with cognitive aging than networks supporting more basic functions [e.g., sensorimotor (
      • Alexander G.E.
      • DeLong M.R.
      • Strick P.L.
      Parallel organization of functionally segregated circuits linking basal ganglia and cortex.
      ,
      • Wang D.
      • Buckner R.L.
      • Fox M.D.
      • Holt D.J.
      • Holmes A.J.
      • Stoecklein S.
      • et al.
      Parcellating cortical functional networks in individuals.
      )]. These subnetworks are distributed throughout the brain and partially overlap, allowing us to examine whether age- or cognitive-relevant information is more tightly concentrated within certain heterogeneous subcomponent constellations.
      Previous studies implicating the human structural connectome in age-related cognitive decline have largely documented age trends in summary indices of connectome topology (e.g., strength, global efficiency) (
      • Gong G.
      • Rosa-Neto P.
      • Carbonell F.
      • Chen Z.J.
      • He Y.
      • Evans A.C.
      Age- and gender-related differences in the cortical anatomical network.
      ,
      • Zhao T.
      • Cao M.
      • Niu H.
      • Zuo X.N.
      • Evans A.
      • He Y.
      • et al.
      Age-related changes in the topological organization of the white matter structural connectome across the human lifespan.
      ) or have used large-scale, exploratory methods to examine how a range of morphometric and diffusion tensor measures relate to age and sociodemographic variables (
      • Zhao L.
      • Matloff W.
      • Ning K.
      • Kim H.
      • Dinov I.D.
      • Toga A.W.
      Age-related differences in brain morphology and the modifiers in middle-aged and older adults.
      ,
      • Smith S.M.
      • Elliott L.T.
      • Alfaro-Almagro F.
      • McCarthy P.
      • Nichols T.E.
      • Douaud G.
      • Miller K.L.
      Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations.
      ). In more than 8000 individuals from UK Biobank (UKB), we examined age trends for individual elements within the whole-brain connectome and its NOIs before exploring how these age trends relate to general dimensions of neurostructural integrity. We used regression weights discovered in UKB to construct summary indices of volumetric structure and white matter connectivity at age 73 years in the independent Lothian Birth Cohort 1936 (LBC1936), which we used to predict concurrent measures of processing speed, visuospatial ability, and memory. We examined the robustness of these associations relative to total brain volume (TBV) and cognitive ability measured at 11 years of age.

      Methods and Materials

      Participants

      UK Biobank

      We analyzed MRI data from 8185 participants (4315 female) from UKB, a large-scale population epidemiology study of individuals across Great Britain (Supplement 1) (
      • Sudlow C.
      • Gallacher J.
      • Allen N.
      • Beral V.
      • Burton P.
      • Danesh J.
      • et al.
      UK Biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age.
      ). Participants ranged in age from 44.64 to 78.17 years (mean [SD] age = 61.9 [7.45] years). Less than 2% of the sample (n = 157 participants) met criteria for potentially confounding dementias and neurological syndromes (e.g., multiple sclerosis, stroke). Excluding these participants from the sample did not change primary outcome measures (rage correlations (before/after exclusion) > .999, mean absolute difference in r = .001 for both edges and nodes). Therefore, we retained the full sample for our analyses. Despite previous research demonstrating neuroanatomical sex differences (
      • Gong G.
      • Rosa-Neto P.
      • Carbonell F.
      • Chen Z.J.
      • He Y.
      • Evans A.C.
      Age- and gender-related differences in the cortical anatomical network.
      ,
      • Ritchie S.J.
      • Cox S.R.
      • Shen X.
      • Lombardo M.V.
      • Reus L.M.
      • Alloza C.
      • et al.
      Sex differences in the adult human brain: evidence from 5216 UK Biobank participants.
      ), we found largely similar patterns of connectome aging across men and women (redge-age correlations = .892; rnode-age correlations = .974, ps < .0005). We therefore report results of analyses of data collapsed across both sexes. UKB received ethical approval from the Research Ethics Committee (reference 11/NW/0382). All participants provided informed consent to participate.

      Lothian Birth Cohort 1936

      We analyzed data from 534 participants (246 female) from the LBC1936 (
      • Deary I.J.
      • Gow A.J.
      • Pattie A.
      • Starr J.M.
      Cohort profile: The Lothian Birth Cohorts of 1921 and 1936.
      ,
      • Taylor A.M.
      • Pattie A.
      • Deary I.J.
      Cohort profile update: The Lothian Birth Cohorts of 1921 and 1936.
      ) study who had reliable brain MRI and cognitive data at the age 73 wave (mean [SD] age = 72.8 [0.70] years), the first wave of brain MRI data collection (Supplement 1). Participants in LBC1936 completed an intelligence test at approximately age 11 years as part of the Scottish Mental Survey 1947 (
      Scottish Council for Research in Education
      The Intelligence of Scottish Children.
      ). Participants were largely healthy: only 7 scored in the mild range of dementia on the Mini-Mental State Examination, zero self-reported symptoms of dementia, and 65 met criteria for neuroradiologically identified stroke (
      • Wardlaw J.M.
      • Bastin M.E.
      • Valdés Hernández M.C.
      • Maniega S.M.
      • Royle N.A.
      • Morris Z.
      • et al.
      Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: Rationale, design and methodology of the imaging protocol.
      ).

      Brain Image Acquisition and Processing

      Magnetic Resonance Imaging

      MRI data for UKB participants were collected on the same MAGNETOM Skyra 3T MRI scanner (Siemens Healthineers AG, Erlangen, Germany) [see Miller et al. (
      • Miller K.L.
      • Alfaro-Almagro F.
      • Bangerter N.K.
      • Thomas D.L.
      • Yacoub E.
      • Xu J.
      • et al.
      Multimodal population brain imaging in the UK Biobank prospective epidemiological study.
      ) and Alfaro-Almagro et al. (
      • Alfaro-Almagro F.
      • Jenkinson M.
      • Bangerter N.K.
      • Andersson J.L.R.
      • Griffanti L.
      • Douaud G.
      • et al.
      Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank.
      ) for full details]. MRI data for LBC1936 participants were collected on the same GE Signa Horizon HDxt 1.5T clinical scanner (General Electric, Milwaukee, WI) [see Wardlaw et al. (
      • Wardlaw J.M.
      • Bastin M.E.
      • Valdés Hernández M.C.
      • Maniega S.M.
      • Royle N.A.
      • Morris Z.
      • et al.
      Brain aging, cognition in youth and old age and vascular disease in the Lothian Birth Cohort 1936: Rationale, design and methodology of the imaging protocol.
      ) for full details]. Further details regarding the acquisition and processing of MRI data are provided in Supplement 1.

      Tractography

      Probabilistic tractography pipelines were largely identical across UKB and LBC1936. Details about diffusion tensor MRI acquisition and processing for both samples are provided in Supplement 1.

      Connectome Construction

      Treatment of the structural brain data for both samples was based on an automated connectivity mapping pipeline (
      • Buchanan C.R.
      • Pernet C.R.
      • Gorgolewski K.J.
      • Storkey A.J.
      • Bastin M.E.
      Test-retest reliability of structural brain networks from diffusion MRI.
      ,
      • Buchanan C.R.
      • Pettit L.D.
      • Storkey A.J.
      • Abrahams S.
      • Bastin M.E.
      Reduced structural connectivity within a prefrontal-motor-subcortical network in amyotrophic lateral sclerosis.
      ), wherein T1-weighted volumes are decomposed into 85 distinct cortical and subcortical regions (nodes) based on the Desikan-Killiany atlas (
      • Desikan R.S.
      • Ségonne F.
      • Fischl B.
      • Quinn B.T.
      • Dickerson B.C.
      • Blacker D.
      • et al.
      An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
      ). Mean fractional anisotropy was averaged along the length of all streamlines identified between each pair of nodes (edges; k = 3570 possible edges). Fractional anisotropy is a diffusion tensor MRI–derived measure of white matter organization that describes the directional coherence of water molecule diffusion. Three edges were estimated as zero across all participants (i.e., probabilistic tractography found no route between the nodes involved). Whole-brain structural connectomes, composed of the 85 gray matter nodes and the 3567 nonzero edges, were created for each participant in UKB and LBC1936. Analyses were run using unthresholded matrices, which were determined to be largely similar to consistency-based thresholded matrices (Supplement 1 text and Figure S1 in Supplement 1) (
      • Buchanan C.R.
      • Bastin M.E.
      • Ritchie S.J.
      • Liewald D.C.
      • Madole J.W.
      • Tucker-Drob E.M.
      • et al.
      The effect of network thresholding and weighting on structural brain networks in the UK Biobank.
      ).

      Networks of Interest

      Masks were created to partition whole-brain connectomes into 9 prespecified NOIs (Figure 1; Table 1; Tables S1 and S2 in Supplement 1). Several NOIs were composed of partially overlapping edges and nodes, collectively referred to here as elements (Table S3 in Supplement 1). Where applicable, details for how overlapping elements were handled are provided in Results.
      Figure thumbnail gr1
      Figure 1Anatomical maps of each prespecified brain network of interest displaying the network-specific connectome elements (i.e., edges and nodes). P-FIT, Parieto-Frontal Integration Theory.
      Table 1Properties of Each Brain NOI With a Canonical Reference Describing the Network’s Makeup, Previous Associations, and Elements
      NetworkNodesEdgesHypothesisSelect RegionsImplicated in
      All Networks (Global)853570+
      P-FIT (
      • Jung R.E.
      • Haier R.J.
      The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence.
      ,
      • Basten U.
      • Hilger K.
      • Fiebach C.J.
      Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence.
      )
      36630+DLPFC, inferior and superior parietal lobule, ACC, specific regions within temporal and occipital lobesGeneral intelligence
      Central Executive (
      • Sridharan D.
      • Levitin D.J.
      • Menon V.
      A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks.
      ,
      • Menon V.
      • Uddin L.Q.
      Saliency, switching, attention and control: A network model of insula function.
      ,
      • Fair D.A.
      • Dosenbach N.U.F.
      • Church J.A.
      • Cohen A.L.
      • Brahmbhatt S.
      • Miezin F.M.
      • et al.
      Development of distinct control networks through segregation and integration.
      )
      828+Right DLPFC, posterior parietal cortexActivation associated with selecting, switching, and attending to salient events
      Multiple Demand (
      • Duncan J.
      The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour.
      ,
      • Fedorenko E.
      • Duncan J.
      • Kanwisher N.
      Broad domain generality in focal regions of frontal and parietal cortex.
      )
      1266+Middle frontal, inferior parietal, pre-SMA, ACC, rostral prefrontal, insula/frontal operculumGeneral purpose activation in cognitively demanding tasks, suggesting a role in cognitive flexibility, executive control, and abstract problem solving
      Cingulo-opercular (
      • Fair D.A.
      • Dosenbach N.U.F.
      • Church J.A.
      • Cohen A.L.
      • Brahmbhatt S.
      • Miezin F.M.
      • et al.
      Development of distinct control networks through segregation and integration.
      )
      1045+Dorsal ACC, superior frontal cortex, anterior frontal cortex, insula, thalamusStable set control, maintenance of task-relevant sustained attention
      Default Mode (
      • Catani M.
      • Dell’acqua F.
      • Thiebaut de Schotten M.
      A revised limbic system model for memory, emotion and behaviour.
      ,
      • Bressler S.L.
      • Menon V.
      Large-scale brain networks in cognition: Emerging methods and principles.
      )
      16120Ventromedial frontal cortex, medial temporal cortex, posterior cingulate cortex, angular gyrus, cingulum bundleExtensive deactivation in functional MRI during cognitively demanding tasks
      Hippocampal-diencephalic (
      • Aggleton J.P.
      EPS Mid-Career Award 2006: Understanding anterograde amnesia: Disconnections and hidden lesions.
      ,
      • Vann S.D.
      • Aggleton J.P.
      • Maguire E.A.
      What does the retrosplenial cortex do?.
      )
      1266+Hippocampus, diencephalon, ventral cingulum, fornixMemory and spatial orientation
      Salience (
      • Menon V.
      Salience network.
      ,
      • Ham T.
      • Leff A.
      • de Boissezon X.
      • Joffe A.
      • Sharp D.J.
      Cognitive control and the salience network: An investigation of error processing and effective connectivity.
      ,
      • Menon V.
      • Uddin L.Q.
      Saliency, switching, attention and control: A network model of insula function.
      )
      1045Insula, ACC, amygdala, substantia nigra/VTA, thalamusOrientation of attention to the most homeostatically relevant events from moment to moment
      Sensorimotor (
      • Alexander G.E.
      • DeLong M.R.
      • Strick P.L.
      Parallel organization of functionally segregated circuits linking basal ganglia and cortex.
      ,
      • Wang D.
      • Buckner R.L.
      • Fox M.D.
      • Holt D.J.
      • Holmes A.J.
      • Stoecklein S.
      • et al.
      Parcellating cortical functional networks in individuals.
      )
      1266Precentral, postcentral, pre- and post-SMA, caudal cingulate, caudal middle frontal gyrus, thalamus, putamenInitiation and control of movements
      Temporo-amygdala-orbital (
      • Catani M.
      • Dell’acqua F.
      • Thiebaut de Schotten M.
      A revised limbic system model for memory, emotion and behaviour.
      ,
      • Rolls E.T.
      Limbic systems for emotion and for memory, but no single limbic system.
      )
      32496Anterior temporal cortex, amygdala, orbitofrontal cortex, ACC, parts of cingulum bundleVisceral emotion and sensation
      For each network, the number of edges is N ∗ [(N − 1)/2] the number of nodes. + refers to subnetwork for which we hypothesized a positive association between subnetwork integrity and cognitive function. − refers to a negative control network, i.e., a network for which we do not hypothesize a positive association between subnetwork integrity and cognitive function. See Figure 1 for illustration of network properties. See Table S2 in Supplement 1 for comparison with other widely used brain subnetworks (
      • Yeo B.T.
      • Krienen F.M.
      • Sepulcre J.
      • Sabuncu M.R.
      • Lashkari D.
      • Hollinshead M.
      • et al.
      The organization of the human cerebral cortex estimated by intrinsic functional connectivity.
      ).
      ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; MRI, magnetic resonance imaging; NOI, network of interest; P-FIT, Parieto-Frontal Integration Theory; SMA, supplementary motor area; VTA, ventral tegmental area.

      Cognitive Testing in LBC1936

      We analyzed data from tests of processing speed, visuospatial ability, and memory, which we have characterized within this cohort in previous research (
      • Ritchie S.J.
      • Tucker-Drob E.M.
      • Cox S.R.
      • Corley J.
      • Dykiert D.
      • Redmond P.
      • et al.
      Predictors of ageing-related decline across multiple cognitive functions.
      ). Visuospatial ability was measured using tests of matrix reasoning (
      • Wechsler D.
      Wechsler Adult Intelligence Scale III-UK Administration and Scoring Manual.
      ), block design (
      • Wechsler D.
      Wechsler Adult Intelligence Scale III-UK Administration and Scoring Manual.
      ), and spatial span (forward and backward) (
      • Wechsler D.
      Wechsler Memory Scale III-UK Administration and Scoring Manual.
      ). Processing speed was measured using the digit symbol substitution (
      • Wechsler D.
      Wechsler Adult Intelligence Scale III-UK Administration and Scoring Manual.
      ), symbol search (
      • Wechsler D.
      Wechsler Adult Intelligence Scale III-UK Administration and Scoring Manual.
      ), 4-choice reaction time (
      • Deary I.
      Reaction times and intelligence differences: A population-based cohort study.
      ), and inspection time (
      • Deary I.J.
      • Simonotto E.
      • Meyer M.
      • Marshall A.
      • Marshall I.
      • Goddard N.
      • Wardlaw J.M.
      The functional anatomy of inspection time: An event-related fMRI study.
      ). Memory was measured using the digit span backward (
      • Wechsler D.
      Wechsler Adult Intelligence Scale III-UK Administration and Scoring Manual.
      ), logical memory (
      • Wechsler D.
      Wechsler Memory Scale III-UK Administration and Scoring Manual.
      ), and verbal paired associates (
      • Wechsler D.
      Wechsler Memory Scale III-UK Administration and Scoring Manual.
      ). All cognitive domains were modeled as latent variables. Fit indices, factor model parameter estimates, and descriptive statistics for the cognitive tests are reported in Table S4 in Supplement 1.

      Results

      Connectome Aging

      Cross-sectional age trends in each connectome element were estimated in the UKB sample. Density distributions of the element-wise age associations for the whole-brain connectome and each NOI are presented in Figure 2A. The majority of elements showed small to modest negative associations with age (edges: 2375/3570 [66.5%] < 0, mean r = −.037, range = −.437 to .268; nodes: 81/85 [95.3%] < 0, mean r = −.160, range = −.322 to .087). Nodes from the P-FIT network displayed a bimodal distribution of age associations, potentially indicating multiple aging-related processes within this network (Hartigans’ dip test D = 0.088, p < .001) (Table S5 in Supplement 1). This multimodality may be driven by network-specific divisions: elements from the central executive network displayed the steepest age-related gradients (mean rage-edge = −.163; mean rage-node = −.211) (Table S6 in Supplement 1), suggesting that it demarcates a particularly age-sensitive constellation of elements within the larger P-FIT network. Only the salience network contained a majority of edges with positive age associations (36/45 [80%] r’s > 0). In contrast, all 10 of its nodes displayed negative age associations.
      Figure thumbnail gr2
      Figure 2(A) Density distributions of association of each element with age, categorized by prespecified network of interest. All networks of interest are subsets of the whole-brain (global) network, such that comparison with the red distribution at the top of both panels is not a comparison of independent elements, but rather a comparison of a subset to a whole. (B) Density distributions of loadings on the first principal component (PC) of the whole-brain connectome, categorized by prespecified network of interest. PC analyses were conducted separately for each network of interest. P-FIT, Parieto-Frontal Integration Theory.

      General Dimensions of Connectome Integrity

      The widespread age-related decrements across NOIs suggest that individual elements may represent broader dimensions of interindividual variation in global connectome integrity. We examined this possibility by residualizing edges and nodes for age and subjecting their respective correlation matrices to principal component (PC) analysis (Supplement 1 text, Figures S2 and S3 in Supplement 1, and Tables S7 and S8 in Supplement 1). The first PC accounted for 11.0% and 36.9% of variation in edges and nodes, respectively. The second PC accounted for less than one fifth the variance accounted for by the first corresponding eigenvalue (Figure S4 in Supplement 1). Whole-brain loadings were overwhelmingly positive (edges: 98.4% of loadings > 0; nodes: 100% of loadings > 0) (Figure 2B). Elements within the central executive network displayed the largest average loadings, potentially driving the bimodal distribution of edges from the P-FIT network (Hartigans’ dip test D = 0.028, p = .001) (Table S5 in Supplement 1). This again suggests that this small subset of the P-FIT network may disproportionately index overall brain integrity.

      Connectome Aging Occurs Along General Dimensions of Edge and Node Integrity

      We tested the extent to which aging-related differences in individual connectome elements occurred along the general dimensions of edge and node integrity identified above. In UKB, we estimated the correlation between each element's loading on the first PC (both whole-brain and network-specific) and each element's association with age separately for edges and nodes. Residualizing connectome elements for age before conducting PC analyses ensured that the tested association between age sensitivity and PC loadings was not an artifact of similar age trends driving element covariation (
      • Lindenberger U.
      • von Oertzen T.
      • Ghisletta P.
      • Hertzog C.
      Cross-sectional age variance extraction: What’s change got to do with it?.
      ,
      • Hofer S.M.
      • Sliwinski M.J.
      Understanding ageing.
      ). Figure 3 displays the whole-brain association between PC loadings and age correlations for edges (left panel) and nodes (right panel). Both edges and nodes that had stronger loadings evinced steeper age gradients (redges = −.583; rnodes = −.420): the more indicative an element was of global variation in brain connectivity or brain volume, the stronger its negative association with age. Similar patterns were obtained when analyses were conducted separately for each individual NOI (Supplement 1 text and Figures S5 and S6 in Supplement 1).
      Figure thumbnail gr3
      Figure 3Scatterplots of correlation of each connectome element with age against its loading on a single principal component (PC) [based on an age-partialled correlation matrix ( in )]. Analyses were conducted separately for edges (left panel) and nodes (right panel). Each point represents a single element of the connectome (3567 nonzero edges; 85 nodes). Points are categorized by the network of interest to which the element belongs. Elements belonging to multiple networks of interest are plotted once for each group membership and jittered for the sake of visual interpretation. Reported correlations and displayed regression lines reflect analyses including each element only once. P-FIT, Parieto-Frontal Integration Theory.
      We tested whether the observed associations between PC loadings and age correlations were explained by the topological centrality (i.e., strength) of elements within the whole-brain connectome, a potential indication of metabolic cost that could confer susceptibility to degeneration with age (Supplement 1) (
      • Perry A.
      • Wen W.
      • Lord A.
      • Thalamuthu A.
      • Roberts G.
      • Mitchell P.B.
      • et al.
      The organisation of the elderly connectome.
      ). We found that topological centrality was strongly correlated with PC loadings (redges = .655; rnodes = .583; both p <. 0005) (Figure S7 in Supplement 1), but only modestly associated with age correlations (redges = −.202, p < .0005; rnodes = −.211, p = .053) (Figure S8 in Supplement 1). Similarly, network membership (i.e., the number of NOIs that an element belongs to) was weakly, if at all, related to the age correlations (Figure S9 in Supplement 1). Topological connectedness of connectome elements was therefore insufficient to explain associations between PC loadings and age correlations.

      General Dimensions of Connectome Integrity Are Associated With Late-Life Cognitive Function

      That connectome aging occurs along general dimensions of variation in edge and node integrity suggests that these dimensions may be particularly relevant for cognitive decline. To test this hypothesis, we created linear composite indices of connectome elements in LBC1936 (Figure S2D in Supplement 1), weighted by either UKB-estimated PC loadings or age correlations, to test associations with latent processing speed, visuospatial ability, and memory factors. As would be expected from the sizable associations between age correlations and PC loadings, age-weighted and PC-weighted composites created for the whole brain were highly correlated (redge-based composites = −.907; rnode-based composites = −.998) and exhibited nearly identical patterns of associations with cognitive outcomes. This indicates that brain age and overall integrity are virtually indistinguishable.

      Edge-Based Composites

      Composite indices of connectome-wide edge integrity were significantly associated with processing speed (rage-weighted = −.193; 95% confidence interval [CI] = [−.285, −.101]; rPC-weighted = .177; 95% CI = [.084, .269]), but not with visuospatial ability (rage-weighted = −.089; 95% CI = [−.186, .008]; rPC-weighted = .064; 95% CI = [−.033, .162]) or memory (rage-weighted = −.083; 95% CI = [−.186, .020]; rPC-weighted = .055; 95% CI = [−.047, .157]). For both age weights and PC weights, a 1000-fold permutation test (Supplement 1 text, Figure S10 in Supplement 1, and Table S9 in Supplement 1) in which the weights were randomly shuffled across edges indicated that observed edge-based composites were more predictive of both processing speed and visuospatial ability than over 99% of the permuted data (empirical ps < .01) and more predictive of memory than over 95% of the permuted data (empirical ps < .05).
      NOI-based composite indices varied in their magnitudes of prediction of processing speed (rage-weighted range = −.193 to −.037; rPC-weighted range = −.095 to .186) but displayed null associations with visuospatial ability (rage-weighted range = −.130 to .009; rPC-weighted range = −.064 to .100) and memory (rage-weighted range = −.099 to −.003; rPC-weighted range = −.030 to .100) (top left panel of Figure 4; top left panel of Figure S11 in Supplement 1). To examine whether differences in the magnitudes of association across NOIs stem from differences in their sizes (i.e., larger networks aggregating more information), we divided each correlation by the total number of elements on which the composite index was based (processing speed: rage-weighted-adjusted range = −.0064 to −.00005; rPC-weighted-adjusted range = −.0021 to .0066) (top right panel of Figure 4; top right panel of Figure S11 in Supplement 1). Edge-based composite indices of central executive network integrity showed the largest size-adjusted magnitudes of association with processing speed. As edges were generally unrelated to visuospatial ability and memory, we did not interpret their size-adjusted associations.
      Figure thumbnail gr4
      Figure 4Prediction of cognitive function in the Lothian Birth Cohort 1936 from UK Biobank–weighted indices of connectome integrity. Raw and adjusted associations between weighted-composite scores reflecting variation in overall connectome integrity and cognitive function in Lothian Birth Cohort 1936. Adjusted estimates were created by dividing the raw estimates by the number of edges or nodes in the network. Note that raw associations for edges and nodes are presented on the same y-axis scale, whereas the scale for the adjusted associations differs for edges and nodes. Scores were created across the whole brain and all networks of interest by summing the Lothian Birth Cohort 1936 data weighted by loading of each element on the first principal component (PC) of its respective subnetwork discovered in UK Biobank. Plots are broken down by element type (i.e., edges or nodes) and reflect correlations between respective weighted composites from each network of interest and the cognitive domains of processing speed, visuospatial ability, and memory. Error bars represent 95% confidence intervals. P-FIT, Parieto-Frontal Integration Theory.

      Node-Based Composites

      Composite indices of connectome-wide node integrity were significantly associated with all cognitive domains (processing speed: rage-weighted = −.245; 95% CI = [−.335, −.155]; rPC-weighted = .234; 95% CI = [.145, .325]; visuospatial ability: rage-weighted = −.386; 95% CI = [−.471, −.301]; rPC-weighted = .383; 95% CI = [.298, .468]; memory: rage-weighted = −.124; 95% CI = [−.223, −.025]; rPC-weighted = .118; 95% CI = [.019, .217]). For both age weights and PC weights, a 1000-fold permutation test (Supplement 1 text, Figure S10 in Supplement 1, and Table S9 in Supplement 1) indicated that observed node-based composites were not substantially more predictive of any domain than the permuted data (empirical ps > .09). This is consistent with the high intercorrelations among the nodes and the observation that the distributions of associations for nearly all permuted node runs were very narrow, indicating that nodes may be largely exchangeable with respect to information relevant to cognitive ability.
      NOI-based composite indices varied in their magnitudes of prediction, with prediction of visuospatial ability generally exceeding that of processing speed or memory (processing speed: rage-weighted range = −.288 to −.128; rPC-weighted range = .120 to .282; visuospatial ability: rage-weighted range = −.377 to −.277; rPC-weighted range = .292 to .373; memory: rage-weighted range = −.151 to −.065; rPC-weighted range = .048 to .147) (bottom left panel of Figure 4; bottom left panel of Figure S11 in Supplement 1). After adjusting for the number of elements, nodes in the central executive network displayed the largest associations with all domains of cognitive function (processing speed: rage-weighted-adjusted = −.026, 95% CI = [−.038, −.015]; rPC-weighted-adjusted = .026, 95% CI = [.014, .037]; visuospatial ability: rage-weighted-adjusted = −.044, 95% CI = [−.055, −.033]; rPC-weighted-adjusted = .044, 95% CI = [.033, .055]; memory: rage-weighted-adjusted = −.013, 95% CI = [−.025, −.0003]; rPC-weighted-adjusted = .013, 95% CI = [.000, .025]) (bottom right panel of Figure 4; bottom right panel of Figure S11 in Supplement 1).

      General Dimensions of Edge and Node Integrity Are Incrementally Predictive of Late-Life Cognitive Function

      Total Brain Volume

      We fitted multiple regression models in LBC1936 to test whether the associations between general dimensions of connectome integrity and cognitive function were unique of TBV, which is perhaps the most robust and well-validated structural MRI predictor of cognitive function (
      • Gignac G.E.
      • Bates T.C.
      Brain volume and intelligence: The moderating role of intelligence measurement quality.
      ,
      • Cox S.R.
      • Ritchie S.J.
      • Fawns-Ritchie C.
      • Tucker-Drob E.M.
      • Deary I.J.
      Structural brain imaging correlates of general intelligence in UK Biobank.
      ). Results are presented as models 1 and 4 in each panel of Table 2. TBV displayed strong associations with node-based composite scores (rage-weighted = −.869; rPC-weighted = .877; ps < .0005), but weak associations with edge-based composites (rage-weighted = −.0004; rPC-weighted = .014; ps > .750). TBV was significantly associated with both processing speed (β = .165, p = .001) and visuospatial ability (β = .333, p < .0005), but not with memory (β = .012, p = .815). Edge- and node-based composites of connectome integrity predicted processing speed incremental of TBV (edges: βage-weighted = −.194; βPC-weighted = .176; nodes: βage-weighted = −.408; βPC-weighted = .382; ps < .0005). Node-based composites of connectome integrity predicted visuospatial ability (βage-weighted = −.401; βPC-weighted = .399; ps < .0005) and memory (βage-weighted = −.446; βPC-weighted = .442; ps < .0005) incremental of TBV.
      Table 2Associations Between Weighted Connectome (Edge and Node) Composites, Total Brain Volume, and Age 11 IQ
      CompositeModelPredictor 1Predictor 2β1 (p Value)β2 (p Value)R2Multiple R
      Processing Speed
      Age-Based1aTBV.165 (.001).027.165
      1bEdgesTBV−.194 (< .0005).165 (< .0005).065.255
      1cNodesTBV−.408 (< .0005)−.188 (.049).069.263
      2aEdges only−.193 (< .0005).037.193
      2bNodes only−.245 (< .0005).060.245
      2cEdgesNodes−.167 (< .0005)−.226 (< .0005).088.297
      3aAge 11 IQ.511 (< .0005).261.511
      3bEdgesAge 11 IQ−.149 (.001).498 (< .0005).282.531
      3cNodesAge 11 IQ−.162 (< .0005).484 (< .0005).285.535
      PC-Based4aTBV.165 (.001).027.165
      4bEdgesTBV.176 (< .0005).163 (.001).058.241
      4cNodesTBV.382 (< .0005)−.168 (.089).062.249
      5aEdges only.177 (< .0005).031.177
      5bNodes only.235 (< .0005).055.235
      5cEdgesNodes.154 (.001).219 (< .0005).079.281
      6aAge 11 IQ.511 (< .0005).261.511
      6bEdgesAge 11 IQ.133 (.002).500 (< .0005).277.526
      6cNodesAge 11 IQ.154 (< .0005).486 (< .0005).283.532
      Visuospatial Ability
      Age-Based1aTBV.333 (< .0005).111.333
      1bEdgesTBV−.087 (.068).331 (< .0005).117.342
      1cNodesTBV−.401 (< .0005)−.017 (.860).149.386
      2aEdges only−.089 (.072).008.089
      2bNodes only−.386 (< .0005).149.386
      2cEdgesNodes−.043 (.363)−.380 (< .0005).150.387
      3aAge 11 IQ.553 (< .0005).306.553
      3bEdgesAge 11 IQ−.039 (.387).549 (< .0005).307.554
      3cNodesAge 11 IQ−.308 (< .0005).504 (< .0005).397.630
      PC-Based4aTBV.333 (< .0005).111.333
      4bEdgesTBV.058 (.224).331 (< .0005).114.338
      4cNodesTBV.399 (< .0005)−.019 (.848).147.383
      5aEdges only.064 (.197).004.064
      5bNodes only.383 (< .0005).147.383
      5cEdgesNodes.023 (.633).380 (< .0005).147.383
      6aAge 11 IQ.553 (< .0005).306.553
      6bEdgesAge 11 IQ.017 (.702).552 (< .0005).306.553
      6cNodesAge 11 IQ.306 (< .0005).505 (< .0005).397.630
      Memory
      Age-Based1aTBV.012 (.815).0001.012
      1bEdgesTBV−.082 (.117).009 (.861).007.084
      1cNodesTBV−.446 (< .0005)−.370 (< .0005).049.221
      2aEdges only−.083 (.116).007.083
      2bNodes only−.124 (.014).015.124
      2cEdgesNodes−.067 (.204)−.166 (.023).020.141
      3aAge 11 IQ.613 (< .0005).376.613
      3bEdgesAge 11 IQ−.027 (.557).615 (< .0005).381.617
      3cNodesAge 11 IQ−.033 (.468).611 (< .0005).381.617
      PC-Based4aTBV.012 (.815).0001.012
      4bEdgesTBV.052 (.329).009 (.863).003.055
      4cNodesTBV.442 (< .0005)−.369 (< .0005).045.214
      5aEdges only.053 (.324).003.053
      5bNodes only.118 (.019).014.118
      5cEdgesNodes.037 (.483).114 (.026).015.122
      6aAge 11 IQ.613 (< .0005).376.613
      6bEdgesAge 11 IQ−.003 (.954).616 (< .0005).379.616
      6cNodesAge 11 IQ.029 (.524).612 (< .0005).380.616
      PC, principal component; TBV, total brain volume.

      Element Type

      We fitted multiple regression models to test whether the associations between edge- and node-based indices of connectome integrity and cognitive function were distinct from one another. Results are presented as models 2 and 5 in each panel of Table 2. All associations that were present in the univariate context were preserved. For processing speed, the multiple R2 values from the models that included both edge- and node-based indices were more than 40% larger than the R2 values from models including only node-based indices and more than 100% larger than the R2 values from models including only edge-based indices. For visuospatial ability and memory, multiple R2 values from the models that included both edge- and node-based indices were only marginally larger than the R2 values from models including node-based indices alone.

      Childhood Intelligence

      LBC1936 has available a high-quality index of IQ at age 11 years, the Moray House Test No. 12. Age 11 IQ was associated with node-based indices of age 73 connectome integrity (βage-weighted = −.158; βPC-weighted = .155; ps < .0005) but was not significantly associated with age 73 edge-based indices (βage-weighted = −.079; βPC-weighted = .076; ps > .076). These results are consistent with previous findings in LBC1936 of comparable associations between age 11 IQ and other age 73 structural MRI indices (brain cortical thickness) (
      • Karama S.
      • Bastin M.E.
      • Murray C.
      • Royle N.A.
      • Penke L.
      • Muñoz Maniega S.
      • et al.
      Childhood cognitive ability accounts for associations between cognitive ability and brain cortical thickness in old age.
      ), collectively suggesting that general dimensions of node integrity may at least partially reflect lifelong brain health.
      To probe whether associations between age 73 connectome integrity and age 73 cognitive function were plausibly reflective of aging-specific processes, we examined whether the observed associations persisted after controlling for age 11 IQ. Results are presented as models 3 and 6 in each panel of Table 2. Age 73 connectome-integrity indices maintained their associations with age 73 processing speed and visuospatial ability even after controlling for age 11 IQ. The modest node-based associations with memory did not persist after controlling for age 11 IQ.

      Regularized Least Absolute Shrinkage and Selection Operator Regression Models

      We were interested in whether a least absolute shrinkage and selection operator approach for indexing connectome age could improve prediction of late-life cognitive function beyond the simple composite indices reported above (Supplement 1). Consistent with previous research that has found differential prediction of age based on brain tissue type (
      • Richard G.
      • Kolskår K.
      • Sanders A.-M.
      • Kaufmann T.
      • Petersen A.
      • Doan N.T.
      • et al.
      Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry.
      ), a least absolute shrinkage and selection operator model in UKB based on all edges predicted 54.6% of the variance in age in the UKB holdout sample, whereas a model based on all nodes predicted only 35.8% of the variation in age (Supplement 1 text and Figures S12 and S13 in Supplement 1). Least absolute shrinkage and selection operator–based prediction of cognitive function in LBC1936 from UKB-trained connectome age did not appreciably improve effect sizes relative to estimates obtained using the simple composite indices reported earlier (Figures S14 and S15 in Supplement 1), suggesting that the sparsity introduced by complex algorithmic learning methods is not advantageous for predicting late-life cognitive abilities from connectome aging.

      Discussion

      Examining variation in elements within the whole-brain structural connectome and several of its NOIs in relation to late-life chronological age and cognitive function may prove fundamental to detecting and mitigating age-related cognitive impairments. Using age-heterogeneous data from UKB, we found that connectome age occurs along the same dimensions of global brain health that underlie correlations among (age-partialled) connectome element integrities. We used indices of these general dimensions of element integrity in LBC1936 to obtain between-sample cross-validated predictions of aging-sensitive domains of cognitive function in older adulthood (
      • Tucker-Drob E.M.
      Cognitive aging and dementia: A life-span perspective.
      ,
      • Salthouse T.A.
      The processing-speed theory of adult age differences in cognition.
      ,
      • Tucker-Drob E.M.
      Global and domain-specific changes in cognition throughout adulthood.
      ). Connectome-wide node integrity was related to all domains of cognitive function, whereas connectome-wide edge integrity was specifically related to processing speed. Associations with processing speed and visuospatial ability persisted after controlling for both TBV and age 11 IQ, suggesting that they capture aging-specific processes. Associations with memory did not survive after controlling for age 11 IQ, suggesting that they may be vestiges of early-life differences in cognitive function. NOI-specific analyses indicated a disproportionally large role of the central executive network in these patterns relative to its small size. Edges in the central executive network were particularly predictive of processing speed after adjustment, suggesting that the efficacy of water diffusion along white matter pathways between regions such as the dorsolateral prefrontal cortex and the posterior parietal cortex may constrain an individual’s ability to efficiently process and act on information.
      That connectome elements with stronger loadings on their corresponding PCs had larger negative correlations with age reveals an important connection between individual differences in global neurostructural integrity and aging-related neurodegeneration. This result parallels findings from cognitive aging research showing that tests with stronger loadings on a general factor of cognitive ability tend to be more closely correlated with age (
      • Salthouse T.A.
      Structural models of the relations between age and measures of cognitive functioning.
      ,
      • Salthouse T.A.
      From description to explanation in cognitive aging.
      ), suggesting a strong shared basis for cognitive aging across different abilities (
      • Tucker-Drob E.M.
      • Brandmaier A.M.
      • Lindenberger U.
      Coupled cognitive changes in adulthood: A meta-analysis.
      ). The current results extend this phenomenon to the brain and highlight that research on individual differences in aging-related cognitive and neurostructural decline would benefit from focusing on broad mechanisms of aging, in addition to more granular processes. This finding also raises considerable interpretation challenges to work on apparent brain age (
      • Cole J.H.
      • Marioni R.E.
      • Harris S.E.
      • Deary I.J.
      Brain age and other bodily ‘ages’: Implications for neuropsychiatry.
      ,
      • Kaufmann T.
      • van der Meer D.
      • Doan N.T.
      • Schwarz E.
      • Lund M.J.
      • Agartz I.
      • et al.
      Common brain disorders are associated with heritable patterns of apparent aging of the brain.
      ), suggesting that brain age may index overall connectome health rather than an aging-specific process. Our findings demonstrate that late-life connectome health is partly accounted for by childhood differences in cognitive ability but that associations between age 73 connectome health and age 73 processing speed and visuospatial ability are also likely to be partly reflective of the aging process proper. Incorporating high-quality controls for prior intelligence or brain structure may facilitate interpreting associations between brain age and external outcomes (
      • Elliott M.L.
      • Belsky D.W.
      • Knodt A.R.
      • Ireland D.
      • Melzer T.R.
      • Poulton R.
      • et al.
      Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort [published online ahead of print Dec 10].
      ). Not only was connectome age strongly related to connectome integrity, but age-weighted connectome composite scores were nearly entirely collinear with PC-weighted composites (redge-based composites = −.892; rnode-based composites = −.999). Thus, any given association with apparent brain age might just as appropriately be conceptualized as an association with overall brain integrity.
      Although this study examined a well-characterized set of high-quality structural brain networks in independent, large-scale samples, it nevertheless had some key limitations. First, though the samples were nonoverlapping, both were based in the United Kingdom, were self-selected, were of the same broad ethnic and cultural background, and were healthier, better-educated, and more cognitively able than average (
      • Deary I.J.
      • Gow A.J.
      • Pattie A.
      • Starr J.M.
      Cohort profile: The Lothian Birth Cohorts of 1921 and 1936.
      ,
      • Taylor A.M.
      • Pattie A.
      • Deary I.J.
      Cohort profile update: The Lothian Birth Cohorts of 1921 and 1936.
      ,
      • Fry A.
      • Littlejohns T.J.
      • Sudlow C.
      • Doherty N.
      • Adamska L.
      • Sprosen T.
      • et al.
      Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population.
      ). To encourage investigations into the external validity of our findings, UKB-derived age and PC weights for connectome elements are available in Table S10 in Supplement 2. Second, the study focused on neurostructural prediction of cross-sectional differences in cognitive level. Future work might benefit from investigating whether these same predictors are relevant for late-life cognitive change. Research integrating longitudinal measurement of aging-related brain changes with previously identified determinants of cognitive decline (
      • Corley J.
      • Cox S.R.
      • Deary I.J.
      Healthy cognitive ageing in the Lothian Birth Cohort studies: Marginal gains not magic bullet.
      ), including medical comorbidities, lifestyle indicators, and genetic risk, may critically advance prediction of cognitive aging. Third, though we used unthresholded connectivity matrices, it is possible that edges that occur in few subjects and involve few streamlines contain greater measurement error (
      • Roberts J.A.
      • Perry A.
      • Roberts G.
      • Mitchell P.B.
      • Breakspear M.
      Consistency-based thresholding of the human connectome.
      ,
      • Betzel R.F.
      • Griffa A.
      • Hagmann P.
      • Mišić B.
      Distance-dependent consensus thresholds for generating group-representative structural brain networks.
      ). Fourth, the LBC1936 and UKB MRI scanners differed in acquisition strength (1.5T and 3T, respectively). It is potentially nontrivial to compare brain indices across scanners of different magnetic strengths (
      • Jovicich J.
      • Czanner S.
      • Han X.
      • Salat D.
      • van der Kouwe A.
      • Quinn B.
      • et al.
      MRI-derived measurements of human subcortical, ventricular and intracranial brain volumes: Reliability effects of scan sessions, acquisition sequences, data analyses, scanner upgrade, scanner vendors and field strengths.
      ,
      • Chen J.
      • Liu J.
      • Calhoun V.D.
      • Arias-Vasquez A.
      • Zwiers M.P.
      • Gupta C.N.
      • et al.
      Exploration of scanning effects in multi-site structural MRI studies.
      ), and future research would benefit from assessing whether these differences bias cross-sample prediction. Fifth, we used multiple IQ-type tests to model latent variables of 3 core domains of cognitive function, but it remains unclear how results might generalize to other cognitive domains, such as nonverbal memory (
      • Drag L.L.
      • Bieliauskas L.A.
      Contemporary review 2009: Cognitive aging.
      ). Studies using different tests may find somewhat different patterns of relationships between specific brain networks and cognitive abilities. Sixth, previous studies have focused on connectivity between several of the networks studied here (
      • Chand G.B.
      • Wu J.
      • Hajjar I.
      • Qiu D.
      Interactions of the salience network and its subsystems with the default-mode and the central-executive networks in normal aging and mild cognitive impairment.
      ). By primarily investigating networks separately, we may have missed the potential role of between-network connections and cognitive aging. Finally, previous research has examined how aging-related disruption of functional connectivity within specific neural subnetworks relates to cognitive performance in older adults (
      • Geerligs L.
      • Maurits N.M.
      • Renken R.J.
      • Lorist M.M.
      Reduced specificity of functional connectivity in the aging brain during task performance: Functional connectivity in the aging brain.
      ,
      • Geerligs L.
      • Renken R.J.
      • Saliasi E.
      • Maurits N.M.
      • Lorist M.M.
      A brain-wide study of age-related changes in functional connectivity.
      ). Though we focus solely on structural connectivity, integrating the structural and functional perspectives is a critical future task for network-focused cognitive neuroscience.
      This study represents a comprehensive investigation of aging within the human structural connectome in relation to late-life cognitive function. We found evidence that aging in the brain as a whole and within specific networks is related to broad dimensions of variation in neurostructural integrity and is substantially predictive of out-of-sample cognitive abilities. Given the wealth of publicly available neuroimaging data, the cross-cohort comparison approach will be fruitful in producing predictively valid estimates of neurostructural associations with cognitive abilities and thus of potential use in detecting and understanding differences in cognitive decline.

      Acknowledgments and Disclosures

      This work was supported by the National Institutes of Health (Grant No. R01AG054628 [to EMT-D] ). The Population Research Center at the University of Texas is supported by the National Institutes of Health (Grant No. P2CHD042849 ). The Lothian Birth Cohorts group is supported by Age UK (The Disconnected Mind grant), the Medical Research Council (Grant No. MR/R024065/1 ), and the University of Edinburgh School of Philosophy , Psychology and Language Sciences . This research was conducted using the UK Biobank Resource (Application No. 10279).
      SJR, EMT-D, and JWM conceived of the study design. CRB and SRC processed the UK Biobank MRI data. SRC, MAH, and MVH processed the Lothian Birth Cohort 1936 MRI data. JWM and SJR conducted the analyses under supervision from EMT-D. JWM, SJR, and EMT-D wrote the manuscript. All authors contributed edits and feedback on the manuscript.
      We thank the UK Biobank participants and UK Biobank team for their work in collecting, processing, and disseminating these data for analysis.
      A previous version of this article was published as a preprint on bioRxiv: https://doi.org/10.1101/2019.12.13.875559.
      IJD is a participant in UK Biobank. All other authors report no biomedical financial interests or potential conflicts of interest.

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