Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life.
Journal
Molecular psychiatry
ISSN: 1476-5578
Titre abrégé: Mol Psychiatry
Pays: England
ID NLM: 9607835
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
14
02
2020
accepted:
30
11
2020
revised:
17
11
2020
pubmed:
6
1
2021
medline:
12
10
2021
entrez:
5
1
2021
Statut:
ppublish
Résumé
Different brain regions can be grouped together, based on cross-sectional correlations among their cortical characteristics; this patterning has been used to make inferences about ageing processes. However, cross-sectional brain data conflate information on ageing with patterns that are present throughout life. We characterised brain cortical ageing across the eighth decade of life in a longitudinal ageing cohort, at ages ~73, ~76, and ~79 years, with a total of 1376 MRI scans. Volumetric changes among cortical regions of interest (ROIs) were more strongly correlated (average r = 0.805, SD = 0.252) than were cross-sectional volumes of the same ROIs (average r = 0.350, SD = 0.178). We identified a broad, cortex-wide, dimension of atrophy that explained 66% of the variance in longitudinal changes across the cortex. Our modelling also discovered more specific fronto-temporal and occipito-parietal dimensions that were orthogonal to the general factor and together explained an additional 20% of the variance. The general factor was associated with declines in general cognitive ability (r = 0.431, p < 0.001) and in the domains of visuospatial ability (r = 0.415, p = 0.002), processing speed (r = 0.383, p < 0.001) and memory (r = 0.372, p < 0.001). Individual differences in brain cortical atrophy with ageing are manifest across three broad dimensions of the cerebral cortex, the most general of which is linked with cognitive declines across domains. Longitudinal approaches are invaluable for distinguishing lifelong patterns of brain-behaviour associations from patterns that are specific to aging.
Identifiants
pubmed: 33398085
doi: 10.1038/s41380-020-00975-1
pii: 10.1038/s41380-020-00975-1
pmc: PMC8254824
mid: EMS114690
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2651-2662Subventions
Organisme : Wellcome Trust
ID : 104036/Z/14/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R024065/1
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : R01 AG054628
Pays : United States
Organisme : Medical Research Council
ID : G0700704
Pays : United Kingdom
Organisme : Medical Research Council
ID : G1001245
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_PC_17209
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0701120
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/K026992/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/M013111/1
Pays : United Kingdom
Organisme : NICHD NIH HHS
ID : P2C HD042849
Pays : United States
Informations de copyright
© 2021. The Author(s).
Références
Prince M, Knapp M, Guerchet M, McCrone P, Prina M, Comas-Herrera A, et al. Dementia UK: update. Alzheimer’s Society; 2014. ISBN: 978-1-906647-31-5.
Fineberg NA, Haddad PM, Capenter L, Gannon B, Sharpe R, Young AH, et al. The size, burden and cost of disorders of the brain in the UK. J Psychopharmacol. 2013;27:761–70.
pubmed: 23884863
pmcid: 3778981
doi: 10.1177/0269881113495118
Bárrios H, Narciso S, Guerreiro M, Maroco J, Logsdon R, de Mendonça A. Quality of life in patients with mild cognitive impairment. Aging Ment Health. 2013;17:287–92.
pubmed: 23215827
doi: 10.1080/13607863.2012.747083
Wimo A, Jönsson L, Bond J, Prince M, Winblad B. The worldwide economic impact of dementia 2010. Alzheimers Dement. 2013;9:1–11.e3.
pubmed: 23305821
doi: 10.1016/j.jalz.2012.11.006
Fjell AM, Walhovd KB. Neuroimaging results impose new views on Alzheimer’s disease-the role of amyloid revised. Mol Neurobiol. 2012;45:153–17.
pubmed: 22201015
doi: 10.1007/s12035-011-8228-7
Raz N, Lindenberger U. Only time will tell: cross-sectional studies offer no solution to the age-brain-cognition triangle—comment on Salthouse (2011). Psychol Bull. 2011;137:790–5.
pubmed: 21859179
pmcid: 3160731
doi: 10.1037/a0024503
Salthouse TA. Neuroanatomical substrates of age-related cognitive decline. Psychol Bull. 2011;137:753–84.
pubmed: 21463028
pmcid: 3132227
doi: 10.1037/a0023262
Cox SR, Ritchie SJ, Tucker-Drob EM, Liewald DC, Hagenaars SP, Davies G, et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat Commun. 2016;7:13629.
pubmed: 27976682
pmcid: 5172385
doi: 10.1038/ncomms13629
Fjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neuosci. 2010;21:187–221.
Wardlaw JM, Valdés Hernández MC, Muñoz, Maniega S. What are white matter hyperintensities made of? J Am Heart Assoc. 2015;4:e001140.
pmcid: 4599520
doi: 10.1161/JAHA.114.001140
Fjell AM, McEvoy L, Holland D, Dale AM, Walhovd KB. What is normal in normal aging? Effects of aging, amyloid and Alzheimer’s disease on the cerebral cortex and the hippocampus. Prog Neurobiol. 2014;117:20–40.
pubmed: 24548606
pmcid: 4343307
doi: 10.1016/j.pneurobio.2014.02.004
Doan NT, Engvig A, Zaske K, Persson K, Lund MJ, Kaufmann T, et al. Distinguishing early and late brain aging from the Alzheimer’s disease spectrum: consistent morphological patterns across independent samples. Neuroimage. 2017;158:282–95.
pubmed: 28666881
doi: 10.1016/j.neuroimage.2017.06.070
Douaud G, Groves AR, Tamnes CK, Westlye LT, Duff EP, Engvig A, et al. A common brain network links development, aging and vulnerability to disease. Proc Natl Acad Sci. 2014;111:17648–53.
pubmed: 25422429
pmcid: 4267352
doi: 10.1073/pnas.1410378111
Doucet GE, Moser DA, Rodrigue A, Bassett DS, Glahn DC, Frangou S. Person-based brain morphometric similarity is heritable and correlates with biological features. Cereb Cortex. 2019;29:852–62.
pubmed: 30462205
doi: 10.1093/cercor/bhy287
Hafkemeijer A, Altmann-Schneider I, de Craen AJM, Slagboom PE, van der Grond, Rombouts SARB. Associations between age and gray matter volume in anatomical brain networks in middle-aged to older adults. Aging Cell. 2014;13:1068–74.
pubmed: 25257192
pmcid: 4326918
doi: 10.1111/acel.12271
Smith SM, Elliott LT, Alfaro-Almagro F, McCarthy P, Nichols TE, Douaud G, et al. Brain aging comprises multiple modes of structural and functional change with distinct genetic and biophysical associations. eLife. 2020;9:e52677.
pubmed: 32134384
pmcid: 7162660
doi: 10.7554/eLife.52677
Molenaar PCM. A manifesto on psychology as idiographic science: bringing the person back into scientific psychology, this time forever. Measurement. 2004;2:911–3.
Carmichael I, McLaren DG, Tommet D, Mungas D, Jones RN, Alzheimer’s Disease Neuroimaging Initiative. Coevolution of brain structures in amnestic mild cognitive impairment. Neuroimage. 2013;66:449–56.
Corley J, Cox SR, Deary IJ. Healthy cognitive ageing in the Lothian Birth Cohort studies: marginal gains not magic bullet. Psychol Med. 2018;48:187–207.
pubmed: 28595670
doi: 10.1017/S0033291717001489
Deary IJ, Gow AJ, Pattie A, Starr JM. Cohort profile: the Lothian Birth Cohorts of 1921 and 1936. Int J Epidemiol. 2012;41:1576–84.
pubmed: 22253310
doi: 10.1093/ije/dyr197
Taylor AM, Pattie A, Deary IJ. Cohort profile update: the Lothian Birth Cohorts of 1921 and 1936. Int J Epidemiol. 2018;47:1042–1042r.
pubmed: 29546429
pmcid: 6124629
doi: 10.1093/ije/dyy022
Wardlaw JM, Bastin ME, Valdés Hernández MC, Muñoz Maniega S, Royle NA, 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. Int J Stroke. 2011;6:547–59.
pubmed: 22111801
doi: 10.1111/j.1747-4949.2011.00683.x
Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis I: segmentation and surface reconstruction. Neuroimage. 1999;9:179–94.
pubmed: 9931268
doi: 10.1006/nimg.1998.0395
Fischl B, Sereon MI, Dale AM. Cortical surface based analysis II: Inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207.
pubmed: 9931269
doi: 10.1006/nimg.1998.0396
Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14:11–22.
pubmed: 14654453
doi: 10.1093/cercor/bhg087
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labelling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31:968–80.
pubmed: 16530430
doi: 10.1016/j.neuroimage.2006.01.021
Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012;61:1402–18.
pubmed: 22430496
doi: 10.1016/j.neuroimage.2012.02.084
Ritchie SJ, Tucker-Drob EM, Starr JM, Deary IJ. Do cognitive and physical functions age in concert from age 70 to 76? Evidence from the Lothian Birth Cohort 1936. Span J Psychol. 2016;19:1–12.
doi: 10.1017/sjp.2016.85
Tucker-Drob EM, Briley DA, Starr JM, Deary IJ. Structure and correlates of cognitive aging in a narrow age cohort. Psychol Aging. 2014;29:236–49.
pubmed: 24955992
pmcid: 4067230
doi: 10.1037/a0036187
Wechsler D. Wechsler Adult Intelligence Scale III-UK administration and scoring manual. London: Psychological Corporation; 1998.
Wechsler D. Wechsler Memory Scale III-UK administration and scoring manual. London: Psychological Corporation; 1998.
Deary IJ, Simonotto E, Meyer M, Marshall A, Marshall I, Goddard N, et al. The functional anatomy of inspection time: an event-related fMRI study. Neuroimage. 2004;22:1466–79.
pubmed: 15275904
doi: 10.1016/j.neuroimage.2004.03.047
Deary IJ, Der G, Ford G. Reaction times and intelligence differences—a population-based cohort study. Intelligence. 2001;29:389–99.
doi: 10.1016/S0160-2896(01)00062-9
Folstein MF, Folstein SE, McHugh PR. “Mini-mental status”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–98.
pubmed: 1202204
doi: 10.1016/0022-3956(75)90026-6
Wenham PR, Price WM, Blandell G. Apolipoprotein E genotypic by one-stage PCR. Lancet. 1991;337:1158–9.
pubmed: 1674030
doi: 10.1016/0140-6736(91)92823-K
R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2015. https://www.R-project.org/ .
Rosseel Y. lavaan: an R package for structural equation modeling. J Stat Softw. 2012;48:1–36.
doi: 10.18637/jss.v048.i02
Revelle, W. Package psych. 2019. https://cran.r-project.org/web/packages/psych/psych.pdf .
Schmid J, Leiman JM. The development of hierarchical factor solutions. Psychometrika. 1957;22:53–61.
doi: 10.1007/BF02289209
Kong X-Z, Mathias SR, Guadalupe T, ENIGMA Laterality Working Group, Glahn DC, Franke B, et al. Mapping cortical brain asymmetry in 17,141 heathy individuals worldwide via the ENIGMA Consortium. Proc Natl Acad Sci USA. 2018;115:E5154–63.
pubmed: 29764998
pmcid: 5984496
doi: 10.1073/pnas.1718418115
Takao H, Abe O, Yamasue H, Aoki S, Kasai K, Sasaki H, et al. Aging effects of cerebral asymmetry: a voxel-based morphometry and diffusion tensor imaging study. Magn Reson Imaging. 2010;28:65–9.
pubmed: 19553049
doi: 10.1016/j.mri.2009.05.020
Burt C. The factorial study of temperamental traits. Br J Stat Psychol. 1948;1:178–203.
doi: 10.1111/j.2044-8317.1948.tb00236.x
Deary IJ, Penke L, Johnson W. The neuroscience of human intelligence differences. Nat Rev Neurosci. 2010;11:201–11.
pubmed: 20145623
doi: 10.1038/nrn2793
McArdle JJ. Dynamic but structural equation modeling of repeated measures data. In: Nesselroade JR, Cattell RB, editors. Handbook of multivariate experimental psychology. New York, NY: Springer US; 1988. p. 561–614.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;57:289–300.
Muthén LK, Muthén BO. Mplus User’s Guide. eighth ed. Los Angeles, CA: Muthén & Muthén; 1998–2017.
Tucker-Drob EM, Brandmaier AM, Lindenberger U. Coupled cognitive changes in adulthood: a meta-analysis. Psychol Bull. 2019;143:273–301.
doi: 10.1037/bul0000179
Buckner RL. Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. Neuron. 2004;44:195–208.
pubmed: 15450170
doi: 10.1016/j.neuron.2004.09.006
MacPherson SE, Philliips LH, Della Sala S. Age, executive function, and social decision making: a dorsolateral prefrontal theory of cognitive ageing. Psychol Aging. 2002;17:598–609.
pubmed: 12507357
doi: 10.1037/0882-7974.17.4.598
Habes M, Janowitz D, Erus G, Toledo JB, Resnick SM, Doshi J, et al. Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns. Transl Psychiatry. 2016;6:e775.
pubmed: 27045845
pmcid: 4872397
doi: 10.1038/tp.2016.39
Seltman RE, Matthews BR. Frontotemporal lobar degeneration: epidemiology, pathology, diagnosis and management. CNS Drugs. 2012;26:841–70.
pubmed: 22950490
doi: 10.2165/11640070-000000000-00000
Crutch SJ, Lehmann M, Schott JM, Rabinovici GD, Rossor MN, Fox NC. Posterior cortical atrophy. Lancet Neurol. 2012;11:170–8.
pubmed: 22265212
pmcid: 3740271
doi: 10.1016/S1474-4422(11)70289-7
Firth NC, Primativo S, Marinescu R-V, Shakespeare TJ, Suarez-Gonzalez A, Lehmann M, et al. Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy. Brain. 2019;142:2082–95.
pubmed: 31219516
pmcid: 6598737
doi: 10.1093/brain/awz136
Snowden JS, Stopford CL, Julien CL, Thompson JC, Davidson Y, Gibbons L, et al. Cognitive phenotypes in Alzheimer’s disease and genetic risk. Cortex. 2007;43:835–45.
pubmed: 17941342
doi: 10.1016/S0010-9452(08)70683-X
Liu CC, Liu CC, Kanekiyo T, Xu H, Bu G. Apolipoprotein E and Alzheimer’s disease: risk, mechanisms and therapy. Nat Rev Neurol. 2013;9:106–18.
pubmed: 23296339
pmcid: 3726719
doi: 10.1038/nrneurol.2012.263
Jack CR Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12:207–16.
pubmed: 23332364
pmcid: 3622225
doi: 10.1016/S1474-4422(12)70291-0
Johnson W, Brett CE, Calvin C, Deary IJ. Childhood characteristics and participation in Scottish Mental Survey 1947 6-day sample follow-ups: implications for participation in aging studies. Intelligence. 2016;54:70–9.
doi: 10.1016/j.intell.2015.11.006
Ritchie SJ, Hill WD, Marioni RE, Davies G, Hagenaars SP, Harris SE, et al. Polygenic predictors of age-related decline in cognitive ability. Mol Psychiatry. 2020;25:2584–98.
pubmed: 30760887
doi: 10.1038/s41380-019-0372-x