Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life
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Abstract
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.
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Gray matter volume decrease is well described in aging.
“Gray matter volume decrease is well described in aging [42], but it is not necessarily correlated with a loss of neurons [43] or synaptic density decrease (as measured by 11C-UCB-J) [35].”
From Preliminary evidence for preserved synaptic density in late-life depression (2024) · cites this paper as [42]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · cited 5× · see citing works
“No other correlations among cognitive changes and either of the secondary factors of cortical change were FDR-significant (all rs ≤ |0.143|).”
InfluentialFolstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · cited 2× · see citing works
“The Mini-Mental State Examination (MMSE; Folstein et al., 1975) was also administered.”
<b>lavaan</b> : An <i>R</i> Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,803 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · see citing works
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · cited 1× · see citing works
“This involved segmentation of each volume, identifying brain tissue types, followed by parcellation of cortical grey matter into 34 regions per hemisphere, according to the Desikan-Killiany atlas 26 (Supplementary Figure 1).”
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · cited 1× · see citing works
“1 23–25 .”
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147 · see citing works
Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70291-0 · Cited by 4,396 · see citing works
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208 · cited 1× · see citing works
“1 23–25 .”
Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy
Liu, C.-C., Kanekiyo, T., Xu, H., & Bu, G. (2013) · Nature Reviews Neurology · doi:10.1038/nrneurol.2012.263 · Cited by 3,459 · see citing works
Within-subject template estimation for unbiased longitudinal image analysis
Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.084 · Cited by 2,356 · see citing works
Wechsler Adult Intelligence Scale--Third Edition
Wechsler, D. (2019) · PsycTESTS Dataset · doi:10.1037/t49755-000 · Cited by 2,327 · see citing works
Molenaar, P. C. M. (2004) · Measurement: Interdisciplinary Research & Perspective · doi:10.1207/s15366359mea0204_1 · Cited by 1,943 · see citing works
Memory and Executive Function in Aging and AD
Buckner, R. L. (2004) · Neuron · doi:10.1016/j.neuron.2004.09.006 · Cited by 1,508 · cited 1× · see citing works
“The fronto-temporal pattern is partly consistent with frontal ageing accounts of cognitive decline [48, 49], and with the partial overlap between fronto-temporal ageing in healthy and Alzheimer’s-type patterns [50].”
On the adaptive control of the false discovery rate in multiple testing with independent statistics
Benjamini, Y., & Hochberg, Y. (2000) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986025001060 · Cited by 1,349 · see citing works
The worldwide economic impact of dementia 2010
Wimo, A. B., Jönsson, L., Bond, J., Prince, M. J., Winblad, B. G., & International, A. D. (2013) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.11.006 · Cited by 1,142 · see citing works
The Development of Hierarchical Factor Solutions
Schmid, J., & Leiman, J. M. (1957) · Psychometrika · doi:10.1007/bf02289209 · Cited by 1,098 · cited 1× · see citing works
“Based on this information, we conducted a Schmid–Leiman [39] transformation (from the psych package) to examine the oblique factor structure beyond any common variance shared (i.”
Structural Brain Changes in Aging: Courses, Causes and Cognitive Consequences
Fjell, A. M., & Walhovd, K. B. (2010) · Reviews in the Neurosciences · doi:10.1515/revneuro.2010.21.3.187 · Cited by 1,085 · see citing works
Apolipoprotein E genotyping by one-stage PCR
Wenham, P. R., Price, W. H., & Blundell, G. (1991) · The Lancet · doi:10.1016/0140-6736(91)92823-k · Cited by 1,070 · cited 1× · see citing works
“Status was determined according to genotyping on the two polymorphic sites (rs7412 and rs429358) that account for e2, e3 and e4 alleles (Wenham et al., 1991).”
The neuroscience of human intelligence differences
Deary, I. J., Penke, L., & Johnson, W. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2793 · Cited by 1,038 · cited 1× · see citing works
“2 and [43]), and for the correlated cognitive domains of visuospatial, processing speed and memory, as well”
Self-citationWhat are White Matter Hyperintensities Made of?
Wardlaw, J. M., Hernández, M. C. V., & Maniega, S. M. (2015) · Journal of the American Heart Association · doi:10.1161/jaha.114.001140 · Cited by 902 · see citing works
Self-citationFjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2014.02.004 · Cited by 799 · cited 1× · see citing works
“Age effects appear to be stronger for some regions than others, with those areas ontogenetically and phylogenetically latest to develop—those that are more strongly linked to more complex cognitive functions—being those most affected [11].”
Dynamic but Structural Equation Modeling of Repeated Measures Data
Mcardle, J. (1988) · Springer eBooks · doi:10.1007/978-1-4613-0893-5_17 · Cited by 670 · cited 1× · see citing works
“intercept and slope, and these contribute to an overall latent intercept and slope of all cognitive tests in that domain) [44].”
MacPherson, S. E., Phillips, L. H., & Sala, S. D. (2002) · Psychology and Aging · doi:10.1037/0882-7974.17.4.598 · Cited by 590 · cited 1× · see citing works
“The fronto-temporal pattern is partly consistent with frontal ageing accounts of cognitive decline 48,49 , and with the partial overlap between fronto-temporal ageing in healthy and Alzheimer’s-type patterns 50 .”
Crutch, S. J., Lehmann, M., Schott, J. M., Rabinovici, G. D., Rossor, M. N., & Fox, N. C. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(11)70289-7 · Cited by 580 · cited 2× · see citing works
“, 2007), though its prevalence is currently unknown, and it is relatively under-researched (Crutch et al., 2012).”
Ageing and brain white matter structure in 3,513 UK Biobank participants
Cox, S. R., Ritchie, S. J., Tucker-Drob, E. M., Liewald, D. C., Hagenaars, S. P., Davies, G., Wardlaw, J. M., Gale, C. R., Bastin, M. E., & Deary, I. J. (2016) · Nature Communications · doi:10.1038/ncomms13629 · Cited by 487 · cited 1× · see citing works
“To test whether the identified correlation structure for left hemisphere intercepts and slopes was replicable (hemisphere differences in brain structural measures are extremely modest-to-null according to cross-sectional estimates [8, 40, 41]), we then repeated this exploratory analysis for the right hemisphere ROIs.”
Self-citationCohort Profile: The Lothian Birth Cohorts of 1921 and 1936
Deary, I. J., Gow, A. J., Pattie, A., & Starr, J. M. (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr197 · Cited by 401 · see citing works
Self-citationMapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
Kong, X.-Z., Mathias, S., Guadalupe, T., Glahn, D., Franke, B., Crivello, F., Tzourio-mazoyer, N., Fisher, S., Thompson, P., & Francks, C. (2018) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1718418115 · Cited by 392 · see citing works
Self-citationNeuroanatomical substrates of age-related cognitive decline.
Salthouse, T. A. (2011) · Psychological Bulletin · doi:10.1037/a0023262 · Cited by 387 · cited 2× · see citing works
“Whereas cross-sectional information can potentially be informative for ageing, it has been strongly criticised in some quarters since it—unlike analysis of longitudinal data—is unable to adequately approximate the dimensionality and time-dependent dynamics of ageing 5–7 .”
Reaction times and intelligence differences: a population-based cohort study.
Deary, I. (2001) · Intelligence · doi:10.1016/s0160-2896(01)00062-9 · Cited by 361 · see citing works
Self-citationA common brain network links development, aging, and vulnerability to disease
Douaud, G., Groves, A. R., Tamnes, C. K., Westlye, L. T., Duff, E. P., Engvig, A., Walhovd, K. B., James, A., Gass, A., Monsch, A. U., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1410378111 · Cited by 317 · see citing works
Cognitive Phenotypes in Alzheimer's Disease and Genetic Risk
Snowden, J. S., Stopford, C. L., Julien, C. L., Thompson, J. C., Davidson, Y., Gibbons, L., Pritchard, A., Lendon, C. L., Richardson, A. M., Varma, A., et al. (2007) · Cortex · doi:10.1016/s0010-9452(08)70683-x · Cited by 254 · cited 2× · see citing works
“It is estimated to account for around 8% of Alzheimer’s disease cases (Snowden et al., 2007), though its prevalence is currently unknown, and it is relatively under-researched (Crutch et al.”
Cohort Profile Update: The Lothian Birth Cohorts of 1921 and 1936
Taylor, A. M., Pattie, A., & Deary, I. J. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy022 · Cited by 235 · cited 1× · see citing works
“Participants were drawn from the Lothian Birth Cohort 1936 (LBC1936; Deary et al., 2012; Taylor et al., 2018; Corley et al., 2018), a longitudinal study of brain and cognitive ageing in healthy community-dwelling older adults.”
Self-citationThe size, burden and cost of disorders of the brain in the UK
Fineberg, N. A., Haddad, P. M., Carpenter, L., Gannon, B., Sharpe, R., Young, A. H., Joyce, E. M., Rowe, J. B., Wellsted, D. M., Nutt, D., et al. (2013) · Journal of Psychopharmacology · doi:10.1177/0269881113495118 · Cited by 213 · cited 1× · see citing works
“The eighth decade of life is a time in which risk for cognitive decline and dementia begins to accelerate markedly 1 , alongside consequent increases in personal and societal burden, and poorer quality of life 2–4 .”
Coupled cognitive changes in adulthood: A meta-analysis.
Tucker–Drob, E. M., Brandmaier, A. M., & Lindenberger, U. (2019) · Psychological Bulletin · doi:10.1037/bul0000179 · Cited by 197 · cited 1× · see citing works
“Just as differential psychology has determined that a single psychological factor may largely underlie age-related declines across multiple cognitive tests (Tucker-Drob et al., 2019), so the current findings indicate that a large proportion of cortical atrophy occurs across a single dimension – and furthermore, that g…”
Self-citationSmith, S. M., Elliott, L. T., Alfaro‐Almagro, F., McCarthy, P., Nichols, T. E., Douaud, G., & Miller, K. L. (2020) · eLife · doi:10.7554/elife.52677 · Cited by 194 · cited 1× · see citing works
“More recent accounts of brain organisation have used cross-sectional data to identify clusters of regions with shared morphometric characteristics 12–16 .”
Wardlaw, J. M., Bastin, M. E., Hernández, M. C. V., Maniega, S. M., Royle, N. A., Morris, Z., Clayden, J. D., Sandeman, E. M., Eadie, E., Murray, C. L., et al. (2011) · International Journal of Stroke · doi:10.1111/j.1747-4949.2011.00683.x · Cited by 189 · cited 1× · see citing works
“MRI Acquisition & Analysis Brain structural MRI data were acquired according to a previously published protocol (Wardlaw et al., 2011) at Waves 2, 3 and 4.”
Self-citationRaz, N., & Lindenberger, U. (2011) · Psychological Bulletin · doi:10.1037/a0024503 · Cited by 187 · see citing works
Quality of life in patients with mild cognitive impairment
Bárrios, H., Narciso, S., Guerreiro, M., Marôco, J. P., Logsdon, R. G., & de Mendonça, A. (2012) · Aging & Mental Health · doi:10.1080/13607863.2012.747083 · Cited by 180 · cited 1× · see citing works
“, 2014), alongside consequent increases in personal and societal burden, and poorer quality of life (Fineberg et al., 2013, Wimo et al., 2013; Bárrios et al., 2013).”
The functional anatomy of inspection time: an event-related fMRI study
Deary, I. J., Simonotto, E., Meyer, M. E., Marshall, A., Marshall, I., Goddard, N., & Wardlaw, J. M. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.047 · Cited by 154 · cited 1× · see citing works
“Processing Speed was measured with Symbol Search and Digit Symbol Substitution from the WAIS III, Visual Inspection Time [32] and Four-Choice Reaction Time [33].”
Self-citationHabes, M., Janowitz, D., Erus, G., Toledo, J. B., Resnick, S. M., Doshi, J. J., der Auwera, S. V., Wittfeld, K., Hegenscheid, K., Hosten, N., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.39 · Cited by 142 · see citing works
THE FACTORIAL STUDY OF TEMPERAMENTAL TRAITS
Burt, C. (1948) · British Journal of Statistical Psychology · doi:10.1111/j.2044-8317.1948.tb00236.x · Cited by 140 · cited 1× · see citing works
“We used Pearson’s r and the coefficient of factor congruence (Burt, 1948) to formally quantify whether the resultant factor loading pattern replicated across hemispheres.”
Frontotemporal Lobar Degeneration: Epidemiology, Pathology, Diagnosis and Management
Seltman, R. E., & Matthews, B. R. (2012) · CNS Drugs · doi:10.2165/11640070-000000000-00000 · Cited by 132 · cited 2× · see citing works
“Whereas we consider it unlikely that our results are predominantly driven by such effects (given the general prevalence of these cases in the population; Snowden et al., 2007; Seltman and Matthews, 2012), neurostructural hallmarks may be detectable prior to the onset of cognitive impairments (Jack Jr et al.”
Hafkemeijer, A., Altmann‐Schneider, I., de Craen, A. J. M., Slagboom, P. E., van der Grond, J., & Rombouts, S. A. R. B. (2014) · Aging Cell · doi:10.1111/acel.12271 · Cited by 127 · see citing works
Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy
Firth, N. C., Primativo, S., Marinescu, R. V., Shakespeare, T. J., Gonzalez, A. S., Lehmann, M., Carton, A. M., Ocal, D., Pavisic, I. M., Paterson, R. W., et al. (2019) · Brain · doi:10.1093/brain/awz136 · Cited by 83 · cited 1× · see citing works
“In contrast, posterior cortical atrophy (PCA) is characterised by selective decline in functions mainly reliant on parietal and occipital brain regions 52,53 .”
Structure and correlates of cognitive aging in a narrow age cohort.
Tucker–Drob, E. M., Briley, D. A., Starr, J. M., & Deary, I. J. (2014) · Psychology and Aging · doi:10.1037/a0036187 · Cited by 69 · cited 1× · see citing works
“These were categorised into three cognitive domains based upon well-fitting, hierarchical structural equation models tested in our previously published work [28, 29].”
Self-citationHealthy cognitive ageing in the Lothian Birth Cohort studies: marginal gains not magic bullet
Corley, J., Cox, S. R., & Deary, I. J. (2017) · Psychological Medicine · doi:10.1017/s0033291717001489 · Cited by 68 · cited 1× · see citing works
“Participants were drawn from the Lothian Birth Cohort 1936 (LBC1936; Deary et al., 2012; Taylor et al., 2018; Corley et al., 2018), a longitudinal study of brain and cognitive ageing in healthy community-dwelling older adults.”
Self-citationRecurrent and chronic appendicitis.
Crabbe, M. M., Norwood, S. H., Robertson, H. D., & SILVA, J. S. (1986) · PubMed · Cited by 67 · see citing works
Polygenic predictors of age-related decline in cognitive ability
Ritchie, S. J., Hill, W. D., Marioni, R. E., Davies, G., Hagenaars, S. P., Harris, S. E., Cox, S. R., Taylor, A. M., Corley, J., Pattie, A., et al. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0372-x · Cited by 56 · cited 1× · see citing works
“neatly illustrated elsewhere, using cognitive data from the present cohort [58].”
Self-citationNeuroimaging Results Impose New Views on Alzheimer’s Disease—the Role of Amyloid Revised
Fjell, A. M., & Walhovd, K. B. (2011) · Molecular Neurobiology · doi:10.1007/s12035-011-8228-7 · Cited by 47 · cited 8× · see citing works
“Whereas cross-sectional information can potentially be informative for ageing, it has been strongly criticised in some quarters since it—unlike analysis of longitudinal data—is unable to adequately approximate the dimensionality and time-dependent dynamics of ageing 5–7 .”
InfluentialDoan, N. T., Engvig, A., Zaske, K., Persson, K., Lund, M. J., Kaufmann, T., Córdova‐Palomera, A., Alnæs, D., Moberget, T., Brækhus, A., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.070 · Cited by 42 · cited 2× · see citing works
“…these analyses once participants with a self-reported diagnosis of dementia or MMSE score > 24 (N = 31) or neuroradiologically-identified stroke (N = 119) at any wave had been removed did not substantially alter the patterns (Supplementary Tables 11 and 12, and Supplementary Figures 5 and 6).”
Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features
Doucet, G. E., Moser, D. A., Rodrigue, A., Bassett, D. S., Glahn, D. C., & Frangou, S. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy287 · Cited by 38 · cited 4× · see citing works
“Re-running these analyses once participants with a self-reported diagnosis of dementia or MMSE score > 24 (N = 31) or neuroradiologically-identified stroke (N = 119) at any wave had been removed did not substantially alter the patterns (Supplementary Tables 11 and 12, and Supplementary Figures 5 and 6).”
InfluentialRitchie, S. J., Tucker–Drob, E. M., Starr, J. M., & Deary, I. J. (2016) · The Spanish Journal of Psychology · doi:10.1017/sjp.2016.85 · Cited by 33 · see citing works
Self-citationCoevolution of brain structures in amnestic mild cognitive impairment
Carmichael, O. T., McLaren, D. G., Tommet, D., Mungas, D., & Jones, R. N. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.029 · Cited by 31 · cited 4× · see citing works
“It is also notable that these three factors of change are similar to three of the five aspects of cortical change identified using a similar factor analytic method, in participants with MCI 18 .”
InfluentialEthics, Science and Mind Control: J. M. Rodríguez-Delgado’s Legacy
Vera, J. A., & Martínez‐Sánchez, F. (2016) · The Spanish Journal of Psychology · doi:10.1017/sjp.2016.2 · Cited by 25 · see citing works
Aging effects on cerebral asymmetry: a voxel-based morphometry and diffusion tensor imaging study
Takao, H., Abe, O., Yamasue, H., Aoki, S., Kasai, K., Sasaki, H., & Ohtomo, K. (2009) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2009.05.020 · Cited by 20 · cited 1× · see citing works
“To test whether the identified correlation structure for intercepts and slopes was replicable (age differences in brain structural measures are extremely modest-to-null according to cross-sectional estimates; Takao et al., 2010; Cox et al., 2016; Kong et al., 2018), we then repeated this exploratory analysis for the r…”
Johnson, W., Brett, C. E., Calvin, C. M., & Deary, I. J. (2015) · Intelligence · doi:10.1016/j.intell.2015.11.006 · Cited by 17 · cited 1× · see citing works
“Moreover, the present results indicate that these additional patterns are relatively subtle among relatively healthy and range restricted [57] group of older adults.”
Self-citationPotential antimalarials. 8. 10-Substituted 9-phenanthrenemethanols
Washburn, L. C., & PEARSON, D. E. (1974) · Journal of Medicinal Chemistry · doi:10.1021/jm00253a004 · Cited by 11 · see citing works
Polygenic predictors of age-related decline in cognitive abilityPreprint
Ritchie, S. J., Hill, W. D., Marioni, R. E., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/375691 · Cited by 9 · see citing works
Self-citationPrince, M., Knapp, M., Guerchet, M., McCrone, P., Prina, M., Comas-Herrera, M., Wittenberg, A., Adelaja, R., Hu, B., King, B., et al. (2014) · Alzheimer's Society · doi:10.13140/2.1.3629.3446
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- [46]Muthén LK Muthén BO 1998-2017, Mplus User’s Guide Los Angeles, CA Muthén & Muthén Eighth Edition
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Sorted by citations
Timing of lifespan influences on brain and cognition
Walhovd, K. B., Lövden, M., & Fjell, A. M. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.07.001 · Cited by 112 · see citing works
“An additional limitation is that there are multiple aspects of brain aging that are merged into one metric of BrainAge [145].”
InfluentialA strong dependency between changes in fluid and crystallized abilities in human cognitive aging
Tucker–Drob, E. M., de la Fuente, J., Köhncke, Y., Brandmaier, A. M., Nyberg, L. H., & Lindenberger, U. (2022) · Science Advances · doi:10.1126/sciadv.abj2422 · Cited by 84 · see citing works
Self-citationA Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity
Jiang, R., Scheinost, D., Zuo, N., Wu, J., Qi, S., Liang, Q., Zhi, D., Luo, N., Chung, Y., Liu, S., et al. (2022) · Advanced Science · doi:10.1002/advs.202201621 · Cited by 62 · see citing works
The Influence of Virus Infection on Microglia and Accelerated Brain Aging
Filgueira, L., Larionov, A. A., & Lannes, N. (2021) · Cells · doi:10.3390/cells10071836 · Cited by 57 · see citing works
General dimensions of human brain morphometry inferred from genome‐wide association data
Fürtjes, A. E., Arathimos, R., Coleman, J. R. I., Cole, J. H., Cox, S. R., Deary, I. J., de la Fuente, J., Madole, J. W., Tucker–Drob, E. M., & Ritchie, S. J. (2023) · Human Brain Mapping · doi:10.1002/hbm.26283 · Cited by 49 · see citing works
Self-citationSørensen, Ø., Walhovd, K. B., & Fjell, A. M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117596 · Cited by 50 · see citing works
Corley, J., Conte, F. P., Harris, S. E., Taylor, A. M., Redmond, P. H., Russ, T. C., Deary, I. J., & Cox, S. R. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01900-4 · Cited by 40 · see citing works
“A SEM-based “factor-of-curves” [53] approach was used, as has been done previously in this cohort [36, 54] which postulates the existence of common latent variables of cognitive change that underlie the distribution of explicit or observable variables (individual cognitive tests).”
MethodSelf-citationHamilton, O. K., Cox, S. R., Okely, J. A., Conte, F. P., Ballerini, L., Bastin, M. E., Corley, J., Taylor, A. M., Page, D., Gow, A. J., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01495-4 · Cited by 40 · see citing works
“been done previously in this cohort (38,39), to examine the initial level and subsequent decline in”
BackgroundMethodSelf-citationMRI-based biomarkers of accelerated aging and dementia risk in midlife: how close are we?
Elliott, M. L. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101075 · Cited by 38 · see citing works
Nyberg, L. H., Andersson, M., Lundquist, A., Baaré, W. F. C., David Bartrés-faz, A., Bertram, L., Boraxbekk, C., Brandmaier, A. M., Demnitz, N., Drevon, C. A., et al. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac400 · Cited by 31 · see citing works
“Elevated cortical and hippocampus atrophy was expected to be associated with lower episodic memory and “APOE-ε4” carriership (cf., Buckner 2004; Rast et al. 2018; Cox et al. 2021).”
BackgroundMethodInfluentialBrain and cognitive ageing: The present, and some predictions (…about the future)
Cox, S. R., & Deary, I. J. (2022) · Aging Brain · doi:10.1016/j.nbas.2022.100032 · Cited by 28 · see citing works
“In older adults, greater negative age associations are found in cortical areas such as prefrontal and temporal cortices [30,93,12,35,65,28].”
BackgroundSelf-citationMaintaining brain health across the lifespan
García‐García, I., Donica, O., Cohen, A. A., Gonseth, S., Heini, A. F., Nusslé, S. C., Pichard, C., Rietschel, E., Tanačković, G., Folli, S., et al. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105365 · Cited by 26 · see citing works
Reliability of structural brain change in cognitively healthy adult samples
Dídac Vidal-piñeiro, A., Sørensen, Ø., Strømstad, M., Amlien, I. K., Anderson, M., Baaré, W. F. C., Bartrés‐Faz, D., Brandmaier, A. M., Bråthen, A. C. S., Garrido, P., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00547 · Cited by 22 · see citing works
“Brain changes in cortical thickness, area, and volume are correlated throughout the brain (within modality) though the strength of these correlations is unclear ( Cox et al., 2021; Sele et al., 2021), and the degree to which measurement error co-varies across the brain is unknown.”
Smith, E. T., Hennessee, J. P., Wig, G. S., Frank, S., González, H. M., Bacci, J. R., Chan, M. Y., Carreno, C. A., Kennedy, K. M., Rodrigue, K. M., et al. (2023) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2023.04.014 · Cited by 20 · see citing works
Johansson, J. J., Wåhlin, A., Lundquist, A., Brandmaier, A. M., Lindenberger, U., & Nyberg, L. H. (2022) · Aging Brain · doi:10.1016/j.nbas.2021.100027 · Cited by 19 · see citing works
“In the present study we investigated the potential existence of a specific relationship between hippocampus/MTL atrophy and episodic memory decline that extends beyond general dimensions of correlated change in brain (wholebrain gray matter) and cognition (episodic memory, processing speed, fluid intelligence, verbal…”
BackgroundAribisala, B. S., Hernández, M. D. C. V., Okely, J. A., Cox, S. R., Ballerini, L., Dickie, D. A., Wiseman, S. J., Riha, R. L., Maniega, S. M., Radaković, R., et al. (2023) · Sleep Medicine · doi:10.1016/j.sleep.2023.03.016 · Cited by 18 · see citing works
Self-citationExploratory factor analysis with structured residuals for brain network data
van Kesteren, E., & Kievit, R. (2020) · Network Neuroscience · doi:10.1162/netn_a_00162 · Cited by 18 · see citing works
“…recent work has used EFA to compare and contrast patterns of individual differences in brain structure at base-line with individual differences in developmental change over time, noting striking differences in dimensionality of change versus cross-sectional differences (Cox et al., 2020).”
InfluentialLongitudinal change-change associations of cognition with cortical thickness and surface area
Nyberg, L. H., Andersson, M., & Lundquist, A. (2023) · Aging Brain · doi:10.1016/j.nbas.2023.100070 · Cited by 16 · see citing works
“Moreover, individual differences in volume changeshavebeen found tobequite homogenous across different cortical zones [4,18].”
BackgroundPreliminary evidence for preserved synaptic density in late-life depression
Vande Casteele, T., Laroy, M., Van Cauwenberge, M., Koole, M., Dupont, P., Sunaert, S., Van den Stock, J., Bouckaert, F., Van Laere, K., Emsell, L., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02837-8 · Cited by 14 · see citing works
Xing, X.-X. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.803436 · Cited by 14 · see citing works
“These morphological changes are associated with cognitive development during the aging process (Cox et al., 2021).”
BackgroundHwang, H., Cho, G., Jin, M. J., Ryoo, J. H., Choi, Y., & Lee, S. H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247592 · Cited by 13 · see citing works
“A few studies used SEM for associating genetic and imaging data with behavioral or cognitive variables [28, 37].”
Precision brain morphometry using cluster scanning
Elliott, M. L., Nielsen, J. A., Hanford, L. C., Hamadeh, A., Hilbert, T., Kober, T., Dickerson, B. C., Hyman, B. T., Mair, R. W., Eldaief, M. C., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00175 · Cited by 12 · see citing works
Midlife Cardiovascular Fitness Is Reflected in the Brain's White Matter
d’Arbeloff, T. C., Elliott, M. L., Knodt, A. R., Sison, M. L., Melzer, T. R., Ireland, D. R., Ramrakha, S., Poulton, R., Caspi, A., Moffitt, T. E., et al. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.652575 · Cited by 10 · see citing works
“Moreover, frontotemporal atrophy is a common archetype of pathological aging and is considered one of the main causes of dementia (Fjell et al., 2015; Cox et al., 2021).”
BackgroundHofmann, S., Goltermann, O., Scherf, N., Müller, K., Loeffler, M., Villringer, A., Gaebler, M., Witte, A. V., & Beyer, F. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00497 · Cited by 9 · see citing works
“…associations between relevance and cortical GM features is in line with studies showing linear age associations in superior and inferior frontal gyri, parietal cortex, and superior parts of the temporal lobe ( Aycheh et al., 2018; Cox et al., 2021; Fjell et al., 2009; Fjell & Walhovd, 2010).”
Negrón-Oyarzo, I., Dib, T., Chacana-Véliz, L., Quilodrán, N. L., & Urrutia-Piñones, J. (2024) · Frontiers in Neural Circuits · doi:10.3389/fncir.2024.1286111 · Cited by 7 · see citing works
The influence of age and sex on the absolute cell numbers of the human brain cerebral cortex
Castro-Fonseca, E., de Morais, V. A. C., da Silva, C. G., Wollner, J., Freitas, J., Neto, A. F. M., Oliveira, L. E., de Oliveira, V. C., Leite, R. E. P., Alho, A. T. D. L., et al. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad148 · Cited by 8 · see citing works
d’Arbeloff, T. C., Cooke, M. E., Knodt, A. R., Sison, M. L., Melzer, T. R., Ireland, D. R., Poulton, R., Ramrakha, S., Moffitt, T. E., Caspi, A., et al. (2020) · Aging · doi:10.18632/aging.104112 · Cited by 8 · see citing works
“Moreover, frontotemporal atrophy is a common archetype of pathological aging and is considered one of the main causes of dementia [42, 43].”
Cox, S. R. (2024) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-010923-102441 · Cited by 8 · see citing works
Self-citationThe Volumetric Changes of the Pineal Gland with Age: An Atlas-based Structural Analysis
Sisakhti, M., Shafaghi, L., & Batouli, S. A. H. (2022) · Experimental Aging Research · doi:10.1080/0361073x.2022.2033593 · Cited by 7 · see citing works
“The representations that root in the heterogeneous origins in terms of phylogeny and ontogeny(Fjell, Mcevoy, Holland, Dale, & Walhovd, 2014) result in adopting discrete strategies for dealing with aging-induced alterations (Cox et al., 2020); therefore, cortical aging is not uniform.”
Capogna, E., Sørensen, Ø., Watne, L. O., Roe, J. M., Strømstad, M., Idland, A., Halaas, N. B., Blennow, K., Zetterberg, H., Walhovd, K. B., et al. (2024) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.12.009 · Cited by 6 · see citing works
“MRI data-driven clustering approaches have proven useful for separating subgroups of healthy older participants ( “ ageotypes ” ) with different biological, cognitive, and sociodemographic characteristics (Ahadi et al., 2020; Cox et al., 2021).”
InfluentialElliott, M. L., Du, J., Nielsen, J. A., Hanford, L. C., Kivisäkk, P., Arnold, S. E., Dickerson, B. C., Mair, R. W., Eldaief, M. C., & Buckner, R. L. (2026) · Nature Communications · doi:10.1038/s41467-026-68886-3 · Cited by 9 · see citing works
“5, 6, 7, and 8).”
Vulnerability to memory decline in aging revealed by a mega-analysis of structural brain change
Dídac Vidal-piñeiro, A., Sørensen, Ø., Strømstad, M., Amlien, I. K., Baaré, W. F. C., Bartrés‐Faz, D., Brandmaier, A. M., Cattaneo, G., Düzel, S., Ghisletta, P., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-66354-y · Cited by 10 · see citing works
Aging Brain from a Lifespan Perspective
Fjell, A. M. (2024) · Current topics in behavioral neurosciences · doi:10.1007/7854_2024_476 · Cited by 6 · see citing works
Brain structure and episodic learning rate in cognitively healthy ageing
Frank, D., Garo-Pascual, M., Reyes-Velásquez, P. A., Payo, B. F., Peled, N., Zhang, L., & Strange, B. A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119630 · Cited by 5 · see citing works
Sleep-dependent memory consolidation in young and aged brains
Kumar, D., Yanagisawa, M., & Funato, H. (2024) · Aging Brain · doi:10.1016/j.nbas.2024.100124 · Cited by 5 · see citing works
“…– 123] Based on a total of 1376 MRI scans, to follow cortical aging throughout the eighth decade of life in an aging cohort at ages 73, 76, and 79 years, Cox and colleagues [124] reported a significant decline (0.87 % per year) in cortical volume, although this was not consistent across regions.”
BackgroundMethodInfluentialThe interplay of age, gender and amyloid on brain and cognition in mid-life and older adults
Borne, L., Thienel, R., Lupton, M. K., Guo, C. C., Mosley, P. E., Behler, A., Giorgio, J., Adam, R. J., Ceslis, A., Bourgeat, P., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-78308-3 · Cited by 4 · see citing works
Bloch‐Ibenfeldt, M., Demnitz, N., Gates, A. T., Garde, E., Siebner, H. R., Kjær, M., & Boraxbekk, C. (2025) · BMC Geriatrics · doi:10.1186/s12877-025-05778-z · Cited by 3 · see citing works
“It is well documented that although aging may affect the brain globally [19], some regions are more prone to show atrophy such as the hippocampus [20] and the pre-frontal cortex [21], which has been further associated with cognitive aging [21, 22].”
Brain maps of general cognitive functioning: neuroimaging and neurobiological signatures
Moodie, J. E., Buchanan, C. R., Fürtjes, A. E., Conole, E. L. S., Stolicyn, A., Corley, J., Ferguson, K. J., Hernández, M. D. C. V., Maniega, S. M., Russ, T. C., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03617-8 · Cited by 5 · see citing works
“3B), with the top 5 correlations r range = − 0.77 to − 0.81 for the caudal middle frontal, rostral anterior cingulate, pars triangularis, paracentral, and temporal pole regions.”
InfluentialSelf-citationKöhncke, Y., Kühn, S., Düzel, S., Sander, M. C., Brandmaier, A. M., & Lindenberger, U. (2023) · Aging Brain · doi:10.1016/j.nbas.2023.100103 · Cited by 3 · see citing works
“In line with general maintenance, Cox and colleagues reported that a broad, cortex-wide dimension of atrophy was associated with declines in general cognitive ability as well as visuospatial ability, processing speed, and memory [7].”
BackgroundBadal, K. K., Sadhu, A., McCracken, C., Raveendra, B., Lozano‐Villada, S., Shetty, A. C., Gillette, P. R., Zhao, Y., Stommes, D. L., Fieber, L. A., et al. (2024) · Aging Cell · doi:10.1111/acel.14228 · Cited by 3 · see citing works
“…Tonoki & Davis, 2015), Aplysia (Greer et al., 2018; Kadakkuzha et al., 2013; Moroz & Kohn, 2010), rodents (Mota et al., 2019; Ximerakis et al., 2019), and humans (Adewale et al., 2021; Cox et al., 2021), have unveiled numerous molecular and cellular changes underlying aging in the nervous sys-tem.”
BackgroundVariations in perfusion detectable in advance of microstructure in white matter aging
Robinson, T. D., Sun, Y. L., Chang, P. T. H., Gauthier, C. J., & Chen, J. J. (2025) · GeroScience · doi:10.1007/s11357-025-01787-3 · Cited by 3 · see citing works
Moodie, J. E., Buchanan, C. R., Fürtjes, A. E., Conole, E. L. S., Stolicyn, A., Corley, J., Ferguson, K. J., Hernández, M. D. C. V., Maniega, S. M., Russ, T. C., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.17.628670 · Cited by 3 · see citing works
Self-citationLorenzon, G., Marseglia, A., Poulakis, K., Imbimbo, C., Rydén, L., Galaris, E., Lindberg, O. E., Shams, S., Mohanty, R., Ferreira, D., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-04583-0 · Cited by 2 · see citing works
“However, relatively few longitudinal studies have examined these associations in nonclinical and demographically uniform populations [12, 14].”
Li, Z., Li, J., Li, J., Wang, M., Xu, A., Huang, Y., Yu, Q., Zhang, L., Li, Y., Li, Z., et al. (2025) · JMIR Aging · doi:10.2196/73004 · Cited by 2 · see citing works
“The frontal lobes exhibited particularly prominent changes within an annual 0.87% reduction in overall cortical volume [54], while the temporo-parieto-occipital junction was intricately involved in high-level human neurological functions, such as language, memory, calculation, and writing [55].”
InfluentialA snapshot of brain and cognition in healthy mid-life and older adultsPreprint
Borne, L., Lupton, M. K., Guo, C. C., Mosley, P. E., Adam, R. J., Ceslis, A., Bourgeat, P., Fazlollahi, A., Maruff, P. T., Rowe, C. C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.20.476706 · Cited by 2 · see citing works
“…between fronto-temporal and occipito-parietal regions in explaining cortical aging in relation to cognitive decline has recently been highlighted in (Cox et al., 2021), showing that fronto-temporal regions explain more of cortical ageing in the eight decade of life than occipito-parietal regions.”
BackgroundLessons we learned from the Lothian Birth Cohorts of 1921 and 1936
Deary, I. J., & Cox, S. R. (2024) · Genomic psychiatry : · doi:10.61373/gp024i.0076 · Cited by 1 · see citing works
Self-citationKorkki, S. M., Narbutas, J., Salami, A., Rydén, L., Kalpouzos, G., Andersson, M., Kern, S., Skoog, J., Westman, E., Skoog, I. G., et al. (2026) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2026.06.002 · Cited by 1 · see citing works
Defining, Re-defining, and Understanding the Brain and Mental Health During Ageing
Kaur, G., & Rattan, S. I. S. (2024) · Healthy ageing and longevity · doi:10.1007/978-3-031-68513-2_1 · Cited by 1 · see citing works
Smith, E. T., Nashiro, K., O’Connell, M., Chen, X., & Basak, C. (2024) · Aging Brain · doi:10.1016/j.nbas.2024.100131 · Cited by 2 · see citing works
Corley, J., Conte, F. P., Harris, S. E., Taylor, A. M., Redmond, P. H., Russ, T. C., Deary, I. J., & Cox, S. R. (2022) · medRxiv · doi:10.1101/2022.02.25.22271448 · Cited by 1 · see citing works
Self-citationHofmann, S., Goltermann, O., Scherf, N., Müller, K., Loeffler, M., Villringer, A., Gaebler, M., Witte, A. V., & Beyer, F. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.27.615357 · Cited by 1 · see citing works
Deary, I. J., Harris, S. E., Russ, T. C., Cox, S. R., & Corley, J. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03274-9
“The structural equation modelling-based approach was consistent with previous work on the Lothian Birth Cohorts [16, 46, 47].”
Self-citationPunctuated memory change - The temporal dynamics and brain basis of memory stability in agingPreprint
Fjell, A. M., Grødem, E., Dídac Vidal-piñeiro, A., Røgeberg, O. J., Sørensen, Ø., Garrido, P. F., Watne, L. O., Lundquist, A., Initiative, T. F. L. D. N., ageing, T. A. I. B. A. L. F. S. O., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9555309/v1
Nagari, S., Begum, Z., Thondapu, K., Udaya Kumar, P., & Janamala, R. P. (2026) · Journal of Clinical Medicine of Kazakhstan · doi:10.23950/jcmk/18165
Reconsidering Brain Age: Why Age-Prediction Models Fail as Measures of Brain AgingPreprint
Grødem, E., Smith, S. M., Dídac Vidal-piñeiro, A., Elliott, M. L., Initiative, F. T. A. D. N., Walhovd, K. B., & Fjell, A. M. (2026) · Research Square · doi:10.21203/rs.3.rs-9608519/v1
Clonal Haematopoiesis Tracks Alzheimer’s-like Cortical Atrophy in Community-Dwelling Older AdultsPreprint
Suwanlikit, Y., Crofts, S., Moodie, J. E., Marioni, R. E., Robertson, N., Tan, S. H., Vivithanaporn, P., Bastin, M. E., Wardlaw, J. M., Kirschner, K., et al. (2026) · medRxiv · doi:10.64898/2026.09.10.26362681
Self-citationDemetriou, I., Correia, M., Dídac Vidal-piñeiro, A., Fisher, S. E., Eising, E., Escott‐Price, V., Bracher‐Smith, M., Apšvalka, D., Attaheri, A., Emery, T., et al. (2026) · Cerebral Cortex · doi:10.1093/cercor/bhag181
- Authors
- Cox, S. R., Harris, M. A., Ritchie, S. J., Buchanan, C. R., Valdés Hernández, M. C., Corley, J., Taylor, A. M., Madole, J. W., Harris, S. E., Whalley, H. C., McIntosh, A. M., Russ, T. C., Bastin, M. E., Wardlaw, J. M., Deary, I. J., & Tucker-Drob, E. M.
- Affiliations
- University of Edinburgh (GB)
- King's College London (GB)
- The University of Texas at Austin (US)
- Alzheimer Scotland (GB)
- UK Dementia Research Institute (GB)
- Corresponding author
- Cox, S. R.
- ORCID
- Venue
- Molecular Psychiatry
- Published
- 2021-01-04
- Volume
- 26
- Issue
- 6
- Pages
- 2651-2662
- ISSN
- 1359-4184 (ISSN-L)
- 1476-5578
- PMC
- PMC8254824
- OpenAlex
- W3120668417
- Cited by
- 60
- References
- 65
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 14 · 2023: 11 · 2022: 10 · 2021: 5 · 2020: 4
- FWCI
- 7.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Molecular Psychiatry (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- University of Texas at Austin (P2CHD042849)
- Wellcome Trust (104036/Z/14/Z, 104036)
- Mrs Gladys Row Fogo Charitable Trust (FID3668413, BRO-D.FID3668413)
- Alzheimer's Society
- British Heart Foundation (104036/Z/14/Z)
- Age UK (MR/M013111/1, G0701120, MR/R024065/1, P2CHD042849, G1001245, LBC1936, R01AG054628)
- Fondation Leducq
- UK Dementia Research Institute
- National Institutes of Health (104036/Z/14/Z, MR/M013111/1, MR/R024065/1)
- Medical Research Council (MC_PC_17209, MR/M013111/1, MR/R024065/1, 104036/Z/14/Z, R01AG054628, MR/M01311/1, MR/K026992/1, P2CHD042849, G0701120, G1001245, LBC1936)
- National Institute on Aging (R01AG054628)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD042849)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Aging and Gerontology Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Keywords
- brain atrophy, cognitive decline, older adults, longitudinal MRI, cortical thickness, visuospatial ability, processing speed, memory
- MeSH terms
- Aged, Aging, Brain, Cerebral Cortex, Cross-Sectional Studies, Humans, Cognitive Dysfunction
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