1932

Abstract

On average, most aspects of adult human brains and cognitive functions experience decline with age, yet individuals also differ greatly in how much decline they experience. This review surveys the state of the art in neurocognitive aging research and our progress toward understanding brain and cognitive aging. It covers the empirical evidence that characterizes their respective mean changes with age before highlighting the substantial and important heterogeneity in how severely people experience aging. It then discusses major neurocognitive aging theories and current supporting evidence alongside methodological and conceptual caveats, including those arising from the replication crisis. This review concludes by using a systems biology schema to survey newer areas of research and future opportunities that will serve to narrow the gaps between biological levels of explanation, furthering our understanding of mechanisms, the stratification of risk, and ameliorative strategies.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-devpsych-010923-102441
2024-12-09
2025-06-19
Loading full text...

Full text loading...

/deliver/fulltext/devpsych/6/1/annurev-devpsych-010923-102441.html?itemId=/content/journals/10.1146/annurev-devpsych-010923-102441&mimeType=html&fmt=ahah

Literature Cited

  1. Altman GD. 2006.. The cost of dichotomising continuous variables. . BMJ 332::1080
    [Crossref] [Google Scholar]
  2. Bartzokis G. 2004.. Age-related myelin breakdown: a developmental model of cognitive decline and Alzhiemer's disease. . Neurobiol. Aging 25::518
    [Crossref] [Google Scholar]
  3. Bastin ME, Clayden JD, Pattie A, Gerrish IF, Wardlaw JM, et al. 2009.. Diffusion tensor and magnetization transfer MRI measurements of periventricular white matter hyperintensities in old age. . Neurobiol. Aging 30::12536
    [Crossref] [Google Scholar]
  4. Beck D, de Lange AMG, Maximov II, Richard G, Andreasson OA, et al. 2021.. White matter microstructure across the adult lifespan: a mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. . NeuroImage 224::117441
    [Crossref] [Google Scholar]
  5. Bennet DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, et al. 2018.. Religious Orders Study and Rush Memory and Aging Project. . J. Alzheimer's Dis. 64::S16189
    [Crossref] [Google Scholar]
  6. Bennett IJ, Madden DJ, Vaidya CJ, Howard DV, Howard JH. 2010.. Age-related differences in multiple measures of white matter integrity: a diffusion tensor imaging study of healthy aging. . Hum. Brain Mapp. 31::37890
    [Crossref] [Google Scholar]
  7. Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, et al. 2022.. Brain charts for the human lifespan. . Nature 604::52533
    [Crossref] [Google Scholar]
  8. Blinkouskaya Y, Weickenmeier J. 2021.. Brain shape changes associated with cerebral atrophy in healthy aging and Alzheimer's disease. Front. . Mech. Eng. 7::7056530
    [Google Scholar]
  9. Braitenberg V, Schüz A. 2013.. Anatomy of the Cortex: Statistics and Geometry. Berlin:: Springer
    [Google Scholar]
  10. Brickman AM, Meier IB, Korgaonkar MS, Provenzano FA, Grieve SM, et al. 2012.. Testing the white matter retrogenesis hypothesis of cognitive aging. . Neurobiol. Aging 33::1699715
    [Crossref] [Google Scholar]
  11. Brouwer RM, Klein M, Grasby KL, Schnack HG, Jahanashad N, et al. 2022.. Genetic variants associated with longitudinal changes in brain structure across the lifespan. . Nat. Neurosci. 25::42132
    [Crossref] [Google Scholar]
  12. Buckner RL. 2004.. Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate. . Neuron 44::195208
    [Crossref] [Google Scholar]
  13. Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flink J, et al. 2013.. Power failure: why small sample size undermines the reliability of neuroscience. . Nat. Rev. Neurosci. 14::36576
    [Crossref] [Google Scholar]
  14. Cabeza R. 2002.. Hemispheric asymmetry reduction in older adults: the HAROLD model. . Psychol. Aging 17::85100
    [Crossref] [Google Scholar]
  15. Cabeza R, Dennis NA. 2012.. Frontal lobes and aging: deterioration and compensation. . In Principles of Frontal Lobe Function, ed. DT Stuss, RT Knight , pp. 62852. New York:: Oxford Univ. Press. , 2nd ed..
    [Google Scholar]
  16. Carroll JB. 1993.. Human Cognitive Abilities. New York:: Cambridge Univ. Press
    [Google Scholar]
  17. Cattell RB. 1971.. Abilities: Their Structure, Growth, and Action. Boston:: Houghton Mifflin
    [Google Scholar]
  18. Cole JH, Ritchie SJ, Bastin ME, Valdés Hernández MC, Muñoz Maniega S, et al. 2018.. Brain age predicts mortality. . Mol. Psychiatry 23::138592
    [Crossref] [Google Scholar]
  19. Conole ELS, Stevenson AJ, Muñoz Maniega S, Harris SE, Green C, et al. 2021.. DNA methylation and protein markers of inflammation and their associations with brain and cognitive aging. . Neurology 97::e234052
    [Crossref] [Google Scholar]
  20. Corley J, Conte F, Harris SE, Taylor AM, Redmond P, et al. 2023.. Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936. . Mol. Psychiatry 28::125671
    [Crossref] [Google Scholar]
  21. Cox SR, Bastin ME, Ferguson KJ, Allerhand M, Royle NA, et al. 2015.. Compensation or inhibitory failure? Testing hypotheses of age-related right frontal lobe involvement in verbal memory ability using structural and diffusion MRI. . Cortex 63::415
    [Crossref] [Google Scholar]
  22. Cox SR, Deary IJ. 2022.. Brain and cognitive ageing: the present, and some predictions (…about the future). . Aging Brain 2::100032
    [Crossref] [Google Scholar]
  23. Cox SR, Harris MA, Ritchie SJ, Buchanan CR, Valdés Hernández MC, et al. 2021.. Three major dimensions of human brain cortical ageing in relation to cognitive decline across the eighth decade of life. . Mol. Psychiatry 26::265162
    [Crossref] [Google Scholar]
  24. Cox SR, Ritchie SJ, Fawns-Ritchie C, Tucker-Drob EM, Deary IJ. 2019.. Structural brain imaging correlates of general intelligence in UK Biobank. . Intelligence 76::101376
    [Crossref] [Google Scholar]
  25. Cox SR, Ritchie SJ, Tucker-Drob EM, Liewald DC, Hagenaars SP, et al. 2016.. Ageing and brain white matter structure in 3,513 UK Biobank participants. . Nat. Commun. 7::12629
    [Crossref] [Google Scholar]
  26. Craik FIM, Rose NS. 2012.. Memory encoding and aging: a neurocognitive perspective. . Neurosci. Biobehav. Rev. 36::172939
    [Crossref] [Google Scholar]
  27. de Chastelaine M, Wang TH, Minton B, Muftuler LT, Rugg MD. 2011.. The effects of age, memory performance, and callosal integrity on the neural correlates of successful associative encoding. . Cereb. Cortex 21::216676
    [Crossref] [Google Scholar]
  28. de Lange A-MG, Anaturk M, Suri S, Kaufmann T, Cole JH et al. 2020.. Multimodal brain-age prediction and cardiovascular risk: the Whitehall II MRI sub-study. . NeuroImage 222::117292
    [Crossref] [Google Scholar]
  29. de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LM, et al. 2001.. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. . J. Neurol. Neurosurg. Psychiatry 70::914
    [Crossref] [Google Scholar]
  30. Davis SW, Dennis NA, Daselaar SM, Fleck MS, Cabeza R. 2008.. Qué PASA? The posterior–anterior shift in aging. . Cereb. Cortex 18::12019
    [Crossref] [Google Scholar]
  31. Deary IJ, Batty GD. 2007.. Cognitive epidemiology. . J. Epidemiol. Commun. Health 61::37884
    [Crossref] [Google Scholar]
  32. Deary IJ, Corley J, Gow AJ, Harris SE, Houlihan LM, et al. 2009.. Age-associated cognitive decline. . Br. Med. Bull. 92::13552
    [Crossref] [Google Scholar]
  33. Deary IJ, Cox SR, Hill WD. 2022.. Genetic variation, brain, and intelligence differences. . Mol. Psychiatry 27::33553
    [Crossref] [Google Scholar]
  34. Deary IJ, Penke L, Johnson W. 2010.. The neuroscience of human intelligence differences. . Nat. Rev. Neurosci. 11::20111
    [Crossref] [Google Scholar]
  35. Demnitz N, Hulme OJ, Siebner HR, Kjaer M, Ebmeier KP, et al. 2023.. Characterising the covariance patter between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts. . Neurobiol. Aging 131::11523
    [Crossref] [Google Scholar]
  36. Di Biase MA, Tian YE, Bethlehem RAI, Seidlitz J, Alexander-Bloch AF, et al. 2023.. Mapping human brain charts cross-sectionally and longitudinally. . PNAS 120::e2216798120
    [Crossref] [Google Scholar]
  37. Dickie DA, Karama S, Ritchie SJ, Cox SR, Sakka E, et al. 2016.. Progression of white matter disease and cortical thinning are not related in older community-dwelling subjects. . Stroke 47::41016
    [Crossref] [Google Scholar]
  38. Dickstein DL, Kabaso D, Rocher AB, Luebke JI, Wearne SL, et al. 2007.. Changes in the structural complexity of the aged brain. . Aging Cell 6::27584
    [Crossref] [Google Scholar]
  39. Douaud G, Groves AR, Tamnes CK, Johansen-Berg H. 2014.. A common brain network links development, aging, and vulnerability to disease. . PNAS 111:(49):1764853
    [Crossref] [Google Scholar]
  40. Duval T, Stikov N, Cohen-Adad J. 2016.. Modeling white matter microstructure. . Funct. Neurol. 31::21728
    [Google Scholar]
  41. Duverne S, Motamedinia S, Rugg MD. 2009.. The relationship between aging, performance, and the neural correlates of successful memory encoding. . Cereb. Cortex 19::73344
    [Crossref] [Google Scholar]
  42. Ebaid D, Crewther SG. 2020.. Time for a systems biological approach to cognitive ageing? A critical review. . Front. Aging Neurosci. 12::114
    [Crossref] [Google Scholar]
  43. Elliott LT, Sharp K, Alfaro-Almagro F, Shi S, Miller KL, et al. 2018.. Genome-wide association studies of brain imaging phenotypes in UK Biobank. . Nature 562::21016
    [Crossref] [Google Scholar]
  44. Fazekas F, Ropele S, Enzinger C, Gorani F, Seewann A, et al. 2005.. MTI of white matter hyperintensities. . Brain 128::292632
    [Crossref] [Google Scholar]
  45. Festini SB, Zahodne L, Reuter-Lorenz PA. 2018.. Theoretical perspectives on age differences in brain activation: HAROLD, PASA, CRUNCH—How do they STAC up?. Oxford Research Encyclopedia of Psychology. https://doi.org/10.1093/acrefore/9780190236557.013.400
    [Google Scholar]
  46. Fisher JE, Guha A, Heller W, Miller GA. 2020.. Extreme-groups designs in studies of dimensional phenomena: advantages, caveats and recommendations. . J. Abnorm. Psychol. 129::1420
    [Crossref] [Google Scholar]
  47. Fjell AM, Walhovd KB. 2010.. Structural brain changes in aging: courses, causes and cognitive consequences. . Rev. Neurosci. 21::187221
    [Crossref] [Google Scholar]
  48. Fjell AM, Walhovd KB, Fennema-Notestine C, McEvoy LK, Hagler DJ, et al. 2009.. One-year brain atrophy evidence in healthy aging. . J. Neurosci. 29::1522331
    [Crossref] [Google Scholar]
  49. Fjell AM, Westlye LT, Grydeland H, Amlien I, Espeseth T, et al. 2013.. Critical ages in the life course of the adult brain: nonlinear subcortical aging. . Neurobiol. Aging 34::223947
    [Crossref] [Google Scholar]
  50. Frangou S, Modabbernia A, Williams SCR, Papachristou E, Doucet GE, et al. 2022.. Cortical thickness across the lifespan: data from 17,075 healthy individuals aged 3–90 years. . Hum. Brain Mapp. 43::43151
    [Crossref] [Google Scholar]
  51. Franke K, Ziegler G, Klöppel S, Gaser C, Alzheimer's Dis. Neuroimaging Initiat. 2010.. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. . NeuroImage 50::88392
    [Crossref] [Google Scholar]
  52. Fujita S, Mori S, Onda K, Hanaoka S, Nomira Y et al. 2023.. Characterization of brain volume changes in aging individuals with normal cognition using serial magnetic resonance imaging. . JAMA Netw. Open 6::e2318153
    [Crossref] [Google Scholar]
  53. Fürtjes AE, Cole JH, Couvy-Duchesne B, Ritchie SJ. 2023.. A quantified comparison of cortical atlases on the basis of trait morphometricity. . Cortex 158::11026
    [Crossref] [Google Scholar]
  54. Glover GH. 2011.. Overview of functional magnetic resonance imaging. . Neurosurg. Clin. N. Am. 22::13339
    [Crossref] [Google Scholar]
  55. Goh JOS. 2011.. Functional dedifferentiation and altered connectivity in older adults: neural accounts of cognitive aging. . Aging Dis. 2::3048
    [Google Scholar]
  56. Grady CL. 2012.. The cognitive neuroscience of ageing. . Nat. Rev. Neurosci. 13::491505
    [Crossref] [Google Scholar]
  57. Ioannidis JPA. 2005.. Why most published research findings are false. . PLOS Med. 2::e124
    [Crossref] [Google Scholar]
  58. Jamadar SD. 2020.. The CRUNCH model does not account for load-dependent changes in visuospatial working memory in older adults. . Neuropsychologia 142::107446
    [Crossref] [Google Scholar]
  59. Jirsaraie RJ, Gorelik AJ, Gatavins MM, Engemann DA, Bogdan R, et al. 2023.. A systematic review of multimodal brain age studies: uncovering divergence between model accuracy and utility. . Patterns 4::100712
    [Crossref] [Google Scholar]
  60. Johansson J, Wahlin A, Lundquist A, Brandmaier AM, Lindenberger U, et al. 2022.. Model of brain maintenance reveals specific change-change association between medial-temporal lobe integrity and episodic memory. . Aging Brain 2022::100027
    [Crossref] [Google Scholar]
  61. Johnson W, Bouchard TJ, Krueger RF, McGue M, Gottesman II. 2004.. Just one g: consistent results from three test batteries. . Intelligence 32::95107. Corrigendum. 2004. Intelligence 32:319
    [Crossref] [Google Scholar]
  62. Johnson W, te Nijenhuis J, Bouchard TJ Jr. 2008.. Still just 1 g: consistent results from five test batteries. . Intelligence 36::8195
    [Crossref] [Google Scholar]
  63. Jones DK, Knösche TR, Turner R. 2013.. White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. . NeuroImage 73::23954
    [Crossref] [Google Scholar]
  64. Karrer TM, Josef AK, Mata R, Morris ED, Samanez-Larkin GR. 2017.. Reduced dopamine receptors and transporters but not synthesis capacity in normal aging adults: a meta-analysis. . Neurobiol. Aging 57::3646
    [Crossref] [Google Scholar]
  65. Karrer TM, McLaughlin CL, Guaglianone CP, Samanez-Larkin GR. 2019.. Reduced serotonin receptors and transporters in normal aging adults: a meta-analysis of PET and SPECT imaging studies. . Neurobiol. Aging 80::110
    [Crossref] [Google Scholar]
  66. Karvelas N, Elahi FM. 2023.. White matter hyperintensities: complex predictor of complex outcomes. . J. Am. Heart Assoc. 12::e030351
    [Crossref] [Google Scholar]
  67. Knights E, Morcom AM, Henson RN. 2021.. Does hemispheric asymmetry reduction in older adults in motor cortex reflect compensation?. J. Neurosci. 41::936173
    [Crossref] [Google Scholar]
  68. Koen JD, Rugg MD. 2019.. Neural dedifferentiation in the aging brain. . Trends Cogn. Sci. 23::54759
    [Crossref] [Google Scholar]
  69. Le Bihan D. 2014.. Diffusion MRI: what water tells us about the brain. . EMBO Mol. Med. 6::56973
    [Crossref] [Google Scholar]
  70. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, et al. 2012.. Diffusion tensor imaging of white matter tract evolution over the lifespan. . NeuroImage 60::34052
    [Crossref] [Google Scholar]
  71. Li HJ, Hou XH, Liu HH, Yue CL, Lu GM, et al. 2015.. Putting age-related task activation into large-scale brain networks: a meta-analysis of 114 fMRI studies on healthy aging. . Neurosci. Biobehav. Rev. 57::15674
    [Crossref] [Google Scholar]
  72. Li SC, Brehmer Y, Shing YL, Werkle-Bergner M, Lindenberger U. 2006.. Neuromodulation of associative and organizational plasticity across the life span: empirical evidence and neurocomputational modelling. . Neurosci. Biobehav. Rev. 30::77590
    [Crossref] [Google Scholar]
  73. Liu S, Abdellaoui A, Verweij KJ, van Wingen GA. 2023.. Replicable brain–phenotype associations require large-scale neuroimaging data. . Nat. Hum. Behav. 7::134456
    [Crossref] [Google Scholar]
  74. Logie RH, Maylor EA. 2009.. An internet study of prospective memory across adulthood. . Psych. Aging 24::76774
    [Crossref] [Google Scholar]
  75. Logothetis NK. 2008.. What we can do and what we cannot do with fMRI. . Nature 453::86978
    [Crossref] [Google Scholar]
  76. Lustig C, Shah P, Seidler R, Reuter-Lorenz PA. 2009.. Aging, training and the brain: a review and future directions. . Neuropsychol. Rev. 19::50422
    [Crossref] [Google Scholar]
  77. Maillard P, Fletcher E, Lockhart SN, Roach AE, Reed B, et al. 2014.. White matter hyperintensities and their penumbra lie along a continuum of injury in the aging brain. . Stroke 45::172126
    [Crossref] [Google Scholar]
  78. Marek S, Tervo-Clemmens B, Clabro FJ, Montez DF, Kay BP, et al. 2022.. Reproducible brain-wide association studies require thousands of individuals. . Nature 603::65460
    [Crossref] [Google Scholar]
  79. Markello RD, Hansen JY, Liu ZQ, Bazinet V, Shafiei G, et al. 2022.. Neuromaps: structural and functional interpretation of brain maps. . Nat. Methods 19::147279
    [Crossref] [Google Scholar]
  80. Markello RD, Misic B. 2021.. Comparing spatial null models for brain maps. . NeuroImage 236::118052
    [Crossref] [Google Scholar]
  81. Masouleh SK, Eickhoff SB, Hoffstaedter F, Genon S, Alzeimer's Dis. Neuroimaging Initiat. 2019.. Empirical examination of the replicability of associations between brain structure and psychological variables. . eLife 8::e43464
    [Crossref] [Google Scholar]
  82. Mathys H, Peng Z, Boi CA, Victor MB, Leary N, et al. 2023.. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer's disease pathology. . Cell 186::436585
    [Crossref] [Google Scholar]
  83. McCartney DL, Hillary RF, Conole ELS, Banos DT, Gadd DA, et al. 2022.. Blood-based epigenome-wide analyses of cognitive abilities. . Genome Biol. 23::26
    [Crossref] [Google Scholar]
  84. McDonough IM, Nolin SA, Visscher KM. 2022.. 25 years of neurocognitive ageing theories: What have we learned?. Front. Aging Neurosci. 14::1002096
    [Crossref] [Google Scholar]
  85. Meltzer CC, Francis PT. 2001.. Brain aging research at the close of the 20th century: from bench to bedside. . Dialogues Clin. Neurosci. 3::16780
    [Crossref] [Google Scholar]
  86. Mendez Colmenares A, Prytherch B, Thomas ML, Burzynska AZ. 2023.. Within-person changes in the aging white matter microstructure and their modifiers: a meta-analysis and systematic review of longitudinal diffusion tensor imaging studies. . Imaging Neurosci. 1::132
    [Crossref] [Google Scholar]
  87. Moaddel R, Ubaida-Mohien C, Tanaka T, Lyashkov A, Basisty N, et al. 2021.. Proteomics in aging research: a roadmap to clinical, translational research. . Aging Cell 20::e13325
    [Crossref] [Google Scholar]
  88. Moodie JE, Harris SE, Harris MA, Buchanan CR, Davies G, et al. 2023.. General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning. . Hum. Brain Mapp. 45::e26641
    [Crossref] [Google Scholar]
  89. Morcom AM, Henson RN. 2018.. Increased prefrontal activity with aging reflects nonspecific neural responses rather than compensation. . J. Neurosci. 38::730313
    [Crossref] [Google Scholar]
  90. Muñoz Maniega S, Valdés Hernández M, Clayden JD, Royle NA, Murray C, et al. 2015.. White matter hyperintensities and normal-appearing white matter integrity in the aging brain. . Neurobiol. Aging 36::90918
    [Crossref] [Google Scholar]
  91. Neisser U, Boodoo G, Bouchard TJ Jr., Wade WA, Brody N, et al. 1996.. Intelligence: knowns and unknowns. . Am. Psychol. 51::77101
    [Crossref] [Google Scholar]
  92. Nyberg L, Andersson M, Lundquist A, Baaré WFC, Bartrés-Faz D, et al. 2023.. Individual differences in brain aging: heterogeneity in cortico-hippocampal but not caudate atrophy rates. . Cereb. Cortex 33::507581
    [Crossref] [Google Scholar]
  93. Nyberg L, Borazbekk C-J, Sörman DE, Hansson P, Herlitz A, et al. 2020.. Biological and environmental predictors of heterogeneity in neurocognitive ageing: evidence from Betula and other longitudinal studies. . Ageing Res. Rev. 64::101184
    [Crossref] [Google Scholar]
  94. Nyberg L, Lindenberger U. 2020.. Brain maintenance and cognition in old age. . In Cognitive Neuroscience, ed. D Poeppel, G Mangun, M Gazzaniga , pp. 8189. Cambridge, MA:: MIT Press
    [Google Scholar]
  95. Nyberg L, Lövden M, Riklund K, Lindenberger U, Bäckman L. 2012.. Memory aging and brain maintenance. . Trends Cogn. Sci. 16::292305
    [Crossref] [Google Scholar]
  96. Nyberg L, Wåhlin A. 2020.. Imaging: the many facets of brain aging. . eLife 9::e56640
    [Crossref] [Google Scholar]
  97. Papenberg G, Lindenberger U, Bäck L. 2016.. Genetics and the cognitive neuroscience of aging. . In Cognitive Neuroscience of Aging: Linking Cognitive and Cerebral Aging, ed. R Cabeza, L Nyberg, DC Park , pp. 41538. New York:: Oxford Acad.
    [Google Scholar]
  98. Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, et al. 2021.. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. . eLife 10::e70119
    [Crossref] [Google Scholar]
  99. Park DC, Polk TA, Park R, Minear M, Savage A, et al. 2004.. Aging reduces neural specialization in ventral visual cortex. . PNAS 101::1309195
    [Crossref] [Google Scholar]
  100. Park DC, Reuter-Lorenz P. 2009.. The adaptive brain: aging and neurocognitive scaffolding. . Annu. Rev. Psychol. 60::17396
    [Crossref] [Google Scholar]
  101. Pereira GA, Nunes MVS, Alzola P, Contador I. 2021.. Cognitive reserve and brain maintenance in aging and dementia: an integrative review. . Appl. Neuropsychol. Adult 6::161525
    [Google Scholar]
  102. Plomin R, Deary IJ. 2015.. Genetics and intelligence differences: five special findings. . Mol. Psychiatry 20::98108
    [Crossref] [Google Scholar]
  103. Poldrack RA, Baker CI, Durnez J, Gorgolewski KJ, Matthews PM, et al. 2017.. Scanning the horizon: towards transparent and reproducible neuroimaging research. . Nat. Rev. Neurosci. 18::11526
    [Crossref] [Google Scholar]
  104. Preacher KJ. 2015.. Extreme group designs. . In The Encyclopedia of Clinical Psychology, ed. RL Cautin, SO Lilienfeld , pp. 118992. Hoboken, NJ:: Wiley
    [Google Scholar]
  105. Rabbitt P. 1993.. Does it all go together when it goes? The nineteenth Bartlett Memorial Lecture. . Q. J. Exp. Psychol. 46::385434
    [Crossref] [Google Scholar]
  106. Raz N. 2004.. The aging brain observed in vivo: differential changes and their modifiers. . In Cognitive Neuroscience of Aging: Linking Cognitive and Cerebral Aging, ed. R Cabeza, L Nyberg, DC Park , pp. 1755. New York:: Oxford Univ. Press
    [Google Scholar]
  107. Raz N, Daugherty AM. 2018.. Pathways to brain aging and their modifiers: Free-Radical Induced Energetic and Neural Decline in Senescence (FRIENDS) model. . Gerontology 64::4957
    [Crossref] [Google Scholar]
  108. Raz N, Lindenberger U. 2011.. Only time will tell: cross-sectional studies offer no solution to the age–brain–cognition triangle: comment on Salthouse 2011. . Psychol. Bull. 137::79095
    [Crossref] [Google Scholar]
  109. Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D, et al. 2005.. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. . Cereb. Cortex 15::167689
    [Crossref] [Google Scholar]
  110. Raz N, Rodrigue KM. 2006.. Differential aging of the brain: patterns, cognitive correlates and modifiers. . Neurosci. Biobehav. Rev. 30::73048
    [Crossref] [Google Scholar]
  111. Reuter-Lorenz PA, Festini SB, Jantz TK. 2016.. Executive function and neurocognitive aging. . In Handbook of the Psychology of Aging, ed. KW Schaie, SL Willis , pp. 24562. San Diego:: Academic
    [Google Scholar]
  112. Reuter-Lorenz PA, Park DC. 2014.. How does it STAC up? Revisiting the scaffolding theory of aging and cognition. . Neuropsychol. Rev. 24::35570
    [Crossref] [Google Scholar]
  113. Ritchie SJ, Booth T, Valdés Hernández MC, Corley J, Muñoz Maniega S, et al. 2015.. Beyond a bigger brain: multivariable structural brain imaging and intelligence. . Intelligence 51::4756
    [Crossref] [Google Scholar]
  114. Ritchie SJ, Tucker-Drob EM, Cox SR, Dickie DA, Valdés Hernández MC, et al. 2017.. Risk and protective factors for structural brain ageing in the eighth decade of life. . Brain Struct. Funct. 222::347790
    [Crossref] [Google Scholar]
  115. Roseborough AD, Saad L, Goodman M, Cipriano LE, Hachinski VC, et al. 2023.. White matter hyperintensities and longitudinal cognitive decline in cognitively normal populations and across diagnostic categories: a meta-analysis, systematic review, and recommendations for future study harmonization. . Alzheimer's Dement. 19::194207
    [Crossref] [Google Scholar]
  116. Salthouse TA. 1994.. How many causes are there of aging-related decrements in cognitive functioning?. Dev. Rev. 14::41337
    [Crossref] [Google Scholar]
  117. Salthouse TA. 2011.. Neuroanatomical substrates of age-related cognitive decline. . Psychol. Bull. 137::75384
    [Crossref] [Google Scholar]
  118. Salthouse TA. 2019.. Trajectories of normal cognitive aging. . Psychol. Aging 34::1724
    [Crossref] [Google Scholar]
  119. Seaman KL, Smith CT, Juarez EJ, Dang LC, Castrellon JJ, et al. 2019.. Differential regional decline in dopamine receptor availability across adulthood: linear and nonlinear effects of age. . Hum. Brain Mapp. 40::312538
    [Crossref] [Google Scholar]
  120. Scahill RI, Frost C, Jenkins R, Whitwell JL, Rossor MN, et al. 2003.. A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. . Arch. Neurol. 60::98994
    [Crossref] [Google Scholar]
  121. Schaie KW. 2005.. What can we learn from longitudinal studies of adult development?. Res. Hum. Dev. 2::13358
    [Crossref] [Google Scholar]
  122. Schönbrodt FD, Perugini M. 2013.. At what sample size do correlations stabilize?. J. Res. Personal. 47::60912
    [Crossref] [Google Scholar]
  123. Schilling KG, Chad JA, Chamberland M, Nozais V, Rheault F, et al. 2023.. White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan. . Imaging Neurosci. 1::124
    [Crossref] [Google Scholar]
  124. Schliebs R, Arendt T. 2011.. The cholinergic system in aging and neuronal degeneration. . Behav. Brain Res. 221::55563
    [Crossref] [Google Scholar]
  125. Sele S, Liem F, Mérillat S, Janke L. 2020.. Decline variability of cortical and subcortical regions in aging: a longitudinal study. . Front. Hum. Neurosci. 14::363
    [Crossref] [Google Scholar]
  126. Shafer AT, Williams OA, Perez E, An Y, Landman BA, et al. 2022.. Accelerated decline in white matter microstructure in subsequently impaired older adults and its relationship with cognitive decline. . Brain Commun. 4::fcac051
    [Crossref] [Google Scholar]
  127. Simmons JP, Nelson LD, Simonsohn U. 2011.. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. . Psychol. Sci. 22::135966
    [Crossref] [Google Scholar]
  128. Smith SM, Elliott LT, Alfaro-Almagro F, McCarthy P, Nichols TE, et al. 2020.. Brain aging comprises many modes of structural and function change with distinct genetic and biophysical associations. . eLife 9::e52677
    [Crossref] [Google Scholar]
  129. Spearman C. 1904.. General intelligence, objectively determined and measured. . Am. J. Psychol. 15::20193
    [Crossref] [Google Scholar]
  130. Spisak T, Bingel U, Wager TD. 2023.. Multivariate BWAS can be replicable with moderate sample sizes. . Nature 615::E47
    [Crossref] [Google Scholar]
  131. Spreng RN, Wojtowicz M, Grady CL. 2010.. Reliable differences in brain activity between young and old adults: a quantitative meta-analysis across multiple cognitive domains. . Neurosci. Biobehav. Rev. 34:(8):117894
    [Crossref] [Google Scholar]
  132. Stern Y. 2002.. What is cognitive reserve? Theory and research application of the reserve concept. . J. Int. Neuropsychol. Soc. 8::44860
    [Crossref] [Google Scholar]
  133. Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, Belleville S, Cantilon M, et al. 2020.. Whitepaper: defining and investigating cognitive reserve, brain reserve, and brain maintenance. . Alzheimer's Dement. 16::130511
    [Crossref] [Google Scholar]
  134. Svennerholm L, Boström K, Jungbjer B. 1997.. Changes in weight and compositions of major membrane components of human brain during the span of adult human life of Swedes. . Acta Neuropathologica 94::34552
    [Crossref] [Google Scholar]
  135. Szucs D, Ioannidis JP. 2020.. Sample size evolution in neuroimaging research: an evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals. . NeuroImage 221::117164
    [Crossref] [Google Scholar]
  136. Taylor AM, Pattie A, Deary IJ. 2018.. Cohort profile update: the Lothian Birth Cohorts of 1921 and 1936. . Int. J. Epidemiol. 47::10421042r
    [Crossref] [Google Scholar]
  137. Tsvetanov KA, Henson RNA, Rowe JB. 2021.. Separating vascular and neuronal effects of age on fMRI BOLD signals. . Phil. Trans. R. Soc. B 376::20190631
    [Crossref] [Google Scholar]
  138. Tucker-Drob ET. 2019.. Cognitive aging and dementia: a life-span perspective. . Annu. Rev. Dev. Psychol. 1::17796
    [Crossref] [Google Scholar]
  139. Tucker-Drob EM, Brandmaier A, Lindenberger U. 2019.. Coupled cognitive changes in adulthood: a meta-analysis. . Psychol. Bull. 145::273301
    [Crossref] [Google Scholar]
  140. von Bartheld CS. 2018.. Myths and truths about the cellular composition of the human brain: a review of influential concepts. J. Chem. Neuroanat. 93::215
    [Crossref] [Google Scholar]
  141. Walhovd KB, Lövden M, Fjell AM. 2023.. Timing of lifespan influences on brain and cognition. . Trends Cogn. Sci. 27::90115
    [Crossref] [Google Scholar]
  142. Walton E, Baltramonaityte V, Calhoun V, Heijmans BT, Thompson PM, et al. 2023.. A systematic review of neuroimaging epigenetic research: calling for an increased focus on development. . Mol. Psychiatry 28::283947
    [Crossref] [Google Scholar]
  143. Wardlaw JM, Valdés Hernández MC, Muñoz Maniega S. 2015.. What are white matter hyperintensities made of?. J. Am. Heart Assoc. 4::e001140
    [Crossref] [Google Scholar]
  144. Wheater ENW, Stoye DQ, Cox SR, Wardlaw JM, Drake AJ, et al. 2020.. DNA methylation and brain structure and function across the life course: a systematic review. . Neurosci. Biobehav. Rev. 113::13356
    [Crossref] [Google Scholar]
  145. Wilson RS, Yang J, Yu L, Leurgans SE, Capuano AW, et al. 2019.. Postmortem neurodegenerative markers and trajectories of decline in cognitive systems. . Neurology 92::e83141
    [Crossref] [Google Scholar]
  146. Zhao L, Matloff W, Ning K, Kim H, Dinov ID, et al. 2019.. Age-related differences in brain morphology and the modifiers in middle-aged and older adults. . Cereb. Cortex 29::416993
    [Crossref] [Google Scholar]
/content/journals/10.1146/annurev-devpsych-010923-102441
Loading
/content/journals/10.1146/annurev-devpsych-010923-102441
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error