Successful cognitive aging is associated with thicker anterior cingulate cortex and lower tau deposition compared to typical aging.

Alzheimer's disease PET amyloid biomarkers cortical thickness exceptional cognitive performance successful aging superaging tau

Journal

Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978

Informations de publication

Date de publication:
24 Aug 2023
Historique:
revised: 30 06 2023
received: 12 04 2023
accepted: 01 08 2023
medline: 24 8 2023
pubmed: 24 8 2023
entrez: 24 8 2023
Statut: aheadofprint

Résumé

There is no consensus on either the definition of successful cognitive aging (SA) or the underlying neural mechanisms. We examined the agreement between new and existing definitions using: (1) a novel measure, the cognitive age gap (SA-CAG, cognitive-predicted age minus chronological age), (2) composite scores for episodic memory (SA-EM), (3) non-memory cognition (SA-NM), and (4) the California Verbal Learning Test (SA-CVLT). Fair to moderate strength of agreement was found between the four definitions. Most SA groups showed greater cortical thickness compared to typical aging (TA), especially in the anterior cingulate and midcingulate cortices and medial temporal lobes. Greater hippocampal volume was found in all SA groups except SA-NM. Lower entorhinal These findings suggest that a feature of SA, regardless of its exact definition, is resistance to tau pathology and preserved cortical integrity, especially in the anterior cingulate and midcingulate cortices. Different approaches have been used to define successful cognitive aging (SA). Regardless of definition, different SA groups have similar brain features. SA individuals have greater anterior cingulate thickness and hippocampal volume. Lower entorhinal tau deposition, but not amyloid beta is related to SA. A combination of cortical integrity and resistance to tau may be features of SA.

Identifiants

pubmed: 37614157
doi: 10.1002/alz.13438
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIH HHS
ID : AG067418
Pays : United States
Organisme : Alzheimer's Association
ID : AARF-22-926053
Pays : United States
Organisme : NIH HHS
ID : AG034570
Pays : United States
Organisme : NIA NIH HHS
ID : K01 AG078443
Pays : United States
Organisme : NIA NIH HHS
ID : R03 AG067033
Pays : United States

Informations de copyright

© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.

Références

Nyberg L, Pudas S. Successful memory aging. Annu Rev Psychol. 2019;70:219-243. doi:10.1146/annurev-psych-010418-103052
de Godoy LL, Alves CAPF, Saavedra JSM, et al. Understanding brain resilience in superagers: a systematic review. Neuroradiology. 2020;63:663-683. doi:10.1007/s00234-020-02562-1
Borelli WV, Schilling LP, Radaelli G, et al. Neurobiological findings associated with high cognitive performance in older adults: a systematic review. Int Psychogeriatr. 2018;30:1813-1825. doi:10.1017/S1041610218000431
Harrison TM, Weintraub S, Mesulam MM, Rogalski E. Superior memory and higher cortical volumes in unusually successful cognitive aging. J Int Neuropsychol Soc. 2012;18:1081-1085. doi:10.1017/S1355617712000847
Cook AH, Sridhar J, Ohm D, et al. Rates of cortical atrophy in adults 80 years and older with superior vs average episodic memory. JAMA-J Am Med Assoc. 2017;317:1373-1375. doi:10.1001/jama.2017.0627
Gefen T, Kawles A, Makowski-Woidan B, et al. Paucity of entorhinal cortex pathology of the Alzheimer's type in superagers with superior memory performance. Cereb Cortex. 2021;31:3177-3183. doi:10.1093/cercor/bhaa409
Cook Maher A, Makowski-Woidan B, Kuang A, et al. Neuropsychological profiles of older adults with superior versus average episodic memory: the northwestern “SuperAger” cohort. J Int Neuropsychol Soc JINS. 2022;28:563-573. doi:10.1017/S1355617721000837
Dekhtyar M, Papp KV, Buckley R, et al. Neuroimaging markers associated with maintenance of optimal memory performance in late-life. Neuropsychologia. 2017;100:164-170. doi:10.1016/j.neuropsychologia.2017.04.037
Mapstone M, Lin F, Nalls MA, et al. What success can teach us about failure: the plasma metabolome of older adults with superior memory and lessons for Alzheimer's disease. Neurobiol Aging. 2017;51:148-155. doi:10.1016/j.neurobiolaging.2016.11.007
Lin F, Ren P, Mapstone M, Meyers SP, Porsteinsson A, Baran TM. The cingulate cortex of older adults with excellent memory capacity. Cortex. 2017;86:83-92. doi:10.1016/j.cortex.2016.11.009
Baran TM, Lin FV. Amyloid and FDG PET of successful cognitive aging: global and cingulate-specific differences. J Alzheimers Dis JAD. 2018;66:307-318. doi:10.3233/JAD-180360
Wang X, Ren P, Baran TM, et al. Longitudinal functional brain mapping in supernormals. Cereb Cortex. 2019;29:242-252. doi:10.1093/cercor/bhx322
Chen Q, Baran TM, Rooks B, et al. Cognitively supernormal older adults maintain a unique structural connectome that is resistant to Alzheimer's pathology. NeuroImage Clin. 2020;28:102413. doi:10.1016/j.nicl.2020.102413
Harrison TM, Maass A, Baker SL, Jagust WJ. Brain morphology, cognition, and β-amyloid in older adults with superior memory performance. Neurobiol Aging. 2018;67:162-170. doi:10.1016/j.neurobiolaging.2018.03.024
Cabeza R, Anderson ND, Locantore JK, McIntosh AR. Aging gracefully: compensatory brain activity in high-performing older adults. NeuroImage. 2002;17:1394-1402. doi:10.1006/nimg.2002.1280
Fjell AM, Walhovd KB, Reinvang I, et al. Selective increase of cortical thickness in high-performing elderly-structural indices of optimal cognitive aging. NeuroImage. 2006;29:984-994. doi:10.1016/j.neuroimage.2005.08.007
Gardener SL, Weinborn M, Sohrabi HR, et al. Longitudinal trajectories in cortical thickness and volume atrophy: superior cognitive performance does not protect against brain atrophy in older adults. J Alzheimers Dis. 2021;81:1039-1052. doi:10.3233/JAD-201243
Dang C, Yassi N, Harrington KD, et al. Rates of age- and amyloid β-associated cortical atrophy in older adults with superior memory performance. Alzheimers Dement Diagn Assess Dis Monit. 2019;11:566-575. doi:10.1016/j.dadm.2019.05.005
Dang C, Harrington KD, Lim YY, et al. Superior memory reduces 8-year risk of mild cognitive impairment and dementia but not amyloid β-associated cognitive decline in older adults. Arch Clin Neuropsychol. 2019;34:585-598. doi:10.1093/arclin/acy078
Borelli WV, Leal-Conceição E, Andrade MA, et al. Increased glucose activity in subgenual anterior cingulate and hippocampus of high performing older adults, despite amyloid burden. J Alzheimers Dis. 2021;81:1419-1428. doi:10.3233/JAD-210063
Katsumi Y, Wong B, Cavallari M, et al. Structural integrity of the anterior mid-cingulate cortex contributes to resilience to delirium in SuperAging. Brain Commun. 2022;4:fcac163. doi:10.1093/braincomms/fcac163
Sun FW, Stepanovic MR, Andreano J, Barrett LF, Touroutoglou A, Dickerson BC. Youthful brains in older adults: preserved neuroanatomy in the default mode and salience networks contributes to youthful memory in superaging. J Neurosci. 2016;36:9659-9668. doi:10.1523/JNEUROSCI.1492-16.2016
Zhang J, Andreano JM, Dickerson BC, Touroutoglou A, Barrett LF. Stronger functional connectivity in the default mode and salience networks is associated with youthful memory in superaging. Cereb Cortex. 2020;30:72-84. doi:10.1093/CERCOR/BHZ071
Katsumi Y, Andreano JM, Barrett LF, et al. Greater neural differentiation in the ventral visual cortex is associated with youthful memory in superaging. Cereb Cortex. 2021;31:5275-5287. doi:10.1093/cercor/bhab157
Gefen T, Peterson M, Papastefan ST, et al. Morphometric and histologic substrates of cingulate integrity in elders with exceptional memory capacity. J Neurosci. 2015;35:1781-1791. doi:10.1523/JNEUROSCI.2998-14.2015
Franke K, Gaser C. Ten years of brainage as a neuroimaging biomarker of brain aging: what insights have we gained? Front Neurol. 2019;10:789. doi:10.3389/fneur.2019.00789
Anatürk M, Kaufmann T, Cole JH, et al. Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging. Hum Brain Mapp. 2021;42:1626-1640. doi:10.1002/hbm.25316
Arenaza-Urquijo EM, Vemuri P. Resistance vs resilience to Alzheimer disease: clarifying terminology for preclinical studies. Neurology. 2018;90:695-703. doi:10.1212/wnl.0000000000005303
Dobyns L, Zhuang K, Baker SL, Mungas D, Jagust WJ, Harrison TM. An empirical measure of resilience explains individual differences in the effect of tau pathology on memory change in aging. Nat Aging. 2023;3:229-237. doi:10.1038/s43587-022-00353-2
Abdi H, Williams LJ. Partial least squares methods: partial least squares correlation and partial least square regression. In: Reisfeld B, Mayeno AN, eds. Comput. Toxicol.. Humana Press; 2013:549-579. doi:10.1007/978-1-62703-059-5_23
Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage. 2011;56:455-475. doi:10.1016/j.neuroimage.2010.07.034
McIntosh AR, Lobaugh NJ. Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage. 2004;23:S250-63. doi:10.1016/j.neuroimage.2004.07.020
de Lange AMG, Cole JH. Commentary: correction procedures in brain-age prediction. NeuroImage Clin. 2020;26:102229. doi:10.1016/j.nicl.2020.102229
de Lange A-MG, Anatürk M, Rokicki J, et al. Mind the gap: performance metric evaluation in brain-age prediction. Hum Brain Mapp. 2022;43:3113-3129. doi:10.1002/hbm.25837
Smith SM, Vidaurre D, Alfaro-Almagro F, Nichols TE, Miller KL. Estimation of brain age delta from brain imaging. NeuroImage. 2019;200:528-539. doi:10.1016/j.neuroimage.2019.06.017
Beheshti I, Nugent S, Potvin O, Duchesne S. Bias-adjustment in neuroimaging-based brain age frameworks: a robust scheme. NeuroImage Clin. 2019;24:102063. doi:10.1016/j.nicl.2019.102063
Rogalski EJ. Don't forget-age is a relevant variable in defining SuperAgers. Alzheimers Dement Diagn Assess Dis Monit. 2019;11:560. doi:10.1016/J.DADM.2019.05.008
Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci. 2000;97:11050-11055. doi:10.1073/pnas.200033797
Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31:968-980. doi:10.1016/j.neuroimage.2006.01.021
Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage. 1999;9:179-194. doi:10.1006/nimg.1998.0395
Fischl B, Salat DH, Busa E, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341-355. doi:10.1016/s0896-6273(02)00569-x
Villeneuve S, Rabinovici GD, Cohn-Sheehy BI, et al. Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation. Brain. 2015;138:2020-2033. doi:10.1093/brain/awv112
Ossenkoppele R, Schonhaut DR, Schöll M, et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. Brain J Neurol. 2016;139:1551-1567. doi:10.1093/brain/aww027
Schöll M, Lockhart SN, Schonhaut DR, et al. PET imaging of tau deposition in the aging human brain. Neuron. 2016;89:971-982. doi:10.1016/j.neuron.2016.01.028
Logan J, Fowler JS, Volkow ND, Wang G-J, Ding Y-S, Alexoff DL. Distribution volume ratios without blood sampling from graphical analysis of PET data. J Cereb Blood Flow Metab. 1996;16:834-840. doi:10.1097/00004647-199609000-00008
Price JC, Klunk WE, Lopresti BJ, et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab Off J Int Soc Cereb Blood Flow Metab. 2005;25:1528-1547. doi:10.1038/sj.jcbfm.9600146
Mormino EC, Smiljic A, Hayenga AO, et al. Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cereb Cortex. 2011;21:2399-2407. doi:10.1093/cercor/bhr025
Giorgio J, Jagust WJ, Baker S, Landau SM, Tino P, Kourtzi Z. A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation. Nat Commun. 2022;13:1887. doi:10.1038/s41467-022-28795-7
Baker SL, Lockhart SN, Price JC, et al. Reference tissue-based kinetic evaluation of 18F-AV-1451 for tau imaging. J Nucl Med Off Publ Soc Nucl Med. 2017;58:332-338. doi:10.2967/jnumed.116.175273
Rousset OG, Ma Y, Evans AC. Correction for partial volume effects in PET: principle and validation. J Nucl Med. 1998;39:904-911.
Baker SL, Maass A, Jagust WJ. Considerations and code for partial volume correcting [18F]-AV-1451 tau PET data. Data Brief. 2017;15:648-657. doi:10.1016/j.dib.2017.10.024
Braak H, Braak E. The human entorhinal cortex: normal morphology and lamina-specific pathology in various diseases. Neurosci Res. 1992;15:6-31. doi:10.1016/0168-0102(92)90014-4
Braak H, Braak E. Staging of Alzheimer's disease-related neurofibrillary changes. Neurobiol Aging. 1995;16:271-278. doi:10.1016/0197-4580(95)00021-6
Chen X, Cassady KE, Adams JN, Harrison TM, Baker SL, Jagust WJ. Regional tau effects on prospective cognitive change in cognitively normal older adults. J Neurosci. 2021;41:366-375. doi:10.1523/JNEUROSCI.2111-20.2020
Maass A, Berron D, Harrison TM, et al. Alzheimer's pathology targets distinct memory networks in the ageing brain. Brain J Neurol. 2019;142:2492-2509. doi:10.1093/brain/awz154
Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage. 2002;15:870-878. doi:10.1006/nimg.2001.1037
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159-174.
Yamazaki Y, Zhao N, Caulfield TR, Liu C-C, Bu G. Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat Rev Neurol. 2019;15:501-518. doi:10.1038/s41582-019-0228-7
Suri S, Heise V, Trachtenberg AJ, Mackay CE. The forgotten APOE allele: a review of the evidence and suggested mechanisms for the protective effect of APOE ɛ2. Neurosci Biobehav Rev. 2013;37:2878-2886. doi:10.1016/j.neubiorev.2013.10.010
Munro CA, Winicki JM, Schretlen DJ, et al. Sex differences in cognition in healthy elderly individuals. Aging Neuropsychol Cogn. 2012;19:759-768. doi:10.1080/13825585.2012.690366
Seeley WW, Carlin DA, Allman JM, et al. Early frontotemporal dementia targets neurons unique to apes and humans. Ann Neurol. 2006;60:660-667. doi:10.1002/ana.21055
Allman JM, Tetreault NA, Hakeem AY, et al. The von Economo neurons in frontoinsular and anterior cingulate cortex in great apes and humans. Brain Struct Funct. 2010;214:495-517. doi:10.1007/s00429-010-0254-0
Butti C, Santos M, Uppal N, Hof PR. Von Economo neurons: clinical and evolutionary perspectives. Cortex J Devoted Study Nerv Syst Behav. 2013;49:312-326. doi:10.1016/j.cortex.2011.10.004
Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27:2349-2356. doi:10.1523/JNEUROSCI.5587-06.2007
Gefen T, Papastefan ST, Rezvanian A, et al. Von Economo neurons of the anterior cingulate across the lifespan and in Alzheimer's disease. Cortex. 2018;99:69-77. doi:10.1016/j.cortex.2017.10.015
Arenaza-Urquijo EM, Przybelski SA, Lesnick TL, et al. The metabolic brain signature of cognitive resilience in the 80+: beyond Alzheimer pathologies. Brain. 2019;142:1134-1147. doi:10.1093/brain/awz037
Riudavets MA, Iacono D, Resnick SM, et al. Resistance to Alzheimer's pathology is associated with nuclear hypertrophy in neurons. Neurobiol Aging. 2007;28:1484-1492. doi:10.1016/j.neurobiolaging.2007.05.005
Iacono D, O'Brien R, Resnick SM, et al. Neuronal hypertrophy in asymptomatic Alzheimer disease. J Neuropathol Exp Neurol. 2008;67:578-589. doi:10.1097/NEN.0b013e3181772794
Driscoll I, Troncoso J. Asymptomatic Alzheimer's disease: a prodrome or a state of resilience? Curr Alzheimer Res. 2011;8:330-335. doi:10.2174/156720511795745348
Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, et al. Whitepaper: defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dement. 2020;16:1305-1311. doi:10.1016/J.JALZ.2018.07.219
Harrison TM, La Joie R, Maass A, et al. Longitudinal tau accumulation and atrophy in aging and Alzheimer disease. Ann Neurol. 2019;85:229-240. doi:10.1002/ana.25406
Knopman DS, Parisi JE, Salviati A, et al. Neuropathology of cognitively normal elderly. J Neuropathol Exp Neurol. 2003;62:1087-1095. doi:10.1093/jnen/62.11.1087
Jack CR, Knopman DS, Jagust WJ, et al. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9:119-128. doi:10.1016/S1474-4422(09)70299-6
Maass A, Lockhart SN, Harrison TM, et al. Entorhinal tau pathology, episodic memory decline, and neurodegeneration in aging. J Neurosci. 2018;38:530-543. doi:10.1523/JNEUROSCI.2028-17.2017
Scott MR, Hampton OL, Buckley RF, et al. Inferior temporal tau is associated with accelerated prospective cortical thinning in clinically normal older adults. NeuroImage. 2020;220:116991. doi:10.1016/j.neuroimage.2020.116991
Hoenig MC, Willscheid N, Bischof GN, van Eimeren T, Drzezga A, Initiative ADN. Assessment of tau tangles and amyloid-β plaques among super agers using PET imaging. JAMA Netw Open. 2020;3:e2028337-e2028337. doi:10.1001/JAMANETWORKOPEN.2020.28337
Braak H, Thal DR, Ghebremedhin E, Del Tredici K. Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J Neuropathol Exp Neurol. 2011;70:960-969. doi:10.1097/NEN.0b013e318232a379
Crary JF, Trojanowski JQ, Schneider JA, et al. Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol (Berl). 2014;128:755-766. doi:10.1007/s00401-014-1349-0
Braak H, Alafuzoff I, Arzberger T, Kretzschmar H, Del Tredici K. Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry. Acta Neuropathol (Berl). 2006;112:389-404. doi:10.1007/s00401-006-0127-z
Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82:239-259.
Jagust W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat Rev Neurosci. 2018;19:687-700. doi:10.1038/s41583-018-0067-3
Krivanek TJ, Gale SA, McFeeley BM, Nicastri CM, Daffner KR. Promoting successful cognitive aging: a ten-year update. J Alzheimers Dis. 2021;81:871-920. doi:10.3233/JAD-201462

Auteurs

Stefania Pezzoli (S)

Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA.
Lawrence Berkeley National Laboratory, Berkeley, California, USA.

Joseph Giorgio (J)

Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA.
University of Newcastle, Newcastle, NSW, Australia.

Adam Martersteck (A)

Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA.

Lindsey Dobyns (L)

Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA.

Theresa M Harrison (TM)

Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA.

William J Jagust (WJ)

Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA.
Lawrence Berkeley National Laboratory, Berkeley, California, USA.

Classifications MeSH