Generic placeholder image

Current Alzheimer Research

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Research Article

Distribution of Cortical Atrophy Associated with Cognitive Decline in Alzheimer’s Disease: A Cross-Sectional Quantitative Structural MRI Study from PUMCH Dementia Cohort

Author(s): Chenhui Mao, Bo Hou, Jie Li, Shanshan Chu, Xinying Huang, Jie Wang, Liling Dong, Caiyan Liu, Feng Feng*, Bin Peng and Jing Gao*

Volume 19, Issue 8, 2022

Published on: 30 September, 2022

Page: [618 - 627] Pages: 10

DOI: 10.2174/1567205019666220905145756

Price: $65

Abstract

Background: Quantitative measures of atrophy on structural MRI are sensitive to the neurodegeneration that occurs in AD, and the topographical pattern of atrophy could serve as a sensitive and specific biomarker.

Objective: We aimed to examine the distribution of cortical atrophy associated with cognitive decline and disease stage based on quantitative structural MRI analysis in a Chinese cohort to inform clinical diagnosis and follow-up of AD patients.

Methods: One hundred and eleven patients who were clinically diagnosed with probable AD were enrolled. All patients completed a systemic cognitive evaluation and domain-specific batteries. The severity of cognitive decline was defined by MMSE score: 1-10 severe, 11-20 moderate, and 21-30 mild. Cortical volume and thickness determined using 3D-T1 MRI data were analyzed using voxelbased morphometry and surface-based analysis supported by the DR. Brain Platform.

Results: The male:female ratio was 38:73. The average age was 70.8 ± 10.6 years. The mild: moderate: severe ratio was 48:38:25. Total grey matter volume was significantly related to cognition while the relationship between white matter volume and cognition did not reach statistical significance. The volume of the temporal-parietal-occipital cortex was most strongly associated with cognitive decline in group analysis, while the hippocampus and entorhinal area had a less significant association with cognitive decline. Volume of subcortical grey matter was also associated with cognition. Volume and thickness of temporoparietal cortexes were significantly correlated with the cognitive decline, with a left predominance observed.

Conclusion: Cognitive deterioration was associated with cortical atrophy. Volume and thickness of the left temporal-parietal-occipital cortex were most important in early diagnosis and longitudinal evaluation of AD in clinical practice. Cognitively relevant cortices were left predominant.

Keywords: Alzheimer’s disease, Cortical thickness, Cortical volume, Cognitive decline, Structure MRI, Quantitative

« Previous
[1]
Blennow K, Leon MJ, Zetterberg H. Alzheimer’s disease. Lancet 2006; 368(9533): 387-403.
[http://dx.doi.org/10.1016/S0140-6736(06)69113-7] [PMID: 16876668]
[2]
Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med 2011; 1(1): a006189.
[http://dx.doi.org/10.1101/cshperspect.a006189] [PMID: 22229116]
[3]
Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: Progress and problems on the road to therapeutics. Science 2002; 297(5580): 353-6.
[http://dx.doi.org/10.1126/science.1072994] [PMID: 12130773]
[4]
Hampel H, Cummings J, Blennow K, Gao P, Jack CR, Vergallo A. Developing the ATX(N) classification for use across the Alzheimer’s disease continuum. Nat Rev Neurol 2021; 17(9): 580-9.
[http://dx.doi.org/10.1038/s41582-021-00520-w] [PMID: 34239130]
[5]
Lane CA, Hardy J, Schott JM. Alzheimer’s disease. Eur J Neurol 2018; 25(1): 59-70.
[http://dx.doi.org/10.1111/ene.13439] [PMID: 28872215]
[6]
Crutch SJ, Lehmann M, Schott JM, Rabinovici GD, Rossor MN, Fox NC. Posterior cortical atrophy. Lancet Neurol 2012; 11(2): 170-8.
[http://dx.doi.org/10.1016/S1474-4422(11)70289-7] [PMID: 22265212]
[7]
Gorno ML, Hillis AE, Weintraub S, et al. Classification of primary progressive aphasia and its variants. Neurology 2011; 76(11): 1006-14.
[http://dx.doi.org/10.1212/WNL.0b013e31821103e6] [PMID: 21325651]
[8]
Lam B, Masellis M, Freedman M, Stuss DT, Black SE. Clinical, imaging, and pathological heterogeneity of the Alzheimer’s disease syndrome. Alzheimers Res Ther 2013; 5(1): 1.
[http://dx.doi.org/10.1186/alzrt155] [PMID: 23302773]
[9]
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group* under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology 1984; 34(7): 939-44.
[http://dx.doi.org/10.1212/WNL.34.7.939] [PMID: 6610841]
[10]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the national institute on aging‐Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 263-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.005] [PMID: 21514250]
[11]
Kunst J, Marecek R, Klobusiakova P, et al. Patterns of grey matter atrophy at different stages of Parkinson’s and Alzheimer’s diseases and relation to cognition. Brain Topogr 2019; 32(1): 142-60.
[http://dx.doi.org/10.1007/s10548-018-0675-2] [PMID: 30206799]
[12]
Risacher S, Saykin A, Wes J, Shen L, Firpi H, McDonald B. Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr Alzheimer Res 2009; 6(4): 347-61.
[http://dx.doi.org/10.2174/156720509788929273] [PMID: 19689234]
[13]
Scheltens P, Leys D, Barkhof F, et al. Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: Diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 1992; 55(10): 967-72.
[http://dx.doi.org/10.1136/jnnp.55.10.967] [PMID: 1431963]
[14]
Koedam ELGE, Lehmann M, van der Flier WM, et al. Visual assessment of posterior atrophy development of a MRI rating scale. Eur Radiol 2011; 21(12): 2618-25.
[http://dx.doi.org/10.1007/s00330-011-2205-4] [PMID: 21805370]
[15]
Jang JW, Park SY, Park YH, et al. A comprehensive visual rating scale of brain magnetic resonance imaging: Application in elderly subjects with Alzheimer’s disease, mild cognitive impairment, and normal cognition. J Alzheimers Dis 2015; 44(3): 1023-34.
[http://dx.doi.org/10.3233/JAD-142088] [PMID: 25380589]
[16]
Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14(4): 535-62.
[http://dx.doi.org/10.1016/j.jalz.2018.02.018] [PMID: 29653606]
[17]
Mao C, Li J, Huang X, et al. White matter hyperintensities and patterns of atrophy in early onset Alzheimer’s disease with causative gene mutations. Clin Neurol Neurosurg 2021; 203: 106552.
[http://dx.doi.org/10.1016/j.clineuro.2021.106552] [PMID: 33601235]
[18]
Scahill RI, Ridgway GR, Bartlett JW, et al. Genetic influences on atrophy patterns in familial Alzheimer’s disease: A comparison of APP and PSEN1 mutations. J Alzheimers Dis 2013; 35(1): 199-212.
[http://dx.doi.org/10.3233/JAD-121255] [PMID: 23380992]
[19]
Sławek J, Narożańska E, Brockhuis B, et al. Neuroimaging in the differential diagnosis of primary progressive aphasia - illustrative case series in the light of new diagnostic criteria. Pol Przegl Radiol Med Nukl 2014; 79: 251-8.
[http://dx.doi.org/10.12659/PJR.890320] [PMID: 25343001]
[20]
Whitwell JL, Jack CR Jr, Kantarci K, et al. Imaging correlates of posterior cortical atrophy. Neurobiol Aging 2007; 28(7): 1051-61.
[http://dx.doi.org/10.1016/j.neurobiolaging.2006.05.026] [PMID: 16797786]
[21]
Ashburner J, Csernansk JG, Davatzikos C, Fox NC, Frisoni GB, Thompson PM. Computer-assisted imaging to assess brain structure in healthy and diseased brains. Lancet Neurol 2003; 2(2): 79-88.
[http://dx.doi.org/10.1016/S1474-4422(03)00304-1] [PMID: 12849264]
[22]
Fischl B, Salat DH, Busa E, et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 2002; 33(3): 341-55.
[http://dx.doi.org/10.1016/S0896-6273(02)00569-X] [PMID: 11832223]
[23]
Fennema NC, Hagler DJ Jr, McEvoy LK, et al. Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Hum Brain Mapp 2009; 30(10): 3238-53.
[http://dx.doi.org/10.1002/hbm.20744] [PMID: 19277975]
[24]
Dickerson BC, Bakkour A, Salat DH, et al. The cortical signature of Alzheimer’s disease: Regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb Cortex 2009; 19(3): 497-510.
[http://dx.doi.org/10.1093/cercor/bhn113] [PMID: 18632739]
[25]
McDonald CR, McEvoy LK, Gharapetian L, et al. Regional rates of neocortical atrophy from normal aging to early Alzheimer’s disease. Neurology 2009; 73(6): 457-65.
[http://dx.doi.org/10.1212/WNL.0b013e3181b16431] [PMID: 19667321]
[26]
McEvoy LK, Brewer JB. Quantitative structural MRI for early detection of Alzheimer’s disease. Expert Rev Neurother 2010; 10(11): 1675-88.
[http://dx.doi.org/10.1586/ern.10.162] [PMID: 20977326]
[27]
Mao C, Sha L, Li J, et al. Relationship between general cognition, visual assessed cortical atrophy, and cerebrospinal fluid biomarkers in Alzheimer’s disease: A cross sectional study from a Chinese PUMCH Cohort. J Alzheimers Dis 2021; 82(1): 205-14.
[http://dx.doi.org/10.3233/JAD-210344] [PMID: 34024840]
[28]
Tan J, Li N, Gao J, et al. Optimal cutoff scores for dementia and mild cognitive impairment of the Montreal Cognitive Assessment among elderly and oldest-old Chinese population. J Alzheimers Dis 2014; 43(4): 1403-12.
[http://dx.doi.org/10.3233/JAD-141278] [PMID: 25147113]
[29]
Ashburner J, Friston KJ. Voxel based morphometry the methods. Neuroimage 2000; 11(6): 805-21.
[http://dx.doi.org/10.1006/nimg.2000.0582] [PMID: 10860804]
[30]
Whitwell JL. Voxel based morphometry: An automated technique for assessing structural changes in the brain. J Neurosci 2009; 29(31): 9661-4.
[http://dx.doi.org/10.1523/JNEUROSCI.2160-09.2009] [PMID: 19657018]
[31]
Meyer P, Feldkamp H, Hoppstädter M, et al. Using voxel-based morphometry to examine the relationship between regional brain volumes and memory performance in amnestic mild cognitive impairment. Front Behav Neurosci 2013; 7: 89.
[http://dx.doi.org/10.3389/fnbeh.2013.00089] [PMID: 23888131]
[32]
Tabatabaei JH, Shaw ME, Walsh E, Cherbuin N. Regional brain atrophy predicts time to conversion to Alzheimer’s disease, dependent on baseline volume. Neurobiol Aging 2019; 83: 86-94.
[http://dx.doi.org/10.1016/j.neurobiolaging.2019.08.033] [PMID: 31585370]
[33]
Poulakis K, Pereira JB, Mecocci P, et al. Heterogeneous patterns of brain atrophy in Alzheimer’s disease. Neurobiol Aging 2018; 65: 98-108.
[http://dx.doi.org/10.1016/j.neurobiolaging.2018.01.009] [PMID: 29455029]
[34]
Risacher SL, Anderson WH, Charil A, et al. Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline. Neurology 2017; 89(21): 2176-86.
[http://dx.doi.org/10.1212/WNL.0000000000004670] [PMID: 29070667]
[35]
Kate M, Dicks E, Visser PJ, et al. Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline. Brain 2018; 141(12): 3443-56.
[http://dx.doi.org/10.1093/brain/awy264] [PMID: 30351346]
[36]
Möller C, Vrenken H, Jiskoot L, et al. Different patterns of gray matter atrophy in early- and late-onset Alzheimer’s disease. Neurobiol Aging 2013; 34(8): 2014-22.
[http://dx.doi.org/10.1016/j.neurobiolaging.2013.02.013] [PMID: 23561509]
[37]
Shima K, Matsunari I, Samuraki M, et al. Posterior cingulate atrophy and metabolic decline in early stage Alzheimer’s disease. Neurobiol Aging 2012; 33(9): 2006-17.
[http://dx.doi.org/10.1016/j.neurobiolaging.2011.07.009] [PMID: 21855172]
[38]
Kilimann I, Grothe M, Heinsen H, et al. Subregional basal forebrain atrophy in Alzheimer’s disease: A multicenter study. J Alzheimers Dis 2014; 40(3): 687-700.
[http://dx.doi.org/10.3233/JAD-132345] [PMID: 24503619]
[39]
Sabuncu MR, Desikan RS, Sepulcre J, et al. The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Arch Neurol 2011; 68(8): 1040-8.
[http://dx.doi.org/10.1001/archneurol.2011.167] [PMID: 21825241]
[40]
Lee JS, Park YH, Park S, et al. Distinct brain regions in physiological and pathological brain aging. Front Aging Neurosci 2019; 11: 147.
[http://dx.doi.org/10.3389/fnagi.2019.00147] [PMID: 31275140]
[41]
Velayudhan L, Proitsi P, Westman E, et al. Entorhinal cortex thickness predicts cognitive decline in Alzheimer’s disease. J Alzheimers Dis 2013; 33(3): 755-66.
[http://dx.doi.org/10.3233/JAD-2012-121408] [PMID: 23047370]
[42]
Gómez IT, Hollister R, West H, et al. Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol 1997; 41(1): 17-24.
[http://dx.doi.org/10.1002/ana.410410106] [PMID: 9005861]
[43]
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 2006; 112(4): 389-404.
[http://dx.doi.org/10.1007/s00401-006-0127-z] [PMID: 16906426]
[44]
Giannakopoulos P, Bouras C, Hof PR. Alzheimer’s disease with asymmetric atrophy of the cerebral hemispheres: Morphometric analysis of four cases. Acta Neuropathol 1994; 88(5): 440-7.
[http://dx.doi.org/10.1007/BF00389496] [PMID: 7847073]
[45]
Murray ME, Graff RNR, Ross OA, Petersen RC, Duara R, Dickson DW. Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: A retrospective study. Lancet Neurol 2011; 10(9): 785-96.
[http://dx.doi.org/10.1016/S1474-4422(11)70156-9] [PMID: 21802369]
[46]
Whitwell JL, Jack CR Jr, Przybelski SA, et al. Temporoparietal atrophy: A marker of AD pathology independent of clinical diagnosis. Neurobiol Aging 2011; 32(9): 1531-41.
[http://dx.doi.org/10.1016/j.neurobiolaging.2009.10.012] [PMID: 19914744]
[47]
Jong LW, Hiele K, Veer IM, et al. Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: An MRI study. Brain 2008; 131(12): 3277-85.
[http://dx.doi.org/10.1093/brain/awn278] [PMID: 19022861]
[48]
Cho H, Seo SW, Kim JH, et al. Changes in subcortical structures in early- versus late-onset Alzheimer’s disease. Neurobiol Aging 2013; 34(7): 1740-7.
[http://dx.doi.org/10.1016/j.neurobiolaging.2013.01.001] [PMID: 23394958]
[49]
Klunk WE, Price JC, Mathis CA, et al. Amyloid deposition begins in the striatum of presenilin-1 mutation carriers from two unrelated pedigrees. J Neurosci 2007; 27(23): 6174-84.
[http://dx.doi.org/10.1523/JNEUROSCI.0730-07.2007] [PMID: 17553989]
[50]
Anderkova L, Barton M, Rektorova I. Striato cortical connections in Parkinson’s and Alzheimer’s diseases: Relation to cognition. Mov Disord 2017; 32(6): 917-22.
[http://dx.doi.org/10.1002/mds.26956] [PMID: 28256044]
[51]
Derflinger S, Sorg C, Gaser C, et al. Grey matter atrophy in Alzheimer’s disease is asymmetric but not lateralized. J Alzheimers Dis 2011; 25(2): 347-57.
[http://dx.doi.org/10.3233/JAD-2011-110041] [PMID: 21422522]
[52]
Ossenkoppele R, Smith R, Ohlsson T, et al. Associations between tau, Aβ and cortical thickness with cognition in Alzheimer’s disease. Neurology 2019; 92(6): e601-12.
[http://dx.doi.org/10.1212/WNL.0000000000006875] [PMID: 30626656]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy