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
[http://dx.doi.org/10.1016/S0140-6736(06)69113-7] [PMID: 16876668]
[http://dx.doi.org/10.1101/cshperspect.a006189] [PMID: 22229116]
[http://dx.doi.org/10.1126/science.1072994] [PMID: 12130773]
[http://dx.doi.org/10.1038/s41582-021-00520-w] [PMID: 34239130]
[http://dx.doi.org/10.1111/ene.13439] [PMID: 28872215]
[http://dx.doi.org/10.1016/S1474-4422(11)70289-7] [PMID: 22265212]
[http://dx.doi.org/10.1212/WNL.0b013e31821103e6] [PMID: 21325651]
[http://dx.doi.org/10.1186/alzrt155] [PMID: 23302773]
[http://dx.doi.org/10.1212/WNL.34.7.939] [PMID: 6610841]
[http://dx.doi.org/10.1016/j.jalz.2011.03.005] [PMID: 21514250]
[http://dx.doi.org/10.1007/s10548-018-0675-2] [PMID: 30206799]
[http://dx.doi.org/10.2174/156720509788929273] [PMID: 19689234]
[http://dx.doi.org/10.1136/jnnp.55.10.967] [PMID: 1431963]
[http://dx.doi.org/10.1007/s00330-011-2205-4] [PMID: 21805370]
[http://dx.doi.org/10.3233/JAD-142088] [PMID: 25380589]
[http://dx.doi.org/10.1016/j.jalz.2018.02.018] [PMID: 29653606]
[http://dx.doi.org/10.1016/j.clineuro.2021.106552] [PMID: 33601235]
[http://dx.doi.org/10.3233/JAD-121255] [PMID: 23380992]
[http://dx.doi.org/10.12659/PJR.890320] [PMID: 25343001]
[http://dx.doi.org/10.1016/j.neurobiolaging.2006.05.026] [PMID: 16797786]
[http://dx.doi.org/10.1016/S1474-4422(03)00304-1] [PMID: 12849264]
[http://dx.doi.org/10.1016/S0896-6273(02)00569-X] [PMID: 11832223]
[http://dx.doi.org/10.1002/hbm.20744] [PMID: 19277975]
[http://dx.doi.org/10.1093/cercor/bhn113] [PMID: 18632739]
[http://dx.doi.org/10.1212/WNL.0b013e3181b16431] [PMID: 19667321]
[http://dx.doi.org/10.1586/ern.10.162] [PMID: 20977326]
[http://dx.doi.org/10.3233/JAD-210344] [PMID: 34024840]
[http://dx.doi.org/10.3233/JAD-141278] [PMID: 25147113]
[http://dx.doi.org/10.1006/nimg.2000.0582] [PMID: 10860804]
[http://dx.doi.org/10.1523/JNEUROSCI.2160-09.2009] [PMID: 19657018]
[http://dx.doi.org/10.3389/fnbeh.2013.00089] [PMID: 23888131]
[http://dx.doi.org/10.1016/j.neurobiolaging.2019.08.033] [PMID: 31585370]
[http://dx.doi.org/10.1016/j.neurobiolaging.2018.01.009] [PMID: 29455029]
[http://dx.doi.org/10.1212/WNL.0000000000004670] [PMID: 29070667]
[http://dx.doi.org/10.1093/brain/awy264] [PMID: 30351346]
[http://dx.doi.org/10.1016/j.neurobiolaging.2013.02.013] [PMID: 23561509]
[http://dx.doi.org/10.1016/j.neurobiolaging.2011.07.009] [PMID: 21855172]
[http://dx.doi.org/10.3233/JAD-132345] [PMID: 24503619]
[http://dx.doi.org/10.1001/archneurol.2011.167] [PMID: 21825241]
[http://dx.doi.org/10.3389/fnagi.2019.00147] [PMID: 31275140]
[http://dx.doi.org/10.3233/JAD-2012-121408] [PMID: 23047370]
[http://dx.doi.org/10.1002/ana.410410106] [PMID: 9005861]
[http://dx.doi.org/10.1007/s00401-006-0127-z] [PMID: 16906426]
[http://dx.doi.org/10.1007/BF00389496] [PMID: 7847073]
[http://dx.doi.org/10.1016/S1474-4422(11)70156-9] [PMID: 21802369]
[http://dx.doi.org/10.1016/j.neurobiolaging.2009.10.012] [PMID: 19914744]
[http://dx.doi.org/10.1093/brain/awn278] [PMID: 19022861]
[http://dx.doi.org/10.1016/j.neurobiolaging.2013.01.001] [PMID: 23394958]
[http://dx.doi.org/10.1523/JNEUROSCI.0730-07.2007] [PMID: 17553989]
[http://dx.doi.org/10.1002/mds.26956] [PMID: 28256044]
[http://dx.doi.org/10.3233/JAD-2011-110041] [PMID: 21422522]
[http://dx.doi.org/10.1212/WNL.0000000000006875] [PMID: 30626656]