In:
Current Alzheimer Research, Bentham Science Publishers Ltd., Vol. 19, No. 8 ( 2022-07), p. 618-627
Abstract:
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. Ojective: 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, 21-30 mild. Cortical volume and thickness determined using 3D-T1 MRI data were analyzed using voxel-based 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 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.
Type of Medium:
Online Resource
ISSN:
1567-2050
DOI:
10.2174/1567205019666220905145756
Language:
English
Publisher:
Bentham Science Publishers Ltd.
Publication Date:
2022
