دانلود مقاله ISI انگلیسی شماره 150592
ترجمه فارسی عنوان مقاله

معیارهای شبکه خاکستری با کاهش شناختی در اختلال شناختی خفیف همراه است

عنوان انگلیسی
Gray matter network measures are associated with cognitive decline in mild cognitive impairment
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
150592 2018 35 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Neurobiology of Aging, Volume 61, January 2018, Pages 198-206

ترجمه کلمات کلیدی
بیماری آلزایمر، کاهش شناختی، شبکه های خاکستری خاکستری اختلال شناختی خفیف، تنها موضوع نظریه گراف،
کلمات کلیدی انگلیسی
Alzheimer's disease; Cognitive decline; Gray matter networks; Mild cognitive impairment; Single-subject; Graph theory;
پیش نمایش مقاله
پیش نمایش مقاله  معیارهای شبکه خاکستری با کاهش شناختی در اختلال شناختی خفیف همراه است

چکیده انگلیسی

Gray matter networks are disrupted in Alzheimer's disease and related to cognitive impairment. However, it is still unclear whether these disruptions are associated with cognitive decline over time. Here, we studied this question in a large sample of patients with mild cognitive impairment with extensive longitudinal neuropsychological assessments. Gray matter networks were extracted from baseline structural magnetic resonance imaging, and we tested associations of network measures and cognitive decline in Mini–Mental State Examination and 5 cognitive domains (i.e., memory, attention, executive function, visuospatial, and language). Disrupted network properties were cross-sectionally related to worse cognitive impairment. Longitudinally, lower small-world coefficient values were associated with a steeper decline in almost all domains. Lower betweenness centrality values correlated with a faster decline in Mini–Mental State Examination and memory, and at a regional level, these associations were specific for the precuneus, medial frontal, and temporal cortex. Furthermore, network measures showed additive value over established biomarkers in predicting cognitive decline. Our results suggest that gray matter network measures might have use in identifying patients who will show fast disease progression.