Lenhart Lukas, Seiler Stephan, Pirpamer Lukas, Goebel Georg, Potrusil Thomas, Wagner Michaela, Dal Bianco Peter, Ransmayr Gerhard, Schmidt Reinhold, Benke Thomas, Scherfler Christoph
Department of Neurology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Department of Neuroradiology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Brain Sci. 2021 Nov 11;11(11):1491. doi: 10.3390/brainsci11111491.
MRI studies have consistently identified atrophy patterns in Alzheimer's disease (AD) through a whole-brain voxel-based analysis, but efforts to investigate morphometric profiles using anatomically standardized and automated whole-brain ROI analyses, performed at the individual subject space, are still lacking. In this study we aimed (i) to utilize atlas-derived measurements of cortical thickness and subcortical volumes, including of the hippocampal subfields, to identify atrophy patterns in early-stage AD, and (ii) to compare cognitive profiles at baseline and during a one-year follow-up of those previously identified morphometric AD subtypes to predict disease progression. Through a prospectively recruited multi-center study, conducted at four Austrian sites, 120 patients were included with probable AD, a disease onset beyond 60 years and a clinical dementia rating of ≤1. Morphometric measures of T1-weighted images were obtained using FreeSurfer. A principal component and subsequent cluster analysis identified four morphometric subtypes, including (i) hippocampal predominant (30.8%), (ii) hippocampal-temporo-parietal (29.2%), (iii) parieto-temporal (hippocampal sparing, 20.8%) and (iv) hippocampal-temporal (19.2%) atrophy patterns that were associated with phenotypes differing predominately in the presentation and progression of verbal memory and visuospatial impairments. These morphologically distinct subtypes are based on standardized brain regions, which are anatomically defined and freely accessible so as to validate its diagnostic accuracy and enhance the prediction of disease progression.
磁共振成像(MRI)研究通过全脑基于体素的分析,始终如一地识别出阿尔茨海默病(AD)的萎缩模式,但在个体受试者空间进行的、使用解剖学标准化和自动化全脑感兴趣区(ROI)分析来研究形态测量特征的工作仍很缺乏。在本研究中,我们旨在:(i)利用源自图谱的皮质厚度和皮质下体积测量值,包括海马亚区的测量值,来识别早期AD的萎缩模式;(ii)比较那些先前确定的形态测量AD亚型在基线时和一年随访期间的认知特征,以预测疾病进展。通过在奥地利四个地点进行的一项前瞻性招募的多中心研究,纳入了120例可能患有AD的患者,疾病发病年龄超过60岁,临床痴呆评定量表评分≤1。使用FreeSurfer获得T1加权图像的形态测量指标。主成分分析和随后的聚类分析确定了四种形态测量亚型,包括:(i)海马为主型(30.8%),(ii)海马-颞-顶叶型(29.2%),(iii)顶-颞叶型(海马未受累,20.8%)和(iv)海马-颞叶型(19.2%)萎缩模式,这些模式与在言语记忆和视觉空间障碍的表现和进展方面主要不同的表型相关。这些形态学上不同的亚型基于标准化的脑区,这些脑区在解剖学上有明确界定且可免费获取,以便验证其诊断准确性并加强对疾病进展的预测。