Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline
Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline
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DOI：
10.1016/j.neuroimage.2007.10.031
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发表时间：
2008-02-15
期刊：
影响因子：
5.7
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Fan, Yong;Batmanghelich, Nematollah;Davatzikos, Christos
应用计算神经解剖学方法，对轻度认知障碍（MCI）和阿尔茨海默病（AD）患者脑萎缩的空间分布进行了研究。这些模式在空间上是复杂的，涉及许多大脑区域。除海马和内侧颞叶灰质外，许多其他区域显示显著萎缩，包括眶额和内侧-前额叶灰质、扣带回（主要是后部）、海马、钩回和颞叶白色物质。大约2/3的MCI组呈现与AD重叠的萎缩模式，而剩余的1/3与认知正常个体重叠，从而表明在该MCI患者队列中，一些但不是全部MCI患者具有显著和广泛的脑萎缩。重要的是，具有AD样模式的组在随访访视中表现出更高的MMSE下降率;相反，模式分类为在基线后一年内表现出相对较高MMSE下降的个体提供了相对较高的分类准确性（87%）。高维模式分类，非线性多变量分析，提供了结构异常的措施，可能是有用的个体患者分类，以及预测进展和检查组分析中的多变量关系。(C)2007年爱思唯尔公司All rights reserved.
Spatial patterns of brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) were measured via methods of computational neuroanatomy. These patterns were spatially complex and involved many brain regions. In addition to the hippocampus and the medial temporal lobe gray matter, a number of other regions displayed significant atrophy, including orbitofrontal and medial-prefrontal grey matter, cingulate (mainly posterior), insula, uncus, and temporal lobe white matter. Approximately 2/3 of the MCI group presented patterns of atrophy that overlapped with AD, whereas the remaining 1/3 overlapped with cognitively normal individuals, thereby indicating that some, but not all, MCI patients have significant and extensive brain atrophy in this cohort of MCI patients. Importantly, the group with AD-like patterns presented much higher rate of MMSE decline in follow-up visits; conversely, pattern classification provided relatively high classification accuracy (87%) of the individuals that presented relatively higher MMSE decline within a year from baseline. High-dimensional pattern classification, a nonlinear multivariate analysis, provided measures of structural abnormality that can potentially be useful for individual patient classification, as well as for predicting progression and examining multivariate relationships in group analyses. (C) 2007 Elsevier Inc. All rights reserved.