Identifying functional imaging markers of mild cognitive impairment in early Alzheimer’s and Parkinson’s disease using multivariate analysis

Chaorui Huang , Paul Mattis , Per Julin
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引用次数: 11

Abstract

Functional neuroimaging, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), provides a valuable technique for detecting regional changes in brain metabolic activity and blood flow associated with mild cognitive impairment (MCI) and dementia. Multivariate analysis techniques have recently received increasing attention. The results of multivariate analysis can be more easily interpreted as a signature of neuronal networks, which lend themselves to prospective application of results from the analysis of one dataset to entirely new datasets. They are well placed to provide information about mean differences and correlations with behavior with potentially greater statistical power and better reproducibility. This article will focus on investigating the baseline and progression of MCI using functional brain imaging techniques and multivariate analysis in order to understand the genesis and natural history of cognitive impairment in Alzheimer’s disease (AD) and Parkinson’s disease (PD), respectively.

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使用多变量分析识别早期阿尔茨海默病和帕金森病轻度认知障碍的功能影像学标志物
功能神经成像,如正电子发射断层扫描(PET)和单光子发射计算机断层扫描(SPECT),为检测与轻度认知障碍(MCI)和痴呆相关的脑代谢活动和血流的区域变化提供了一种有价值的技术。多元分析技术近年来受到越来越多的关注。多变量分析的结果可以更容易地解释为神经网络的特征,这使得它们可以将一个数据集的分析结果前瞻性地应用于全新的数据集。它们很好地提供了关于平均差异和行为相关性的信息,具有潜在的更大的统计能力和更好的可重复性。本文将重点研究MCI的基线和进展,使用功能脑成像技术和多变量分析,以了解阿尔茨海默病(AD)和帕金森病(PD)认知功能障碍的发生和自然历史。
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