Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)
Discriminative analysis of early Alzheimer's disease using multi-modal imaging and multi-level characterization with multi-classifier (M3)
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DOI：
10.1016/j.neuroimage.2011.10.003
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发表时间：
2012-02-01
期刊：
影响因子：
5.7
通讯作者：
He, Yong
中科院分区：
文献类型：
--
作者：
Dai, Zhengjia;Yan, Chaogan;He, Yong
最近，人们越来越关注模式识别和脑成像技术在阿尔茨海默病（AD）的有效和准确诊断中的应用。然而，大多数现有研究侧重于使用单模态（例如结构或功能 MRI）或单水平（例如大脑局部或连接性指标）生物标志物来诊断 AD。在这项研究中，我们提出了一种方法框架，称为多模态成像和多分类器 (M3) 的多层次特征，以区分 AD 患者与健康对照。该方法涉及两种成像模式的数据分析：结构 MRI，用于测量区域灰质体积；静息态功能 MRI，用于测量三个不同水平的功能特征，包括低频波动幅度 (ALFF)、区域均匀性 (ReHo) 和区域功能连接强度 (RFCS)。对于每个指标，我们计算了从先前图集导出的 90 个感兴趣区域的值，然后使用基于四个最大不确定性线性判别分析基分类器的多分类器对其进行进一步训练。使用留一法交叉验证来评估该方法的性能。将 M3 方法应用于包含 16 名 AD 患者和 22 名健康对照的数据集，分类准确度为 89.47%，敏感性为 87.50%，特异性为 90.91%。进一步分析表明，最具区分性的分类特征主要涉及几个默认模式（内侧额回、后扣带回、海马和海马旁回）、枕叶（梭状回、枕下回和中枕回）和皮质下（杏仁核和豆状核苍白球）区域。因此，M3方法通过整合来自不同成像方式和不同功能特性的信息，显示出有前景的分类性能，并且有可能改善AD的临床诊断和治疗评估。 (C) 2011 Elsevier Inc. 保留所有权利。
Increasing attention has recently been directed to the applications of pattern recognition and brain imaging techniques in the effective and accurate diagnosis of Alzheimer's disease (AD). However, most of the existing research focuses on the use of single-modal (e.g., structural or functional MRI) or single-level (e.g., brain local or connectivity metrics) biomarkers for the diagnosis of AD. In this study, we propose a methodological framework, called multi-modal imaging and multi-level characteristics with multi-classifier (M3), to discriminate patients with AD from healthy controls. This approach involved data analysis from two imaging modalities: structural MRI, which was used to measure regional gray matter volume, and resting-state functional MRI, which was used to measure three different levels of functional characteristics, including the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and regional functional connectivity strength (RFCS). For each metric, we computed the values of ninety regions of interest derived from a prior atlas, which were then further trained using a multi-classifier based on four maximum uncertainty linear discriminant analysis base classifiers. The performance of this method was evaluated using leave-one-out cross-validation. Applying the M3 approach to the dataset containing 16 AD patients and 22 healthy controls led to a classification accuracy of 89.47% with a sensitivity of 87.50% and a specificity of 90.91%. Further analysis revealed that the most discriminative features for classification are predominantly involved in several default-mode (medial frontal gyrus, posterior cingulate gyrus, hippocampus and parahippocampal gyrus), occipital (fusiform gyrus, inferior and middle occipital gyrus) and subcortical (amygdale and pallidum of lenticular nucleus) regions. Thus, the M3 method shows promising classification performance by incorporating information from different imaging modalities and different functional properties, and it has the potential to improve the clinical diagnosis and treatment evaluation of AD. (C) 2011 Elsevier Inc. All rights reserved.