Background: Neuroimaging is an important tool in early detection of Alzheimer’s disease (AD), which is a serious neurodegenerative brain disease among the elderly subjects. Independent component analysis (ICA) is arguably one of the most widely used algorithm for the analysis of brain imaging data, which can be used to extract intrinsic networks of brain from functional magnetic resonance imaging (fMRI).

Methods: Witnessed by recent studies, a more flexible model known as restricted Boltzmann machine (RBM) can also be used to extract spatial maps and time courses of intrinsic networks from resting state fMRI, moreover, RBM shows superior temporal features than ICA. Here, we seek to employ RBM to improve the performance of classifying individuals. Experiments are performed on healthy controls and subjects at the early stage of AD, i.e., cognitive normal (CN) and early mild cognitive impairment participants (EMCI), and two types of data, i.e., structural magnetic resonance imaging (sMRI) and fMRI data.

Results: (1) By separately employing ICA for sMRI and fMRI, the features extracted from fMRI improve classification accuracy by 7.5% for CN and EMCI; (2) instead of applying ICA to fMRI, using RBM further improves classification accuracy by 7.75% for CN and EMCI; (3) the lesions at the early stage of AD are more likely to occur in the regions around slices 4, 6, 10, 14, 19, 51 and 59 of the whole brain in the longitudinal direction.

Conclusion: By using fMRI instead of sMRI and RBM instead of ICA, we can classify CN and EMCI more efficiently.

背景：神经影像学是早期发现阿尔茨海默氏病（AD）的重要工具，该病是老年受试者中的一种严重的神经退行性脑疾病。独立成分分析（ICA）可以说是分析大脑成像数据最广泛使用的算法之一，可用于从功能磁共振成像（fMRI）提取大脑的固有网络。

方法：最近的研究表明，一种更灵活的模型，称为受限玻尔兹曼机（RBM），也可以用于从静止状态fMRI提取内在网络的空间图和时程，此外，RBM具有比ICA优越的时空特征。在这里，我们寻求运用RBM来提高对个人进行分类的效果。在AD的健康对照和受试者（即认知正常（CN）和早期轻度认知障碍参与者（EMCI））以及两种类型的数据（即结构磁共振成像（sMRI）和fMRI数据）上进行实验。

结果：（1）通过将ICA分别用于sMRI和fMRI，从fMRI提取的特征将CN和EMCI的分类准确率提高了7.5％；（2）使用RBM可以将CN和EMCI的分类准确性进一步提高7.75％，而不是将ICA应用于fMRI。（3）AD早期的病变更可能发生在纵向上全脑的切片4、6、10、14、19、51和59周围的区域。

结论：通过使用fMRI代替sMRI和RBM代替ICA，我们可以更有效地对CN和EMCI进行分类。