Long Zhuqing, Li Jie, Fan Jianghua, Li Bo, Du Yukeng, Qiu Shuang, Miao Jichang, Chen Jian, Yin Juanwu, Jing Bin
Medical Apparatus and Equipment Deployment, Hunan Children's Hospital, Changsha, Hunan Province, China.
School of Biomedical Engineering, Capital Medical University, Beijing, China.
Front Aging Neurosci. 2023 Aug 31;15:1212275. doi: 10.3389/fnagi.2023.1212275. eCollection 2023.
Multi-modal neuroimaging metrics in combination with advanced machine learning techniques have attracted more and more attention for an effective multi-class identification of Alzheimer's disease (AD), mild cognitive impairment (MCI) and health controls (HC) recently.
In this paper, a total of 180 subjects consisting of 44 AD, 66 MCI and 58 HC subjects were enrolled, and the multi-modalities of the resting-state functional magnetic resonance imaging (rs-fMRI) and the structural MRI (sMRI) for all participants were obtained. Then, four kinds of metrics including the Hurst exponent (HE) metric and bilateral hippocampus seed independently based connectivity metrics generated from fMRI data, and the gray matter volume (GMV) metric obtained from sMRI data, were calculated and extracted in each region of interest (ROI) based on a newly proposed automated anatomical Labeling (AAL3) atlas after data pre-processing. Next, these metrics were selected with a minimal redundancy maximal relevance (MRMR) method and a sequential feature collection (SFC) algorithm, and only a subset of optimal features were retained after this step. Finally, the support vector machine (SVM) based classification methods and artificial neural network (ANN) algorithm were utilized to identify the multi-class of AD, MCI and HC subjects in single modal and multi-modal metrics respectively, and a nested ten-fold cross-validation was utilized to estimate the final classification performance.
The results of the SVM and ANN based methods indicated the best accuracies of 80.36 and 74.40%, respectively, by utilizing all the multi-modal metrics, and the optimal accuracies for AD, MCI and HC were 79.55, 78.79 and 82.76%, respectively, in the SVM based method. In contrast, when using single modal metric, the SVM based method obtained a best accuracy of 72.62% with the HE metric, and the accuracies for AD, MCI and HC subjects were just 56.82, 80.30 and 75.86%, respectively. Moreover, the overlapping abnormal brain regions detected by multi-modal metrics were mainly located at posterior cingulate gyrus, superior frontal gyrus and cuneus.
Taken together, the SVM based method with multi-modal metrics could provide effective diagnostic information for identifying AD, MCI and HC subjects.
多模态神经影像学指标与先进的机器学习技术相结合，近年来在阿尔茨海默病（AD）、轻度认知障碍（MCI）和健康对照（HC）的有效多类别识别方面受到越来越多的关注。
本文共纳入180名受试者，包括44名AD患者、66名MCI患者和58名HC受试者，并获取了所有参与者的静息态功能磁共振成像（rs-fMRI）和结构磁共振成像（sMRI）的多模态数据。然后，在数据预处理后，基于新提出的自动解剖标记（AAL3）图谱，在每个感兴趣区域（ROI）计算并提取了四种指标，包括赫斯特指数（HE）指标、基于功能磁共振成像数据独立生成的双侧海马种子连接性指标，以及从结构磁共振成像数据获得的灰质体积（GMV）指标。接下来，使用最小冗余最大相关（MRMR）方法和顺序特征收集（SFC）算法对这些指标进行选择，经过这一步骤后仅保留最优特征的一个子集。最后，利用基于支持向量机（SVM）的分类方法和人工神经网络（ANN）算法分别在单模态和多模态指标中识别AD、MCI和HC受试者的多类别，并采用嵌套十折交叉验证来估计最终的分类性能。
基于支持向量机和人工神经网络的方法结果表明，利用所有多模态指标时，最佳准确率分别为80.36%和74.40%，在基于支持向量机的方法中，AD、MCI和HC的最佳准确率分别为79.55%、78.79%和82.76%。相比之下，使用单模态指标时，基于支持向量机的方法使用HE指标获得的最佳准确率为72.62%，AD、MCI和HC受试者的准确率分别仅为56.82%、80.30%和75.86%。此外，多模态指标检测到的重叠异常脑区主要位于后扣带回、额上回和楔叶。
综上所述，基于支持向量机的多模态指标方法可为识别AD、MCI和HC受试者提供有效的诊断信息。
