We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer’s disease (AD) and 2) jointly predict the Mini Mental State Examination (MMSE) scores of South Korean patients with AD. Using resting-state functional Magnetic Resonance Imaging (rs-fMRI) scans of 331 participants, we obtained functional 3-dimensional (3-D) independent component spatial maps for use as features in classification and regression tasks. A 3-D convolutional neural network (CNN) architecture was developed for the classification task. MMSE scores were predicted using: linear least square regression (LLSR), support vector regression, bagging-based ensemble regression, and tree regression with group independent component analysis (gICA) features. To improve MMSE regression performance, we applied feature optimization methods including least absolute shrinkage and selection operator and support vector machine-based recursive feature elimination (SVM-RFE). The mean balanced test accuracy was 85.27% for the classification of AD versus healthy controls. The medial visual, default mode, dorsal attention, executive, and auditory related networks were mainly associated with AD. The maximum clinical MMSE score prediction accuracy with the LLSR method applied on gICA combined with SVM-RFE features had the lowest root mean square error (3.27 ± 0.58) and the highest R² value (0.63 ± 0.02). Classification of AD and healthy controls can be successfully achieved using only rs-fMRI and MMSE scores can be accurately predicted using functional independent component features. In the absence of trained clinicians, AD disease status and clinical MMSE scores can be jointly predicted using 3-D deep learning and regression learning approaches with rs-fMRI data.

中文翻译：

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基于3D深度学习的阿尔茨海默氏病自动诊断与MMSE联合预测，采用静态FMRI。

我们进行这项研究的目的是：1）评估用于诊断阿尔茨海默氏病（AD）的新型深度学习方法，以及2）共同预测韩国AD患者的迷你精神状态检查（MMSE）得分。使用331名参与者的静止状态功能磁共振成像（rs-fMRI）扫描，我们获得了功能性3维（3-D）独立分量空间图，可用作分类和回归任务中的特征。针对分类任务，开发了3D卷积神经网络（CNN）体系结构。MMSE分数的预测使用：线性最小二乘回归（LLSR），支持向量回归，基于装袋的整体回归和具有组独立成分分析（gICA）功能的树回归。为了提高MMSE回归性能，我们应用了包括最小绝对收缩和选择算子在内的特征优化方法，并支持基于向量机的递归特征消除（SVM-RFE）。AD对比健康对照组的平均平衡测试准确度为85.27％。内侧视觉，默认模式，背侧注意力，执行和听觉相关网络主要与AD相关。在ICA上结合LLVM方法和SVM-RFE功能使用LLSR方法获得的最大临床MMSE评分预测准确性具有最低的均方根误差（3.27±0.58）和最高的R 与听觉相关的网络主要与AD相关。在ICA上结合LLVM方法和SVM-RFE功能使用LLSR方法获得的最大临床MMSE评分预测准确性具有最低的均方根误差（3.27±0.58）和最高的R 与听觉相关的网络主要与AD相关。在ICA上结合LLVM方法和SVM-RFE功能使用的最大临床MMSE评分预测准确性具有最低的均方根误差（3.27±0.58）和最高的R^2个值（0.63±0.02）。仅使用rs-fMRI即可成功实现AD和健康对照的分类，而使用功能独立的组件功能可准确预测MMSE评分。在没有训练有素的临床医生的情况下，可以使用具有rs-fMRI数据的3-D深度学习和回归学习方法共同预测AD疾病状态和MMSE临床得分。