Imaging parameters.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer’s disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient’s age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders’ development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.静息态功能磁共振成像（resting-state functional magnetic resonance imaging，rs-fMRI）已愈发广泛应用于阿尔茨海默病（Alzheimer’s disease，AD）与精神分裂症（schizophrenia，SZ）的相关研究。当前针对AD与SZ的rs-fMRI研究多以患者与健康对照进行对比，但直接比较AD与SZ患者以识别两种疾病共有的潜在生物标志物，同样具备重要研究价值。然而，由于存在潜在混淆因素（如患者年龄、扫描方案差异等），对不同研究中采集的患者群组开展直接比较颇具挑战性。本研究对162例AD及晚发性轻度认知障碍（late mild cognitive impairment，LMCI）患者、181例精神分裂症患者与315例认知正常（cognitively normal，CN）受试者的静息态功能网络连接（resting-state functional network connectivity，rs-FNC）进行了对比分析。我们采用经混淆因素校正的rs-FNC数据，并应用机器学习算法（包括支持向量机、逻辑回归、随机森林及k近邻算法）与深度学习模型（即全连接神经网络），根据受试者的诊断标签（如AD、SZ与CN）开展二分类与三分类任务。统计学分析结果显示，相较于SZ患者，AD患者的以下网络对之间的功能连接更强：皮层下-小脑网络、皮层下-认知控制网络、认知控制-小脑网络以及视觉-感觉运动网络。与之相反，SZ患者的以下网络对之间的功能连接强于AD患者：皮层下-视觉网络、皮层下-听觉网络、皮层下-感觉运动网络、小脑-视觉网络、感觉运动-认知控制网络，以及小脑网络内部连接。此外，我们观察到，尽管AD与SZ各自存在独特的功能网络连接异常，但二者也存在一些共有的功能异常，这可能源于两种疾病发病机制相似的神经生物学基础或遗传因素。进一步研究发现，在AD与SZ的二分类任务中，我们实现了85%的分类准确率，其中默认模式网络、视觉网络与皮层下网络对分类贡献最大；而在AD、SZ与CN的三分类任务中，分类准确率达68%，其中皮层下域是贡献度最高的特征。最后，我们的研究结果表明，除AD与SZ的二分类任务外，其余所有分类任务中，男性受试者的分类可预测性均高于女性。