Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.

许多精神疾病具有重叠或相似的临床症状，使诊断混乱。重要的是系统地表征独特和相似的变化模式反映脑部疾病的程度。关于神经影像数据的共享倡议越来越多，为研究脑部疾病提供了前所未有的机会。然而，在研究之间复制和翻译研究结果仍然是一个悬而未决的问题。非常需要用于捕获可再现和可比的成像标记物的标准化方法。在这里，我们提出了一个基于先验驱动的独立成分分析NeuroMark的管道，该管道能够从功能磁共振成像（fMRI）数据中估算出大脑功能网络的测量值，这些数据可用于链接不同数据集之间的大脑网络异常，研究，和疾病。NeuroMark通过利用从1828个健康对照中提取的可靠脑网络模板作为指导，自动估计适合每个个体的特征，并在数据集/研究/疾病之间具有可比性。进行了包括2442名受试者在内的四项研究，涵盖了六种脑部疾病（精神分裂症，自闭症谱系障碍，轻度认知障碍，阿尔茨海默氏病，躁郁症和重度抑郁症），以从不同角度（重复性脑部异常，交叉研究比较，识别细微的大脑变化以及使用已识别的生物标记物进行多疾病分类）。我们的结果表明，NeuroMark有效地识别了跨不同数据集的精神分裂症的复制性脑网络异常；揭示了有关自闭症和精神分裂症重叠和特异性的有趣的神经线索；在轻度认知障碍和阿尔茨海默氏病中表现出不同程度的脑功能障碍；并捕获了在分类躁郁症和重度抑郁症中表现良好的生物标志物。