NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders.
NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders.
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NeuroMark：基于自动化和自适应 ICA 的管道，用于识别脑部疾病的可重复功能磁共振成像标记。
DOI：
10.1016/j.nicl.2020.102375
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Du Y;Fu Z;Sui J;Gao S;Xing Y;Lin D;Salman M;Abrol A;Rahaman MA;Chen J;Hong LE;Kochunov P;Osuch EA;Calhoun VD;Alzheimer's Disease Neuroimaging Initiative
提出一种新的流程，将不同数据集、研究和疾病之间的大脑变化联系起来。
利用独立数据确定精神分裂症中可重复的生物标志物。
找出精神分裂症和自闭症中共同及独特的大脑损伤。
揭示从健康对照到轻度认知障碍再到阿尔茨海默病的渐进变化。
在双相情感障碍和重度抑郁症之间获得高分类准确率（约90%）。
许多精神疾病有重叠或相似的临床症状，这给诊断带来了困扰。系统地描述独特和相似的变化模式在多大程度上反映大脑疾病是很重要的。神经影像学数据共享举措的增加为研究大脑疾病提供了前所未有的机会。然而，在不同研究中重复和转化研究结果仍然是一个未解决的问题。非常需要用于获取可重复且可比的影像标志物的标准化方法。在此，我们提出一种基于先验驱动的独立成分分析的流程，即NeuroMark，它能够从功能磁共振成像（fMRI）数据中估计大脑功能网络指标，这些指标可用于将不同数据集、研究和疾病之间的大脑网络异常联系起来。NeuroMark通过利用从1828名健康对照中提取的可靠大脑网络模板作为指导，自动估计适用于每个个体且在数据集/研究/疾病之间可比的特征。对2442名受试者进行了四项研究，涵盖六种大脑疾病（精神分裂症、自闭症谱系障碍、轻度认知障碍、阿尔茨海默病、双相情感障碍和重度抑郁症），从不同角度（大脑异常的重复、跨研究比较、细微大脑变化的识别以及利用已识别的生物标志物进行多疾病分类）评估所提出流程的有效性。我们的结果强调，NeuroMark有效地识别了不同数据集中精神分裂症可重复的大脑网络异常；揭示了自闭症和精神分裂症之间重叠和特异性的有趣神经线索；证明了轻度认知障碍和阿尔茨海默病中不同程度存在的大脑功能损伤；并捕捉到了在双相情感障碍和重度抑郁症分类中表现良好的生物标志物。
Propose a new pipeline to link brain changes among different datasets, studies, and disorders. Identify reproducible biomarkers in schizophrenia using independent data. Find both common and unique brain impairments in schizophrenia and autism. Reveal gradual changes from healthy controls to mild cognitive impairment to Alzheimer’s disease. Obtain high classification accuracy (~90%) between bipolar disorder and major depressive disorder. 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.
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影响因子：
14.5
作者：
Bailey, A;Luthert, P;Lantos, P
通讯作者：
Lantos, P
影响因子：
25
作者：
Cohen JD;Daw N;Engelhardt B;Hasson U;Li K;Niv Y;Norman KA;Pillow J;Ramadge PJ;Turk-Browne NB;Willke TL
通讯作者：
Willke TL
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影响因子：
4.6
作者：
Chen, Pinhong;Ye, Enmao;Wang, Lubin
通讯作者：
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影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
5.7
作者：
Demirci, Oguz;Clark, Vincent P.;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.