There is a lack of objective biomarkers to accurately identify the underlying etiology and related pathophysiology of disparate brain-based disorders that are less distinguishable clinically. Brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been a popular tool for discovering candidate biomarkers. Specifically, independent component analysis (ICA) of rs-fMRI data is a powerful multivariate technique for investigating brain networks. However, ICA-derived brain networks that are not highly reproducible within heterogeneous clinical populations may exhibit mean statistical separation between groups, yet not be sufficiently discriminative at the individual-subject level. We hypothesize that functional brain networks that are most reproducible in subjects within clinical and control groups separately, but not when the two groups are merged, may possess the ability to discriminate effectively between the groups even at the individual-subject level. In this study, we present DisConICA or “Discover Confirm Independent Component Analysis”, a software package that implements the methodology in support of our hypothesis. It relies on a “discover-confirm” approach based upon the assessment of reproducibility of independent components (representing brain networks) obtained from rs-fMRI (discover phase) using the gRAICAR (generalized Ranking and Averaging Independent Component Analysis by Reproducibility) algorithm, followed by unsupervised clustering analysis of these components to evaluate their ability to discriminate between groups (confirm phase). The unique feature of our software package is its ability to seamlessly interface with other software packages such as SPM and FSL, so that all related analyses utilizing features of other software can be performed within our package, thus providing a one-stop software solution starting with raw DICOM images to the final results. We showcase our software using rs-fMRI data acquired from US Army soldiers returning from the wars in Iraq and Afghanistan who were clinically grouped into the following groups: PTSD (posttraumatic stress disorder), comorbid PCS (post-concussion syndrome) + PTSD, and matched healthy combat controls. This software package along with test data sets is available for download at https://bitbucket.org/masauburn/disconica.

中文翻译：

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DisConICA：用于评估大脑网络的再现性及其跨疾病区分性的软件包。


缺乏客观的生物标志物来准确识别临床上难以区分的不同脑部疾病的潜在病因和相关病理生理学。源自静息态功能磁共振成像（rs-fMRI）的大脑网络一直是发现候选生物标志物的流行工具。具体来说，rs-fMRI 数据的独立成分分析 (ICA) 是研究大脑网络的强大多变量技术。然而，ICA 衍生的大脑网络在异质临床人群中的重复性不高，可能会表现出组间的平均统计分离，但在个体受试者水平上没有足够的区分能力。我们假设，在临床组和对照组的受试者中分别最具可重复性的功能性大脑网络，但当两组合并时则不然，即使在个体受试者水平上，也可能具有有效区分各组之间的能力。在这项研究中，我们提出了 DisConICA 或“ Discover Confirm独立成分分析”，这是一个软件包，它实现了支持我们假设的方法。它依赖于一种“发现-确认”方法，该方法基于使用 gRAICAR（通过再现性进行广义排名和平均独立成分分析）算法对从 rs-fMRI（发现阶段）获得的独立成分（代表大脑网络）的再现性进行评估，然后通过对这些组件进行无监督聚类分析来评估它们区分群体的能力（确认阶段）。 我们的软件包的独特之处在于它能够与其他软件包（例如SPM和FSL）无缝对接，因此所有利用其他软件功能的相关分析都可以在我们的软件包中进行，从而提供一站式软件解决方案原始 DICOM 图像到最终结果。我们使用从伊拉克和阿富汗战争返回的美国陆军士兵获得的 rs-fMRI 数据展示我们的软件，这些士兵在临床上分为以下几组：PTSD（创伤后应激障碍）、共病 PCS（脑震荡后综合症）+ PTSD 和匹配健康的战斗控制。该软件包以及测试数据集可从 https://bitbucket.org/masauburn/disconica 下载。