ImageNomer：功能连接与组学分析工具的描述及识别种族混杂因素的案例研究
ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
AI总结：
ImageNomer是一个基于Python的GUI工具，用于可视化分析fMRI功能连接和基因组数据，通过识别PNC数据集中种族对成就预测的强混杂效应，展示了其在混杂因素检测中的优势。
摘要：
大多数用于分析基于fMRI的功能连接（FC）和基因组数据的软件包都采用编程语言接口，缺乏易于导航的图形用户界面（GUI）前端。这加剧了这类数据中存在的两个问题：人口统计学混杂因素以及在高维特征面前的质量控制。原因在于，使用编程接口来创建识别数据集中所有相关性、混杂效应或质量控制问题所需的所有可视化既缓慢又繁琐。为解决这一问题，我们开发了ImageNomer，一个数据可视化和分析工具，允许检查受试者级别和队列级别的人口统计学、基因组和影像特征。该软件基于Python，在自包含的Docker镜像中运行，并包含基于浏览器的GUI前端。我们通过识别费城神经发育队列（PNC）数据集中预测成就分数时的一个意外种族混杂因素，展示了ImageNomer的实用性。过去，许多研究试图使用FC来识别fMRI中与成就相关的特征。使用ImageNomer，我们发现种族存在明显的潜在混杂效应。通过ImageNomer软件中的相关性分析，我们表明与广泛成就测验（WRAT）分数相关的FC实际上与种族的相关性更高。进一步调查，我们发现尽管FC和SNP（基因组）特征都能解释WRAT分数变异的10-15%，但在控制种族后，这种预测能力消失了。在这项工作中，我们展示了ImageNomer GUI工具在数据探索和混杂因素检测方面的优势。此外，这项工作将种族确定为FC数据中的一个强混杂因素，并对在健康青少年的fMRI和SNP数据中找到无偏的成就相关特征的可能性提出了质疑。
英文摘要：
Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer, we find a clear potential for confounding effects of race. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15\% of WRAT score variation, this predictive ability disappears when controlling for race. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.
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