PDF(Pubmed)
Abstract：
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. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. 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, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. 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. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. 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.
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与编程语言接口一起使用，缺乏易于导航的GUI前端。这加剧了在这些类型的数据中发现的两个问题:面对高维度的特征,人口统计学混淆和质量控制。原因是使用编程接口来创建识别所有相关性所需的所有必要可视化内容太慢且麻烦，混杂效应，或数据集中的质量控制问题。特别是FC通常每个主题包含成千上万的功能，并且只能使用可视化进行总结和有效探索。为了纠正这种情况，我们开发了ImageNomer,一种数据可视化和分析工具，可以检查受试者水平和队列水平的人口统计，基因组，和成像功能。该软件是基于Python的，在独立的Docker映像中运行，并包含基于浏览器的GUI前端。我们通过在费城神经发育队列（PNC）数据集中预测成就分数时识别意外的种族混淆来证明ImageNomer的有用性，其中包含健康青少年的多任务fMRI和单核苷酸多态性（SNP）数据。在过去,许多研究尝试使用FC来识别功能磁共振成像中与成就相关的特征.使用ImageNomer可视化种族之间成就得分的趋势，如果可以使用FC预测种族，我们发现明显的混淆效应潜力。使用ImageNomer软件中的相关性分析，我们表明，与广泛成就测试（WRAT）得分相关的FC实际上与种族高度相关。进一步调查,我们发现，尽管FC和SNP(基因组)特征都可以占WRAT评分变异的10-15%，当控制种族时，这种预测能力就消失了。我们还使用ImageNomer来研究双相和精神分裂症中间表型网络（BSNIP）数据集中的种族-FC相关性。在这项工作中,我们展示了ImageNomerGUI工具在数据探索和混淆检测方面的优势.此外,这项工作将种族认定为FC数据中的一个强混淆因素,并对在健康青少年的fMRI和SNP数据中发现与成就相关的无偏见特征的可能性产生怀疑.
