Analyzing fMRI and next-generation-sequenced data for schizophrenia biomarkers

分析精神分裂症生物标志物的功能磁共振成像和下一代测序数据

• 批准号: 8745758
• 负责人: Yin Yao
• 金额: $ 8.69万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8745758
• 关键词: Accounting; Affect; Age; Agreement; Anatomy; Biological Markers; Brain; Brain region; China; Classification; Complex; Computer software; Data; Data Set; Depressive disorder; Diagnosis; Disease; Environmental Risk Factor; Extramural Activities; Functional Magnetic Resonance Imaging; Gender; Genetic; Individual; Influentials; Institutional Review Boards; Measurement; Methods; Modeling; Patients; Persons; Pharmaceutical Preparations; Phenotype; Province; Research Personnel; Rest; Schedule; Schizophrenia; Series; Siblings; Statistical Methods; Statistical Models; Symptoms; Techniques; Testing; Therapeutic; Time; Universities; Validation; design; disease phenotype; flexibility; human disease; improved; meetings; method development; next generation sequencing; novel; simulation

Part 1. For our initial analysis, we parcellated the whole brain into 116 anatomic regions and 1000 functional networks, then analyzed connectivity within and among brain regions and networks. To test our results, we analyzed the same data using a multivariate classification method, which confirmed most of the connectivity features our new method selected. As expected, both comparisons showed widespread differences in brain connectivity between schizophrenia cases and healthy unrelated controls. More unusually, they also revealed unexpectedly large differences between cases and their healthy siblings. This project will continue in FY2014, when (in accordance with formal IRB approval and detailed collaborative agreement) extramural collaborators are scheduled to provide us with new datasets for analysis. Part 2. Following our initial analytic method development, proof-of-concept simulations, and real data validation tests , we now aim to refine our multi-sequenced mixture model for the analysis of series of data points in order to predict the tipping point when, in our test case, disease symptoms or therapeutic drug effects become evident. In extensive simulation tests, our mixture model has produced encouraging results. We will therefore continue to improve our model by making it more flexible in terms of handling missing data and will test its feasibility using real data, including data sets from the massive STAR*D study of persons diagnosed with depressive disorders.

第1部分。在最初的分析中，我们将整个大脑划分为116个解剖区域和1000个功能网络，然后分析了大脑区域和网络内部和之间的连通性。为了测试我们的结果，我们使用多元分类方法分析了相同的数据，这证实了我们新方法选择的大多数连通性特征。正如预期的那样，这两项比较都显示了精神分裂症患者和健康的非相关对照组之间大脑连通性的广泛差异。更不寻常的是，他们还揭示了患者与健康同胞之间出人意料的巨大差异。该项目将在2014财年继续进行，届时（根据IRB的正式批准和详细的合作协议）将安排外部合作者为我们提供新的数据集进行分析。