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

第一部分：在我们的初步分析中，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接。为了测试我们的结果，我们使用多变量分类方法分析了相同的数据，这证实了我们的新方法选择的大多数连通性特征。正如预期的那样，这两项比较都显示出精神分裂症患者和健康无关对照组之间在大脑连接方面的广泛差异。更不同寻常的是，他们还揭示了病例和他们的健康兄弟姐妹之间出人意料的巨大差异。 第二部分。我们现在的目标是改进我们的多序列混合模型，用于分析一系列数据点，以便在我们的测试案例中，当疾病症状或治疗药物效果明显时预测临界点。在广泛的模拟测试中，我们的混合模型产生了令人鼓舞的结果。因此，我们将继续改进我们的模型，使其在处理缺失数据方面更加灵活，并将使用真实数据测试其可行性，包括大规模STAR*D研究中对被诊断为抑郁障碍的人的数据集。

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. Part 2. 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.