Data_Sheet_1_Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.pdf

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.

本文中，我们构建了一种贝叶斯多模态模型，用于检测生物标志物（或神经标志物），该模型基于静息态功能性和结构数据，并在老年抑郁症组和健康对照组之间进行比较。生物标志物的检测有助于确定治疗干预的目标，以实现对治疗抵抗型患者的最佳治疗效果。在检测生物标志物的过程中，我们量化了结构连通性对功能活动的影响。通过我们的创新方法，在生物标志物搜索过程中，同时生成并测试了成千上万种假设，以控制小样本的假发现率。我们调查并比较了多种现有统计方法，这些方法常用于神经影像数据分析，并与所提出的方法通过模拟进行对比，以展示其卓越的性能。结果通过晚期抑郁症研究生成的一个实时数据集进行展示。我们还探讨了所检测生物标志物在认知功能方面的作用。