Collaborative Research: Bayesian Approaches for Inference on Brain Connectivity

这一合作研究项目将开发新的统计方法，用于分析和解释脑成像数据。需要改进信号检测并导致对大脑功能模式的临床相关洞察的统计方法。这项研究将促进对大脑不同区域在任务或休息时如何相互作用和共享信息的理解。将要开发的统计方法将有可能对统计学和神经成像产生影响，并将普遍适用于在受试组中测量多种类型的神经成像数据的研究。从社会的角度来看，所获得的知识将指导临床医生选择最有针对性的治疗方法，以改善个人的生活质量。该项目将包括针对研究生的教育和培训活动。研究结果将被传播给研究界，并用于进一步开展神经成像领域的跨学科合作。将开发软件和代码并存放在公共储存库中。将开发新的统计方法，将收集到的多个受试者组的成像模式提供的信息整合在一起。这项拟议研究的一个特别重点是描述受试者内部和之间大脑功能的异质性。这项研究将产生灵活的贝叶斯统计方法，这些方法可以在受试者之间共享信息，并考虑到关于大脑结构和功能机制的现有知识。新的综合时空模型将允许在大脑网络中存在高度连接和持久的中枢。动态图形模型方法将增加对大脑功能连接的动态本质的理解，以及当受试者完成任务时连接是如何中断的。研究人员将把新方法应用于神经性疾病(癫痫或精神分裂症)受试者的成像数据，以及将作为对照的健康人的数据。了解脑连接体异常在各种神经疾病中的作用一直是连接性研究的主要焦点。对作为对照的健康个体的数据进行比较分析，将能够识别不同受试者群体之间连接的差异，以及它们如何影响多个认知域。

This collaborative research project will develop new statistical methods for the analysis and interpretation of brain imaging data. Statistical methods that improve signal detection and that lead to clinically relevant insights into the patterns of brain functions are needed. This research will advance understanding of how the different regions of the brain interact and share information with each other during a task or at rest. The statistical methods to be developed will have the potential to impact both statistics and neuroimaging and will apply generally to studies where multiple types of neuroimaging data are measured on groups of subjects. From a societal perspective, the acquired knowledge will guide clinicians in the selection of optimally targeted treatments to improve the quality of life of individuals. The project will include educational and training activities for graduate students. Findings will be disseminated to the research community and used to further interdisciplinary collaborative efforts in neuroimaging. Software and code will be developed and deposited in public repositories.The new statistical methods to be developed will integrate the information provided by multiple imaging modalities collected on groups of subjects. A particular focus of the proposed research is to characterize the heterogeneity of brain functioning both within and between subjects. This research will produce flexible Bayesian statistical methods that can share information across subjects and take into account available knowledge on brain structure and functional mechanisms. New integrative spatio-temporal models will allow for the presence of highly connected and persistent hubs in the brain networks. Dynamic graphical model approaches will increase understanding of the dynamic nature of functional brain connectivity and how connectivity is disrupted when subjects are completing tasks. The investigators will apply the new methods to imaging data from subjects with a neurological disorder (epilepsy or schizophrenia) and data from healthy individuals who will serve as controls. Understanding the role that abnormalities in the brain connectome play in various neurological diseases has been a major focus in connectivity studies. Comparative analyses of data from healthy individuals serving as controls will allow the identification of differences in connectivity across groups of subjects and how they affect multiple cognitive domains.