Collaborative Research: Bayesian Approaches for Inference on Brain Connectivity

合作研究：大脑连通性推理的贝叶斯方法

• 批准号: 1659925
• 负责人: Marina Vannucci
• 金额: $ 23万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659925&HistoricalAwards=false
• 关键词: Collaborative; Research; Bayesian; Approaches; Inference

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.

该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脑功能模式的临床相关见解的统计方法。 这项研究将进一步了解大脑的不同区域如何在任务期间或休息时相互作用并共享信息。 待开发的统计方法将有可能影响统计学和神经影像学，并将普遍适用于在受试者组中测量多种类型神经影像学数据的研究。从社会的角度来看，获得的知识将指导临床医生选择最佳的靶向治疗，以提高个人的生活质量。该项目将包括对研究生的教育和培训活动。研究结果将被传播到研究界，并用于进一步跨学科的合作努力，在神经影像学。将开发软件和代码并存放在公共资料库中，将开发的新统计方法将整合从多种成像模式收集的关于各组主题的信息。拟议的研究的一个特别重点是表征大脑功能的异质性内和受试者之间。这项研究将产生灵活的贝叶斯统计方法，可以跨学科共享信息，并考虑到大脑结构和功能机制的现有知识。新的整合时空模型将允许大脑网络中存在高度连接和持久的枢纽。动态图形模型方法将增加对功能性大脑连接的动态性质以及当受试者完成任务时连接如何被破坏的理解。研究人员将把新方法应用于神经系统疾病（癫痫或精神分裂症）受试者的成像数据和健康个体的数据，这些数据将作为对照。了解大脑连接体异常在各种神经系统疾病中的作用一直是连接研究的主要焦点。作为对照的健康个体的数据的比较分析将允许识别跨受试者组的连接差异以及它们如何影响多个认知领域。

项目成果

NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data

• DOI: 10.1007/s12561-017-9205-0
• 发表时间: 2019-04-01
• 期刊: STATISTICS IN BIOSCIENCES
• 影响因子: 1
• 作者: Kook, Jeong Hwan;Guindani, Michele;Vannucci, Marina
• 通讯作者: Vannucci, Marina

Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity.

• DOI: 10.1371/journal.pone.0190220
• 发表时间: 2018
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Chiang S;Vankov ER;Yeh HJ;Guindani M;Vannucci M;Haneef Z;Stern JM
• 通讯作者: Stern JM

Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.

• DOI: 10.1002/hbm.23456
• 发表时间: 2017-03
• 期刊: Human brain mapping
• 影响因子: 4.8
• 作者: Chiang S;Guindani M;Yeh HJ;Haneef Z;Stern JM;Vannucci M
• 通讯作者: Vannucci M

A Hierarchical Bayesian Model for the Identification of PET Markers Associated to the Prediction of Surgical Outcome after Anterior Temporal Lobe Resection.

• DOI: 10.3389/fnins.2017.00669
• 发表时间: 2017
• 期刊: Frontiers in neuroscience
• 影响因子: 4.3
• 作者: Chiang S;Guindani M;Yeh HJ;Dewar S;Haneef Z;Stern JM;Vannucci M
• 通讯作者: Vannucci M

Prospective validation study of an epilepsy seizure risk system for outpatient evaluation

• DOI: 10.1111/epi.16397
• 发表时间: 2019-12-02
• 期刊: EPILEPSIA
• 影响因子: 5.6
• 作者: Chiang, Sharon;Goldenholz, Daniel M.;Stern, John M.
• 通讯作者: Stern, John M.