Bayesian Inference of Whole-Brain Directed Networks Using Neuroimaging Data

使用神经影像数据进行全脑定向网络的贝叶斯推理

• 批准号: 2242568
• 负责人: Tingting Zhang
• 金额: $ 27.51万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242568&HistoricalAwards=false
• 关键词: Bayesian; Inference; Whole; Brain; Directed

This research project will develop new statistical models and computationally efficient algorithms to analyze functional magnetic resonance imaging (fMRI) data. The advent of fMRI offers unprecedented opportunities for scientists to study the functional organization of the human brain because fMRI provides non-invasive measurements of the entire human brain's activity with a high spatial resolution. However, the massive size and large noise of fMRI data and the complex functional organizations of human brains pose challenges to scientists. This project will develop efficient fMRI data analysis methods to address these challenges. The new methods will be applied to investigate human brains' functional organizations, functional organization changes during brain development, and the relationship between the human brain and behavior in the population. The project results will contribute to the critical knowledge of the development of both healthy brain functions and risks for mental health challenges. Open-source software that implements the new statistical tools will be developed and made publicly available. The project will provide educational and training opportunities for undergraduate and graduate students.This project will build Bayesian models for whole-brain networks of many subjects based on their fMRI data. The new models will specifically characterize functionally specialized modules of brain regions and connections within and between the modules in whole-brain networks. In addition, the new models offer model flexibility, improved estimation efficiency, and robustness to model error and data noise in characterizing many subjects' whole-brain networks. Efficient computational algorithms will be developed to address the computational challenge in analyzing massive fMRI data and to map many subjects' whole-brain networks simultaneously. The project also will propose scalar-on-network regressions that feature heterogeneous relationships between behavior and brain networks in the population. The investigator will apply the new methods to neuroimaging and behavioral data of many subjects and examine the variation and distribution of the human brain's functional organization and its relationship to human behavior in the population. The project's findings will enhance understanding of the brain, human behavior, the risk for mental health challenges, and the role of the brain in overall health and well-being.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目将开发新的统计模型和计算效率高的算法来分析功能性磁共振成像（fMRI）数据。功能磁共振成像的出现为科学家研究人脑的功能组织提供了前所未有的机会，因为功能磁共振成像提供了高空间分辨率的整个人脑活动的非侵入性测量。然而，fMRI数据的海量和大噪声以及人脑复杂的功能组织给科学家带来了挑战。该项目将开发有效的fMRI数据分析方法来应对这些挑战。新方法将用于研究人脑的功能组织，大脑发育过程中功能组织的变化，以及人脑与人群行为之间的关系。该项目的结果将有助于发展健康的大脑功能和心理健康挑战的风险的关键知识。将开发并公开提供实施新统计工具的开放源码软件。该项目将为本科生和研究生提供教育和培训的机会。该项目将根据他们的fMRI数据为许多受试者建立全脑网络的贝叶斯模型。新模型将具体描述大脑区域的功能专门模块以及全脑网络中模块内部和模块之间的连接。此外，新模型提供了模型灵活性，提高了估计效率，并在表征许多受试者的全脑网络时对模型误差和数据噪声具有鲁棒性。高效的计算算法将被开发出来，以解决在分析大量的功能磁共振成像数据和映射许多受试者的全脑网络同时计算的挑战。该项目还将提出网络上的标量回归，其特征在于人口中行为和大脑网络之间的异质关系。研究人员将把新方法应用于许多受试者的神经成像和行为数据，并研究人类大脑功能组织的变化和分布及其与人类行为的关系。该项目的发现将提高对大脑、人类行为、精神健康挑战的风险以及大脑在整体健康和福祉中的作用的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。