Multimodal modeling framework for fusing structural and functional connectome data

用于融合结构和功能连接组数据的多模态建模框架

• 批准号: 9700764
• 负责人: SRIKANTAN S. NAGARAJAN
• 金额: $ 23.01万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9700764
• 关键词: Multimodal; modeling; framework; fusing; structural

PROJECT SUMMARY / ABSTRACT Project Summary A key goal of computational neuroscience is to discover how the brain’s structural organization produces its functional behavior, and how impairment of the former causes dysfunction and disease. Rapid advances in neural measurement technologies are finally beginning to enable in vivo measurements of large-scale functional organization (via EEG, MEG, fMRI, PET, optical imaging) and the underlying structural connectivity architecture (via diffusion MRI, tractography). Traditional non-linear numerical simulations of single neurons or local circuits is challenging to extrapolate to macroscopic brain dynamics, and deterministic brain network models are needed that can integrate across modalities and scales. We propose an ambitious multi-scale, parsimonious and analytic model of brain function based on spectral graph theory. Bayesian inference using graphical modeling is proposed to deduce structure from function. These algorithms will be implemented and shared via a Network Dynamics Workbench that can be used by neuroscientists and clinicians to perturb structure and generate hypotheses regarding functional impairment in stimulus and disease conditions. The key insight underlying this proposal is that the emergent macroscopic behavior of the brain is essentially deterministic, and is undergirded by network “eigen-modes”. We will develop graph models of neural dynamics that are accessible analytically by simple equations rather than via numerical simulations. These models will be minimal and simple, and linear wherever appropriate. The final deliverable is a Network Dynamics Workbench for experimentally interrogating brain function and dysfunction. Relevance Neurological and psychiatric disorders constitute an overwhelming burden of disease today, especially in a rapidly aging population. A validated model of brain function predicted from structure will provide a critical tool in understanding and fighting these disorders.

项目摘要/摘要 项目摘要 计算神经科学的一个关键目标是发现大脑的结构组织是如何产生其 功能性行为，以及前者的损害如何导致功能障碍和疾病。快速发展的 神经测量技术终于开始能够在体内测量大规模的功能 组织(通过EEG、MEG、fMRI、PET、光学成像)和底层结构连接架构 (通过弥散磁共振成像、纤维束成像)。单个神经元或局部电路的传统非线性数值模拟 对宏观脑动力学进行外推具有挑战性，需要确定性的脑网络模型 可以跨模式和规模进行集成。我们提出了雄心勃勃的多尺度、节俭和分析性的 基于谱图理论的脑功能模型。提出了基于图形化建模的贝叶斯推理方法 从功能中推断结构。这些算法将通过网络动态实施和共享 可供神经学家和临床医生用来扰乱结构和生成假说的工作台 关于刺激和疾病条件下的功能损害。 这一建议背后的关键洞见是，大脑的紧急宏观行为本质上是 确定性，并得到网络“特征模式”的支持。我们将开发神经动力学的图形模型 它们可以通过简单的方程而不是通过数值模拟来解析。这些模型将是 最小和简单，适当的地方是线性的。最终交付内容是网络动态工作台 对大脑功能和功能障碍进行试验性审问。 相关性 神经和精神障碍构成了当今疾病的压倒性负担，特别是在 人口迅速老龄化。根据结构预测的经验证的大脑功能模型将为 理解和抗击这些障碍。