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: 9360098
• 负责人: SRIKANTAN S. NAGARAJAN
• 金额: $ 18.48万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 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 2017-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9360098
• 关键词: Address; Age; Algorithms; Anatomy; Architecture; Bayesian Analysis; Behavior; Brain; Classification; Code; Cognitive; Complex; Corpus Callosum; Coupling; Data; Data Set; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Electroencephalography; Electromagnetics; Equation; Exhibits; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Grant; Graph; Human; Impairment; Individual; Internet; Language; MRI Scans; Magnetic Resonance Imaging; Magnetoencephalography; Measurement; Measures; Memory; Mental disorders; Methods; Modality; Modeling; Multimodal Imaging; Network-based; Neurons; Online Systems; Pathology; Performance; Physiology; Positron-Emission Tomography; Presenile Alzheimer Dementia; Property; Signal Detection Analysis; Specific qualifier value; Stimulus; Structure; Structure-Activity Relationship; Study Subject; Technology; Theoretical model; Time; Validation; Work; Writing; aging population; base; burden of illness; cohort; computational neuroscience; connectome; design; fighting; flexibility; functional disability; graph theory; human data; in vivo; insight; multimodality; nervous system disorder; network models; neural model; neuroimaging; novel; open source; optical imaging; reconstruction; relating to nervous system; response; simulation; spatiotemporal; statistics; tool; tractography

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.

项目概要/摘要 项目概要 计算神经科学的一个关键目标是发现大脑的结构组织如何产生其 功能行为，以及前者的损害如何导致功能障碍和疾病。快速进步 神经测量技术终于开始能够对大规模功能进行体内测量 组织（通过 EEG、MEG、fMRI、PET、光学成像）和底层结构连接架构 （通过扩散 MRI、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 推断宏观大脑动力学具有挑战性，需要确定性大脑网络模型 可以跨模式和规模进行整合。我们提出了一个雄心勃勃的多尺度、简约和分析的 基于谱图理论的脑功能模型。提出使用图形建模的贝叶斯推理 从功能推导出结构。这些算法将通过网络动力学来实现和共享 神经科学家和临床医生可以使用工作台扰乱结构并生成假设 关于刺激和疾病条件下的功能障碍。 这一提议的关键见解是，大脑的宏观宏观行为本质上是 确定性的，并且由网络“本征模式”支撑。我们将开发神经动力学的图模型 可以通过简单的方程而不是数值模拟来分析。这些模型将 最小化和简单，并且在适当的情况下是线性的。最终的交付成果是网络动态工作台 用于通过实验检查大脑功能和功能障碍。 关联 神经和精神疾病构成了当今压倒性的疾病负担，尤其是在 人口迅速老龄化。从结构预测大脑功能的经过验证的模型将提供一个关键工具 了解并对抗这些疾病。