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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

基本信息

批准号：
9534617
负责人：
SRIKANTAN S. NAGARAJAN
金额：
$ 41.63万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-30 至 2020-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9534617
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

项目摘要

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.

项目总结/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如何产生其功能。功能行为，以及前者的损害如何导致功能障碍和疾病。的快速进步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 (via弥散MRI、纤维束成像）。传统的单神经元或局部电路的非线性数值模拟外推到宏观脑动力学是一个挑战，需要确定性的脑网络模型可以跨模式和规模集成。我们提出了一个雄心勃勃的多尺度，简约和分析基于谱图理论的脑功能模型。提出了一种基于图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现和共享神经科学家和临床医生可以使用它来扰乱结构并产生假设关于刺激和疾病条件下的功能障碍。这一提议背后的关键见解是，大脑的新兴宏观行为本质上是确定性，并由网络“本征模式”支撑。我们将开发神经动力学的图形模型可以通过简单的方程而不是通过数值模拟来分析。这些模型将在最小的和简单的，线性的，只要合适。最后的交付成果是一个Network Dynamics 用于实验性地询问大脑功能和功能障碍。相关性神经和精神疾病构成了当今压倒性的疾病负担，特别是在一个人口迅速老龄化。一个有效的从结构预测大脑功能的模型将提供一个关键的工具，了解并战胜这些疾病