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：
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
项目类别：
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

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.

项目摘要 /摘要项目摘要计算神经科学的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引起功能障碍和疾病。快速进步神经测量技术终于开始实现大规模功能的体内测量组织（通过EEG，MEG，FMRI，PET，光学成像）和潜在的结构连接体系结构（通过扩散MRI，拖拉学）。单个神经元或局部电路的传统非线性数值模拟要推断出宏观的大脑动力学，并且需要确定性的大脑网络模型具有挑战性可以跨模态和量表整合。我们提出了一个雄心勃勃的多尺度，简约和分析性的基于光谱图理论的大脑功能模型。提出了使用图形建模的贝叶斯推断从功能中推导结构。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的工作台来干扰结构并产生假设刺激和疾病状况中有关功能障碍。该提议的基础的关键见解是，大脑的紧急宏观行为本质上是确定性，并受到网络“特征模式”的影响。我们将开发神经动力学的图形模型可以通过简单方程而不是通过数值模拟在分析上访问。这些模型将是最小而简单，并在适当的地方进行线性。最后交付是网络动态工作台用于实验询问大脑功能和功能障碍。关联神经和精神疾病构成了当今疾病的压倒性燃烧，尤其是在人口迅速老龄化。通过结构预测的经过验证的大脑功能模型将为您提供关键的工具理解和战斗这些疾病。