NCS-FO: Collaborative Research: Individual variability in human brain connectivity, modeled using multi-scale dynamics under energy constraints

NCS-FO：协作研究：人脑连接的个体差异，在能量限制下使用多尺度动力学建模

• 批准号: 1533257
• 负责人: Lilianne Mujica-Parodi
• 金额: $ 15万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533257&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Individual

Recent years have witnessed an explosion of interest in human brain connectivity and its relationship to brain-based disease. Functional connectivity analyses in neuroimaging have taken three general forms: cross-correlations, weighting of directed connections, and graph-theoretic approaches. Graph-theoretic measures, in particular, provide valuable insights into network features at the time of imaging. Yet, they cannot identify how the brain came to have those features, nor can they inform estimation of future network evolution. Neurological and psychiatric illnesses tend to have degenerative or oscillatory time-courses that range over decades; thus, network evolution will be critical to understanding why two individuals with the same diagnosis show markedly distinct developmental onsets and prognoses. At the most fundamental level, clinical neuroscience currently lacks the tools for probing how biological constraints imposed upon synapses impact functional connectivity patterns. These constraints include, among others: limited energy resources as per the aggregate energy conversion rate of a finite number of mitochondria, the need to balance excitatory and inhibitory neurotransmitters in order to maintain homeostasis, and neural repair mechanisms (e.g., inflammation, MMP-9). Our long-range goal is to develop these tools, focusing first upon energy constraints across synaptic-hemodynamic scales, for three strategic reasons. 1) Glycemic load is implicated in many neurological diseases, including epilepsy, brain cancer, and dementia. 2) Energy utilization is easy to manipulate experimentally through diet, and to quantify via CO-2 monitoring, with protocols that permit translation to/from animal models for multi-scale modeling. 3) Recent findings link neural connectivity to metabolic expenditure. In the short-term, we focus upon establishing feasibility for three critical principles in preparation for the proposed work. First, we will conduct a pilot neuroimaging study (36 scans; N=12, under three conditions) to establish that our proposed experimental manipulation of energy supply and demand provokes reorganization of brain networks. Second, we aim to bridge scales: to demonstrate how agent-based simulations of point-neurons can incorporate network structure imposed at the level of human neuroimaging, and evolve as a function of changing inputs (energy supply, demand). Third, we propose to develop/adapt methods required to mathematically characterize dynamic networks for both fMRI data and simulations. This fundamental work will position us to conduct future research on modeling of metabolic processes as a function of synapses, glia, and mitochondria, and to use these simulations to predict individual variability of fMRI results as a function of neural energy consumption.

近年来，人们对人脑连接及其与脑疾病的关系的兴趣激增。神经影像学中的功能连接分析有三种一般形式：互相关、有向连接加权和图论方法。特别是图论测量，在成像时为网络特征提供了有价值的见解。然而，他们无法确定大脑是如何拥有这些特征的，也无法为未来网络进化的估计提供信息。神经系统和精神疾病往往具有数十年的退行性或振荡性时间过程;因此，网络进化对于理解为什么具有相同诊断的两个人表现出明显不同的发育发作和发育异常至关重要。在最基本的层面上，临床神经科学目前缺乏工具来探索施加在突触上的生物约束如何影响功能连接模式。这些限制包括，除其他外：根据有限数量的线粒体的总能量转化率的有限能量资源，平衡兴奋性和抑制性神经递质以维持稳态的需要，以及神经修复机制（例如，炎症，MMP-9）。我们的长期目标是开发这些工具，首先关注突触-血流动力学尺度上的能量约束，有三个战略原因。1)许多神经系统疾病，包括癫痫、脑癌和痴呆，都与糖负荷有关。2)能量利用很容易通过饮食进行实验操作，并通过CO-2监测进行量化，其协议允许翻译到/从动物模型进行多尺度建模。3)最近的发现将神经连接与代谢消耗联系起来。 在短期内，我们的重点是确定三个关键原则的可行性，为拟议的工作做准备。 首先，我们将进行一项试点神经影像学研究（36次扫描; N=12，在三种条件下），以确定我们提出的能量供应和需求的实验操纵引起大脑网络的重组。 其次，我们的目标是桥接尺度：演示点神经元的基于代理的模拟如何结合在人类神经成像水平上施加的网络结构，并作为不断变化的输入（能源供应，需求）的函数而发展。第三，我们建议开发/调整所需的方法来数学表征动态网络的fMRI数据和模拟。这项基础性工作将使我们能够进行未来的研究，对代谢过程建模作为突触，神经胶质细胞和线粒体的功能，并使用这些模拟来预测个体差异的功能磁共振成像结果作为神经能量消耗的函数。