MRS validation of computational metabolic modeling of human brain function to determine energetic disruptions underlying fMRI-derived functional connectivity in degenerative or psychiatric disorders

静息状态的功能磁共振成像表明，静息状态的人脑中存在高功能连接的中枢，例如 默认模式网络(DMN)，与其他地区相比。在这份提案中，我们将直接评估 代谢在支持高连接方面的作用，并评估代谢功能障碍是否会导致连接 在神经退行性(如衰老、阿尔茨海默氏症)和神经精神障碍中可见DMN中枢的减少。 健康人脑的功能连接的复杂性要求非常高的能量需求， 由葡萄糖氧化产生的高ATP(CMRglc(Ox))所满足的需求。使用H[C]MRS，我们发现 1 13 神经元CMRglc(Ox)与谷氨酸能神经传递呈线性关系，静息状态下大部分脑区CMRglc(Ox)与谷氨酸能神经传递呈线性关系 皮质的能量生产主要用于信号传递。然而，有相当一部分的精力投入到 内务需要，如突触发生和维持膜电位。鉴于……的紧密联系 能量学和信号我们使用定量PET成像令人惊讶地发现，DMN中的总CMRglc(Ox) 类似于fMRI衍生连接性较低的区域。 对这一悖论的一个潜在解释是，与其他皮质区域相比，DMN中的中枢具有更大的 用于信令的总能量的一小部分，而不是用于非信令的部分，从而使DMN集线器 容易受到功能能量故障的影响。或者，已经提出了更高的非信令需求(例如， 突触重构)在DMN中存在。回答这个对口译有重大影响的新奇问题 静息状态的fMRI数据，并研究能量代谢和组织组成障碍如何影响 功能，因此需要新的测量和计算工具。 为了解决这个挑战，我们将开发一个计算模型来计算信令和非信令 能源成本。该模型使用了来自单个受试者的组织成分数据，这些数据来自于高密度脂蛋白。 分辨率核磁共振。该模型将通过与1H[13C]的比较在啮齿动物模型和人类身上进行验证。 MRS，它可以唯一地测量神经能量学的信号和非信号成分。这个 将在高功能连接性中测量和计算信令与非信令的相对比率 在健康的年轻人和老年人中，DMN的区域和对照的低连接性皮质区域。我们 假设功能连通性高的地区将拥有更大比例的能源生产 致力于发出信号，这一比例将随着年龄的增长而下降。一旦计算预算模型 经过开发和验证，它将为研究皮质的变化提供一个强大的非侵入性工具 导致功能磁共振成像连接性丧失的能量学和组织成分，以及潜在的 评估预后和治疗的新的临床生物标志物。

Resting-state fMRI has implicated hubs of high functional connectivity in the resting human brain, e.g., the default mode network (DMN), compared to other regions. In this proposal we will directly assess the role of metabolism in supporting high connectivity and assess whether metabolic dysfunction leads to connectivity reduction in DMN hubs seen in neurodegenrative (e.g., aging, Alzheimer's) and neuropsychiatric disorders. The complex nature of functional connectivity in healthy human brain has very high-energy demands, a need that is met by high ATP yielded from glucose oxidation (CMRglc(ox)). Using H[ C] MRS, we found that 1 13 neuronal CMRglc(ox) changes linearly with glutamatergic neurotransmission and that at rest most of the cerebral cortex's energy production is devoted to signaling. However there is a significant fraction of energy devoted to housekeeping needs such as synaptogenesis and maintaining membrane potentials. Given the tight link of energetics and signaling we found surprisingly, using quantitative PET imaging, that total CMRglc(ox) in the DMN is similar to regions with lower fMRI-derived connectivity. A potential explanation for this paradox is that the hubs in DMN vs. other cortical regions have a greater fraction of their total energy devoted to signaling than to nonsignaling, thus making the DMN hubs more vulnerable to functional energy failure. Alternatively it has been proposed that higher nonsignaling needs (e.g., synaptic remodeling) is present in DMN. To answer this novel question with large implications for interpreting resting-state fMRI data and to study how dysfunction of energy metabolism and tissue composition impacts function, there is a need for novel measurement and computational tools. To address this challenge we will develop a computational model to calculate signaling and nonsignaling energy costs. The model uses data from individual subjects on tissue composition obtained from high- resolution MRI. The model will be validated in both a rodent model and humans by comparison with 1H[13C] MRS, which can uniquely measure the signaling and nonsignaling components of neuroenergetics. The relative ratios of signaling to nonsignaling will be measured and calculated in high functional connectivity regions of the DMN and control low connectivity cortical regions in healthy young and elderly adults. We hypothesize that regions of high functional connectivity will have a greater fraction of energy production devoted to signaling and that this fraction will decline with age. Once the computational budget model is developed, and validated, it will provide a powerful noninvasive tool for studying alterations in cortical energetics and tissue composition that lead to loss of fMRI-derived connectivity as well as potentially as a novel clinical biomarker for assessing prognosis and treatment.