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

静息态功能磁共振成像暗示了静息态人脑中高功能连接的中枢，例如， 与其他区域相比，默认模式网络 (DMN)。在本提案中，我们将直接评估 代谢支持高连接性并评估代谢功能障碍是否导致连接性 在神经退行性疾病（例如衰老、阿尔茨海默病）和神经精神疾病中观察到 DMN 中枢的减少。 健康人脑功能连接的复杂性具有非常高的能量需求， 葡萄糖氧化 (CMRglc(ox)) 产生的高 ATP 可以满足这一需求。使用 H[C] MRS，我们发现 1 13 神经元 CMRglc(ox) 随谷氨酸能神经传递线性变化，并且在休息时大部分大脑 皮层的能量产生专门用于信号传递。然而，有很大一部分能量投入到 管家需求，例如突触发生和维持膜电位。鉴于紧密联系 我们使用定量 PET 成像令人惊讶地发现，DMN 中的总 CMRglc(ox) 与功能磁共振成像衍生连接性较低的区域相似。 对于这一悖论的一个可能的解释是，DMN 中的中枢与其他皮质区域相比具有更大的 其总能量中用于信令的部分比用于非信令的部分少，从而使 DMN 集线器更 容易遭受功能性能量衰竭。或者，有人提出更高的非信号需求（例如， 突触重塑）存在于 DMN 中。回答这个对口译有重大影响的新问题 静息态功能磁共振成像数据并研究能量代谢和组织成分功能障碍如何影响 功能，需要新颖的测量和计算工具。 为了应对这一挑战，我们将开发一个计算模型来计算信号和非信号 能源成本。该模型使用来自个体受试者的组织成分数据，这些数据是从高 分辨率 MRI。通过与 1H[13C] 进行比较，该模型将在啮齿动物模型和人类中得到验证 MRS，可以独特地测量神经能量学的信号和非信号成分。的 将在高功能连接中测量和计算信号与非信号的相对比率 DMN 区域并控制健康年轻人和老年人的低连通性皮质区域。我们 假设功能连接性高的区域将拥有更大比例的能源生产 致力于信号传递，并且这一比例会随着年龄的增长而下降。一旦计算预算模型是 开发和验证后，它将为研究皮质变化提供强大的非侵入性工具 能量学和组织成分导致功能磁共振成像衍生的连接性丧失以及潜在的 用于评估预后和治疗的新型临床生物标志物。

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.