Accurate and Individualized Prediction of Excitation-Inhibition Imbalance in Alzheimer's Disease using Data-driven Neural Model

激发-抑制失衡的准确个体化预测 基于数据驱动神经模型的阿尔茨海默病 项目摘要/摘要 阿尔茨海默病(AD)是最常见的痴呆形式，其特点是进行性和不可逆性 认知能力下降。尽管它对美国医疗保健系统造成了毁灭性的影响，但它的确切病因和有效 治疗方法仍然缺乏选择。最近的动物研究和人类神经成像数据表明 兴奋-抑制(E-I)平衡在阿尔茨海默病中可能成为重要的病理生理和治疗靶点。 然而，现有的功能磁共振成像(FMRI)分析技术不允许在细胞和 线路级别。为了克服这些限制，我们发展了一种多尺度神经模型反演(MNMI) 基于静息功能磁共振成像和扩散磁共振成像的电路级E-I失衡检测框架 在疾病背后的神经网络中。该项目的目标是验证和提炼 MNMI框架，用于准确和个性化地估计AD患者的E-I失衡。 为了实现这一目标，我们将追求两个具体目标。在目标1中，我们将预测AD中E-I平衡的中断 采用RS-fMRI的MNMI建立小鼠模型。我们将首先对野生型(WT)对照进行中兴通讯功能磁共振成像和数字磁共振成像 3xTg-AD(TG)小鼠。然后，我们将应用MNMI模型来预测基于rs-fmri的区域E-I平衡和 DMRI和衍生出AD小鼠E-I损伤的区域。根据MNMI预测，我们将选择四个大脑 体内光学区域(三个在TG小鼠中具有最显著的E-I损伤的区域加上一个对照区域) 测量。在目标2中，我们将使用活体光学E-I测量来验证MNMI模型的预测 和行为测试。我们将首先同时执行ZTE-fMRI和光纤光度测量(在选定的四个 在另一组年龄匹配的WT和TG小鼠中)作为目标1。然后我们将验证模型预测 在个体受试者和群体层面，并改进MNMI框架，如果模型预测偏离 经验性E-I测量。最后，我们将检查TG小鼠的E-I失衡是否与认知有关 减损。我们研究的首要目标是将计算建模、功能磁共振成像和 尖端的神经调节和记录工具来描绘病理网络活动，阐明 潜在的电路机制，并开发更有效的治疗AD的方式。成功 该项目的实施将产生一个创新的计算框架，用于确定 无创性MRI检测病理性E-I失衡有助于新诊断技术的发展 早期干预中发现和恢复E-I失衡的个体化治疗。拟议中的工作 对神经科学、神经成像、精神病学和心理学领域也具有更广泛的意义，因为 将开发新的工具，以便能够确定健康中的E-I平衡和疾病中的不平衡。

Accurate and Individualized Prediction of Excitation-Inhibition Imbalance in Alzheimer’s Disease using Data-driven Neural Model Project Summary/Abstract Alzheimer’s disease (AD) is the most common form of dementia characterized by progressive and irreversible cognitive decline. Despite its devastating impacts on the US health care system, its precise etiology and effective treatment options are still lacking. Recent animal studies and human neuroimaging data indicate disrupted excitation-inhibition (E-I) balance in AD which may serve as important pathophysiological and therapeutic target. However, existing analytical techniques in functional MRI (fMRI) do not allow for E-I mapping at cellular and circuit levels. To overcome these limitations, we have developed a Multiscale Neural Model Inversion (MNMI) framework based on resting-state fMRI (rs-fMRI) and diffusion MRI (dMRI) to detect circuit-level E-I imbalance in neuronal networks underlying disease conditions. The goal of this project is to validate and refine the MNMI framework for accurate and individualized estimation of E-I imbalance in AD. To achieve this goal, we will pursue two specific aims. In Aim 1, we will predict disrupted E-I balance in an AD mouse model using MNMI of rs-fMRI. We will first perform ZTE-fMRI and dMRI on wild-type (WT) control and 3xTg-AD (TG) mice. We will then apply the MNMI model to predict regional E-I balance based on rs-fMRI and dMRI and derive areas with E-I impairments in AD mice. Based on MNMI predictions we will select four brain regions (three with the most significant E-I impairments in TG mice plus one control region) for in vivo optical measurements. In Aim 2, we will validate the MNMI model predictions using in vivo optical E-I measurements and behavioral testing. We will first perform simultaneous ZTE-fMRI and fiber photometry (at the four selected sites) in a different set of age-matched WT and TG mice as Aim 1. We will then validate the model predictions at both individual subject and group levels and improve the MNMI framework if model predictions deviate from empirical E-I measures. Lastly, we will examine if the E-I imbalance in TG mice is associated with cognitive impairments. The overarching goal of our research is to combine computational modeling, fMRI, and cutting-edge neuromodulation and recording tools to delineate pathological network activity, elucidate the underlying circuit mechanisms, and develop more effective treatment modalities for AD. Successful implementation of this project will lead to an innovative computational framework that serves to identify pathological E-I imbalance using noninvasive MRI and facilitates the development of new diagnostic technique and personalized treatment for detecting and restoring E-I imbalance in early intervention. The proposed work is also of broader significance to the fields of neuroscience, neuroimaging, psychiatry, and psychology since novel tools will be developed to enable the identification of E-I balance in health and imbalance in diseases.