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

使用数据驱动的神经模型准确、个性化地预测阿尔茨海默病的兴奋抑制失衡

• 批准号: 10727356
• 负责人: GUOSHI LI
• 金额: $ 42.76万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10727356
• 关键词: 3xTg-AD mouse; Age; Alzheimer' s Disease; Alzheimer' s disease model; Amyloid; Animals; Appearance; Area; Biological Models; Blood Vessels; Brain; Brain Mapping; Brain region; Clinical; Cognitive; Computer Models; Computer Simulation; Data; Dementia; Development; Diagnostic Procedure; Diffusion Magnetic Resonance Imaging; Disease; Early Diagnosis; Early Intervention; Energy consumption; Equilibrium; Etiology; Exhibits; Fiber; Functional Magnetic Resonance Imaging; Goals; Health; Healthcare Systems; Human; Hyperemia; Impaired cognition; Impairment; Individual; Magnetic Resonance Imaging; Maps; Measurement; Measures; Memory Loss; Mental disorders; Methodology; Methods; Modality; Modeling; Mus; Neurodegenerative Disorders; Neurologic; Neurons; Neurosciences; Neurosciences Research; Optics; Pathologic; Photometry; Psyche structure; Psychiatry; Psychology; Qualifying; Research; Research Personnel; Rest; Rodent; Rodent Model; Role; Running; Signal Transduction; Site; Synaptic Transmission; System; Techniques; Therapeutic Intervention; Time; Validation; Work; behavior test; cell type; community setting; computer framework; effective intervention; effective therapy; graph theory; health knowledge; improved; in vivo; independent component analysis; inhibitory neuron; innovation; mouse model; multidisciplinary; nervous system disorder; neural circuit; neural model; neuroimaging; neuronal circuit disruption; neuroregulation; novel; novel diagnostics; personalized medicine; personalized predictions; pre-clinical; predictive modeling; public health relevance; symptomatology; therapeutic target; tool; translational impact

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.

兴奋抑制失衡的个体化预测 使用数据驱动神经模型的阿尔茨海默病 项目总结/摘要 阿尔茨海默病（Alzheimer's disease，AD）是最常见的痴呆形式，其特征是进行性和不可逆的 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，但其确切的病因和有效的治疗方法， 治疗方案仍然缺乏。最近的动物研究和人类神经成像数据表明， 兴奋-抑制（E-I）平衡的改变可能成为AD重要的病理生理和治疗靶点。 然而，功能性MRI（fMRI）中的现有分析技术不允许在细胞和组织上进行E-I映射。 电路水平。为了克服这些局限性，我们开发了多尺度神经模型反演（MNMI） 基于静息态fMRI（rs-fMRI）和扩散MRI（dMRI）的检测电路级E-I失衡的框架 神经元网络中潜在的疾病状况。本项目的目标是验证和完善 MNMI框架，用于准确和个性化地估计AD中的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平衡的破坏 小鼠模型，采用rs-fMRI的MNMI。我们将首先对野生型（WT）对照进行ZTE-fMRI和dMRI， 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， dMRI和导出AD小鼠中具有E-I损伤的区域。根据MNMI的预测，我们将选择四个大脑 区域（三个在TG小鼠中具有最显著的E-I损伤加上一个对照区域）用于体内光学成像。 测量.在目标2中，我们将使用体内光学E-I测量来验证MNMI模型预测 和行为测试我们将首先进行同步ZTE-fMRI和纤维光度测定（在四个选定的 位点）在不同组的年龄匹配的WT和TG小鼠中作为Aim 1。然后，我们将验证模型预测 在个体和群体层面，如果模型预测偏离 经验E-I测量。最后，我们将研究TG小鼠的E-I失衡是否与认知功能相关。 损伤我们研究的首要目标是将联合收割机计算建模、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 该项目的实施将导致一个创新的计算框架，用于识别 病理性E-I失衡，并促进新诊断技术的发展 和个性化治疗，用于早期干预中检测和恢复E-I失衡。拟议工作 对神经科学、神经影像学、精神病学和心理学领域也具有更广泛的意义， 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。