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.

准确和个性化的激发抑制不平衡预测 使用数据驱动的神经模型的阿尔茨海默氏病 项目摘要/摘要 阿尔茨海默氏病（AD）是痴呆症的最常见形式，其特征是进行性和不可逆转 认知能力下降。尽管对美国医疗保健系统产生了毁灭性的影响，但其精确的病因和有效 仍缺乏治疗选择。最近的动物研究和人类神经影像学数据表明中断 AD中的兴奋性抑制（E-I）平衡可能是重要的病理生理和治疗靶点。 但是，功能MRI（fMRI）中现有的分析技术不允许在细胞和 电路水平。为了克服这些局限性，我们开发了多尺神经模型反演（MNMI） 基于静止状态fMRI（RS-FMRI）和扩散MRI（DMRI）的框架以检测电路级E-I不平衡 在疾病的神经元网络中。该项目的目的是验证和完善 MNMI框架，用于AD中E-I不平衡的准确和个性化估计。 为了实现这一目标，我们将追求两个具体的目标。在AIM 1中，我们将预测广告中的E-I平衡中断 使用RS-FMRI的MNMI鼠标模型。我们将首先在野生型（WT）控制和 3XTG-AD（TG）小鼠。然后，我们将使用MNMI模型来预测基于RS-FMRI和 DMRI并在AD小鼠中衍生出E-I损伤的区域。基于MNMI预测，我们将选择四个大脑 体内光学的区域（TG小鼠中最重要的E-I损伤以及一个控制区域中最重要的E-I损伤） 测量。在AIM 2中，我们将使用体内光学E-I测量值验证MNMI模型预测 和行为测试。我们将首先执行同时的中兴传染性企业和光纤光度法（在选定的四个） 站点）在另一组年龄匹配的WT和TG小鼠中作为AIM 1。然后，我们将验证模型预测 如果模型预测偏离 经验E-I措施。最后，我们将检查TG小鼠中的E-I不平衡是否与认知有关 障碍。我们研究的总体目标是结合计算建模，fMRI和 尖端的神经调节和记录工具来描述病理网络活动，阐明 基础电路机制，并为AD开发更有效的治疗方式。成功的 该项目的实施将导致一个创新的计算框架，该框架有助于识别 使用非侵入性MRI的病理E-I不平衡，并促进了新的诊断技术的发展 以及在早期干预中检测和恢复E-I失衡的个性化治疗方法。拟议的工作 对于神经科学，神经影像，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，以识别疾病健康和不平衡的E-I平衡。