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

兴奋-抑制失衡的准确和个性化预测

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.