Understanding dynamics of brain network in TDP-43 related neurodegeneration

Mislocalization of TDP-43 is a common pathological feature of several neurodegenerative diseases, including Alzheimer’s disease, amyotrophic lateral sclerosis, and frontotemporal dementia. Many studies support the loss-of-function mechanism caused by TDP-43 nuclear clearance as a major pathogenesis pathway toward neurodegeneration. It is of great importance to understand the dynamics of disease progression in TDP-43 knockout mouse models. Functional microcircuits are at the heart of the information processing capability of the brain. Calcium imaging is a powerful tool to study functional microcircuits. Calcium imaging can generate high-dimensional longitudinal datasets, which are collected at multiple time points over a temporal process (each observation time point is a wave). Longitudinal analysis models the evolving temporal process. However, existing analysis methods are primarily designed to process cross-sectional data, which provides a static view of the brain network. As a result, existing methods have limited capability to model complex dynamic network patterns for high- dimensional data. Lack of advanced longitudinal analysis methods is a bottleneck for using calcium imaging to study the dynamics of brain networks in TDP-43 knockout mouse models. This project seeks to develop a Bayesian computational system to model longitudinal functional microcircuits and use it to examine microcircuit changes in a TDP-43 knockout mouse model. The developed system is referred to as Bayesian Longitudinal Microcircuit Analysis (BLMA). The Specific Aims are: Aim 1. Develop a computational system for longitudinal microcircuit modeling. The proposed system, BLMA, will include these components: preprocessing, microcircuit construction, feature extraction, and Bayesian multivariate mixed modeling. Aim 2. Understand microcircuit changes in a TDP-43 knockout mouse model. We will apply BLMA to an existing longitudinal calcium imaging dataset of pyramidal neurons of the prefrontal cortex (PFC) from awake behaving Tdp-43F/F mice. We will compare the control and knockout groups to determine whether the knockout group exhibits abnormal PFC microcircuit trajectories. This project will develop BLMA to advance the state-of-the-art in data analysis and modeling for longitudinal calcium imaging. It leverages Bayesian machine learning to address critical challenges in longitudinal calcium imaging data analysis: shared information across waves and high dimensionality. This project is innovative because it will develop a novel Bayesian system to model microcircuit changes based on calcium imaging data and delineate a unique brain network mechanism leading to TDP-43 related neurodegeneration. At the completion of this project, we will have delineated a unique brain network mechanism in TDP-43 related neurodegeneration.