Wednesday, January 15, 2025
1/15/2025
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.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).