UCSF

This study aims to enhance the quality of metabolic data from mouse brain through the application of tMPPCA denoising and Whittacker baseline correction. The goal is to develop a robust pipeline for detecting spectral signal changes using MATLAB, with applicability to various diseases and pre-clinical spectra data. The study employed data collected from Alzheimer's disease mouse models, analyzing different groups with varied drug injections and time points. By comparing key ratios like Lactate/Pyruvate, Bicarbonate/Lactate, and Bicarbonate/Pyruvate obtained from the pipeline with MestreNova software, the pipeline's efficacy was validated. Furthermore, the application of denoising and baseline correction methods was crucial for reliable results. Notably, the pipeline demonstrated remarkable similarity in results with MestreNova software, validating its usability. This pipeline's importance lies in its potential to analyze low signals in diverse slab data. This study's contribution lies in its role in addressing low signal challenges through denoising and baseline correction in the realm of hyperpolarized carbon 13 studies. The conclusion emphasizes the pipeline's robustness and its validation through comparisons. A key focus is the observation of distinctive metabolic shifts in APOE 4 mutation mice, signifying enhanced glycolysis, anaerobic metabolism, mitochondrial dysfunction, and impaired oxidative metabolism. This thesis not only refines metabolic analysis precision but also enriches the understanding of metabolic dynamics in various contexts, offering insights into potential therapeutic interventions targeting metabolic pathways.