Keywords

Huntington disease; Artificial Intelligence; Neurodegenerative Diseases; Neuropathology Assessment; Striatal Atrophy; Image Segmentation

Abstract

As our life expectancy continues to rise, so does the prevalence of neurodegenerative diseases, such as Huntington disease (HD). Neurodegeneration leads to progressive regional brain atrophy, typically initiating prior to symptom onset. With the advancements of medical treatments designed to target neurodegeneration, researchers measure their impact on atrophy in animal models to assess their effectiveness. This is important because treatments designed to combat neuropathology are more likely to modify the disease itself, per contra to treatments designed to mask or treat symptoms. One method of brain region size quantification is magnetic resonance imaging (MRI), which while accurate, is prohibitively expensive. Conversely, stereological volume assessment, the process of estimating the volume of individual 3D brain regions from imaged 2D brain sections, is more commonly used. This method involves manually tracing brain region(s) of interest to determine their 2D area in regularly spaced imaged cross-sections, followed by application of the Cavalieri principle to estimate the volume. The pertinent caveats of this approach are lack of efficiency, resulting from the labor-intensive manual tracing process, and potential inaccuracies that arise due to individual differences in perception of boundaries within the brain, requiring that a single investigator evaluate all brains for a particular study. My project has automated this regional brain tracing and identification using artificial intelligence (AI) and concepts from topological data analysis to create the Neuropathology Assessment Tool (NAT). The NAT was validated by comparing speed and accuracy between manual and NAT volumetric analysis of the striatum in the brains of HD model mice. The NAT successfully and accurately detected genotypic differences with a much higher efficiency than manual assessment, while maintaining a strong agreement with manual measurements and significantly lower inter-group variability. The NAT’s success in automating and enhancing stereological volume assessment could increase the efficiency of preclinical evaluation of neuropathology, allowing for a greater number of experimental therapies to be tested and facilitating drug discovery for this and other intractable neurodegenerative diseases.

Thesis Completion Year

2024

Thesis Completion Semester

Fall

Thesis Chair

Southwell, Amber

College

College of Medicine

Department

Burnett School of Biomedical Sciences

Thesis Discipline

Burnett School of Biomedical Sciences

Language

English

Access Status

Open Access

Length of Campus Access

None

Campus Location

Orlando (Main) Campus

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Rights Statement

In Copyright