Preliminary results on using clustering of functional data to identify patients with alzheimer’s disease by analyzing brain MRI scans
Faculty Advisor
Date
2025
Keywords
clustering of functional data, brain MRI, Alzheimer’s disease
Abstract (summary)
This study delves into the effectiveness of funWeightClust, a sophisticated model-based clustering technique that leverages functional linear regression models to pinpoint patients diagnosed with Alzheimer’s Disease. Our research entailed a thorough analysis of voxelwise fractional anisotropy data derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, with a particular emphasis on the Cingulum and Corpus Callosum, which are critical regions of interest in understanding the disease’s impact on brain structure. Through a series of experiments, we established that funWeightClust is efficient at distinguishing between patients with Alzheimer’s Disease and healthy control subjects. Notably, the clustering model yielded even more pronounced and accurate results when we focused our analysis on specific brain regions, such as the Left Hippocampus and the Splenium. We postulate that integrating additional biomarkers could significantly enhance the accuracy and reliability of funWeightClust in identifying patients who exhibit signs of Alzheimer’s Disease.
Publication Information
Anton, C., Anton, C., El-Hajj, M., Craner, M., & Lui, R. (2025). Preliminary results on using clustering of functional data to identify patients with alzheimer’s disease by analyzing brain MRI scans. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING (BIOSTEC 2025). Volume 1, pp. 363-368. SciTePress. https://doi.org/10.5220/0013263500003911
Notes
Item Type
Presentation
Language
Rights
Attribution-NoDerivs (CC BY-ND)
