Spectral Clustering of Resting-State fMRI for Data-driven Functional Brain Segmentation in Neurodegeneration

Neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are characterized by progressive cognitive decline, often associated with alterations in brain function and connectivity. Resting-state functional magnetic resonance imaging has emerged as a powerful tool for studying brain connectivity in both healthy individuals and patients with neurodegeneration. This paper presents a novel approach to analyzing resting-state fMRI data using data-driven spectral clustering techniques to identify functionally connected brain regions. The AAL brain atlas was utilized to segment the brain into predefined regions of interest and apply spectral clustering to uncover functional network organization patterns. The study aims to explore disrupted connectivity patterns in neurodegeneration, with potential implications for early diagnosis, disease monitoring, and therapeutic targeting. This work highlights the potential of advanced clustering techniques to uncover meaningful insights from resting-state fMRI data in the context of neurodegenerative diseases.