Functional Parcellation

Functional parcellation aims to divide the brain into distinct regions based on their functional connectivity or anatomical properties, revealing the organization of brain networks. Current research employs diverse approaches, including deep learning models (like ensemble networks and those integrating surface reconstruction), geometry-based tractography, and multi-stage clustering algorithms, often applied to high-resolution MRI data (including ex vivo scans). These advancements improve the accuracy and reliability of brain mapping, enabling more precise analyses of brain structure and function in both healthy individuals and those with neurological disorders, ultimately enhancing our understanding of brain organization and disease.