Diagnosis of Parkinson's Disease Based on Functional Brain Networks and Machine Learning

• Grant number: 24/05002-6
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: October 01, 2024
• End date: June 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Francisco Aparecido Rodrigues
• Grantee: Théo Bruno Frey Riffel
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Parkinson's Disease (PD) is a progressive nervous system brain disorder which affects motor neuron loss control and movement coordination. According to the World Health Organization (WHO), Parkinson's disease affects 1 percent of the world's population over the age of 65, making it the second most common neurodegenerative disease. Its signs and symptoms may vary for each person, making the current diagnosis, based on clinical symptoms, challenging. In this project, we plan to use fMRI data of different stages of PD to build a quantitative method for the automatic diagnosis ofPD. Our approach is based on previous studies that suggested that Parkinson's is due to changes in brain organization. We will prove a method capable of extracting measures from the brain network of PD patients and healthy controls for an accurate classification of them while also providing a biological interpretation of the connections between brain regions and their relation in different stages of PD.