Multimodal approach for the detection, prediction, and management of Freezing of Gait in Parkinson's Disease

• Grant number: 24/04859-0
• Support Opportunities: Regular Research Grants
• Start date: February 01, 2025
• End date: January 31, 2029
• Field of knowledge: Engineering - Biomedical Engineering
• Principal Investigator: Daniel Boari Coelho
• Grantee: Daniel Boari Coelho
• Principal researcher abroad: Friedhelm Hummel
• Institution abroad: École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
• Principal researcher abroad: Silvestro Micera
• Institution abroad: École Polytechnique Fédérale de Lausanne (EPFL), Switzerland
• Principal researcher abroad: Solaiman Shokur
• Institution abroad: Centre Hospitalier Universitaire Vaudois, Switzerland
• Host Institution: Centro de Engenharia, Modelagem e Ciências Sociais Aplicadas (CECS). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Associated researchers: Carla da Silva Batista ; Diego Orcioli da Silva ; Fabio Augusto Barbieri ; Luis Augusto Teixeira ; Mohamed Bouri ; Victor Spiandor Beretta

Abstract

Freezing of Gait (FoG), a debilitating motor symptom in Parkinson's disease (PD), manifests as sudden and unpredictable difficulty initiating or maintaining forward movement. These episodes significantly impact daily activities, increase fall risk, and diminish the quality of life for people with Parkinson's disease (pwPD). Current diagnostic methods lack objectivity, making it challenging to predict FoG occurrences in real-time while existing treatments provide only limited relief from FoG symptoms. This research addresses these challenges by investigating the underlying mechanisms of FoG in PD through objective biomarkers derived from the integration of different neurophysiological and biomechanical information. Employing a multimodal approach, this comprehensive analysis aims to: (1) elucidate the complex neural and biomechanical processes contributing to FoG by analyzing together three domains of the neuromusculoskeletal system: brain activity, muscle activation patterns, and movement characteristics, and (2) develop real-time FoG detection and prediction models using machine learning techniques based on characteristic changes in the multimodal data streams. Building upon these insights, a closed-loop wearable stimulation system will be designed and tested to assist in mitigating the freezing. Such device explores three non-invasive intervention modalities attempting pwPD to regain the movements through: haptic feedback for alerting users of impending FoG episodes, functional electrical stimulation (FES) for targeted muscle activation during FoG events, and non-invasive transcranial electric temporal interference stimulation (tTIS) of the basal ganglia to investigate its potential in mitigating FoG severity. The feasibility and effectiveness of this technology in suppressing or preventing FoG events will be assessed in pwPD. Furthermore, by comparing the efficacy of each intervention modality, the study aims to optimize FoG interventional strategies, leading to improved outcomes and potentially opening doors for new therapeutic solutions for the management of FoG in pwPD. (AU)