A team of neuroscientists from Bar-Ilan University is one of four winners of The Michael J. Fox Foundation for Parkinson’s Research (MJFF) and Sage Bionetworks BEAT-PD (Biomarker and Endpoint Assessment to Track Parkinson’s Disease) DREAM Challenge, a data competition designed to benchmark new methods to predict Parkinson's disease severity in patients in their homes.
MJFF and Sage partnered with Evidation Health, Northwestern University, Radboud University Medical Center, and the BRAIN Commons to host the BEAT-PD Challenge.
The challenge focused on predicting the subjective patient assessment of key symptom’s of Parkinson’s disease such as tremor, dyskinesia and the on-off phenomenon based solely on passive kinematic data collected from smartwatches and smartphones of the patients during free everyday behavior.
The Bar-Ilan team, termed “HaProzdor,” consisted of Ayala Matzner, Yuval El-Hanany and Prof. Izhar Bar-Gad from the Neural Interfaces Lab at Bar-Ilan's Gonda (Goldschmied) Multidiciplinary Brain Research Center. The Bar-Ilan team is the only winning team from outside of the United States. The additional winners were groups from Harvard Medical School, the University of Rochester Medical Center, and the University of Michigan.
“We congratulate all the winners. The Foundation has supported research into sensors and other digital tools for Parkinson’s for many years,” said Mark Frasier, PhD, Senior Vice President, Research Programs at MJFF. “The BEAT-PD projects are unlocking the potential of data collected by digital devices to help people with Parkinson’s, their physicians, and researchers. Now more than ever, we understand the critical importance of remote monitoring for the safe and effective delivery of healthcare and the progress of clinical research.”
In a previous data challenge, teams proved that disease status and symptom severity could be predicted using data collected during the completion of specific tasks while monitored by a physician. The BEAT-PD Challenge built on this to determine whether disease severity could be assessed from passive sensor data from consumer electronics, collected during daily life, not pre-set tasks, which will bring us closer to the promise of at-home monitoring of disease progression.
Three of the teams, including HaProzdor, approached the problem by applying signal processing methods to the smartwatch and smartphone sensor data, the results of which were then used in machine learning models which allowed for patient-specific characteristics.
Forty-three teams participated in the Challenge with data hosted by the BRAIN Commons, a scalable cloud-based platform for computational discovery designed for the brain health community. The winners share a $25,000 prize. The winning teams have been invited to collaborate to improve upon their individual models, as well as to test them against clinician-validated symptom severity ratings and to co-author a manuscript based on their findings.
The Neural Interfaces Lab headed by Prof. Bar-Gad targets the research of bidirectional brain computer interactions to gain insights into the mechanisms underlying various neural disorders and to provide tools for their treatment. Current research in the lab focuses on using experimental and computational tools to shed light on motor and behavioral disorders associated with basal ganglia malfunction such as Parkinson's disease, Tourette syndrome and attention deficit hyperactivity disorder (ADHD), and the amendment of their symptoms.