2024 International Congress

September 27-October 1, 2024. Philadelphia, PA.

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PreActive-PD Telehealth Coaching to Increase Physical Activity in Early-Stage Parkinson Disease: A Randomized Controlled Feasibility Trial

K. Long, A. Pacheco, L. Quinn, D. Wani, J. Alomar, D. Kipnis, C. Macpherson (Walnut Creek, USA)
Precision Functional Mapping of Therapeutic Response in Task-specific Focal Dystonia

V. Nguyen, A. Meyer, J. Hudson, S. Norris, E. Gordon (St Louis, USA)
Preclinical Studies: Investigating Serum-Based Exosomal miRNAs in Prodromal Drug-Induced Parkinson’s Disease

M. Ahmad (Hyderabad, India)
Predictability of Motor States With Foslevodopa/Foscarbidopa in Patients With Advanced Parkinson’s Disease

R. Hauser, M. Bouchard, D. Santos-Garcia, L. Bergmann, R. Gupta, L. Harmer, M. Shah, C. Yan, S. Isaacson (Tampa, USA)
Predicting Clinical Status of Patients with Huntington’s Disease from Raw Speech Samples, a Lightweight Machine Learning Approach

C. Le Moine, R. Massart, A-C. Bachoud-Lévi (Paris, France)
Predicting cognitive phenotypes in de novo Parkinson’s disease: an fMRI data-driven approach.

MI. de Bartolo, A. Ojha, G. Leodori, C. Piervincenzi, G. Vivacqua, S. Pietracupa, M. Costanzo, F. D'Antonio, S. Barbetti, R. Margiotta, G. Bruno, A. Conte, A. Berardelli, G. Fabbrini, P. Pantano, D. Belvisi (Rome, Italy)
Predicting Disease Severity in Friedreich’s Ataxia through Wearable Monitoring and Machine Learning.

R. Mishra, A. Sastre, A. Enriquez, V. Profeta, M. Wells, A. Vaziri, D. Lynch (Newton, USA)
Predicting Freezing of Gait (FoG) in Parkinson’s Disease from Conventional T1-Weighted MRI

G. Rathi, A. Gardner, Z. Mari, V. Mishra (Birmingham, USA)
Predicting prodromal Parkinson’s disease with a deep learning algorithm using medical and prescription data

WW. Lee, Y. Koo, M. Kim (Seoul, Republic of Korea)
Predicting the Risk of Parkinson’s Disease by Body Composition and Metabolic Diseases in Gender and Age Dependent Manners

J. Fu, H. Shang, X. Chen (Chengdu, Sichuan, China)

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