MDS Abstracts

Abstracts from the International Congress of Parkinson’s and Movement Disorders.

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2024 International Congress

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

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  • Determining and Leveraging Local Ancestry to Assess Individual-Level Risk: from the Global Parkinson’s Genetics Program

    G. Parkinson'S_genetics_program (gp2) (Bethesda, USA)

  • Determining Cerebrospinal Fluid Alpha-Synuclein Seed Amplification Assay Status from Demographics and Clinical Data

    C. Venuto, K. Herbst, K. Kieburtz (Rochester, USA)

  • Determining the Direction of segmented DBS Micro Electrodes through the EEG

    K. Balmuri, V. Rama Raju (Hyderabad, India)

  • Determining the optimal filtering method for the removal of gait-related movement artefacts from EEG acquired from People with Parkinson’s Disease

    B. Wijntjes (Dublin, Ireland)

  • Developing a selection framework for digital measures of Parkinson’s disease progression for the EJS ACT-PD trial

    R. Ellis-Doyle, ML. Zeissler, C. Gonzalez-Robles, M. Bartlett, S. Collins, S. Del-Din, S. Haar, M. Hu, A. Jha, C. Lambert, A. Noyce, R. Petty, K. Pushparatnam, L. Rochester, C. Siu, G. Mills, A. Schrag, T. Foltynie, C. Carroll, A. Yarnall (London, United Kingdom)

  • Developing a Supervised Machine Learning Model for Long-Term Cognitive Status Prediction in Parkinson Disease

    L. Saadatpour, A. Vijayakumari, D. Floden, H. Fernandez, B. Walter (Cleveland, USA)

  • Developing a Supportive Care and Wellness Program for Parkinson’s Patients

    K. Farnes, H. Ooi, N. Hellmers, M. Lopez, H. Sarva (New York, USA)

  • Developing a Virtual Brick-Building Group for Veterans with Movement Disorders

    J. Kaplan, C. Kausek, J. Lehosit (Richmond, USA)

  • Developing Composite Digital Measures for Tracking Parkinson’s Disease (PD) Progression using a Comprehensive Machine Learning-based Framework

    S. Zhai, J. Ren, J. Shen, S. Chatterjee, Y. Xu, A. Liaw, V. Svetnik, O. Patil, ER. Dorsey, J. Adams, M. Dockendorf, R. Baumgartner (Rahway, USA)

  • Developing composite measures that track physical activity change in people with early-stage Parkinson’s disease using machine learning and wearable sensors

    K. Ho, C. Chen, S. Shin, S. Li, N. Kowahl, L. Evers, M. Meinders, L. Shih, B. Bloem, A. Siderowf, R. Kapur (South San Francisco, USA)

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