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Funded Studies

The Foundation supports research across basic, translational and clinical science to speed breakthroughs that can lead to the creation of new treatments and a better quality of life for people with Parkinson's disease.

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Previously funded studies appear chronologically, with the most recent appearing first.

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  • Research Grant, 2019
    Supplement: Investigation of PINK1-dependent Phosphorylation of Rab GTPases

    Promising Outcomes of Original Grant:
    We were trying to understand how the PINK1 gene targets a set of proteins known as Rab GTPases through protein modification known as phosphorylation. The...

  • Improved Biomarkers and Clinical Outcome Measures, 2019
    Applying Artificial Intelligence for Early Identification of Parkinson’s Disease

    Study Rationale:
    The nervous system regulates heart rate in response to situations such as stress and emergencies. Dysfunction of part of the nervous system controlling the heart can cause changes in...

  • Priority Target Award, 2016
    Advancing PINK1 Knockout Models of Parkinson’s

    Study Rationale:
    Loss-of-function mutations in PINK1 are causally linked to early-onset Parkinson’s disease (PD). MJFF funded the generation and initial characterization of PINK1 knockout (KO) models...

  • Research Grant, 2019
    Immune Cell Dysfunction in PINK1 Parkinson’s Models

    Promising Outcomes of Original Grant:
    In our previous project, we significantly advanced the characterization of PINK1 knockout (KO) models. We found evidence of innate and adaptive immune cell...

  • Research Grant, 2019
    PPM1H Activators for the Treatment of Parkinson’s Disease

    Study Rationale:
    Although Parkinson’s disease (PD) causes are in most cases unknown (idiopathic) some cases are linked to a person’s genetics. Leucine-rich repeat kinase 2 (LRRK2) is a very important...

  • Research Grant, 2019
    Enhancing the Clinical Utility of Data-Driven Parkinson’s Subtypes with Knowledge Distillation and Visualization

    Study Rationale:
    Data-driven analytics, such as machine learning methods, have been applied to discover comprehensive Parkinson’s disease (PD) subtypes from observational patient data. However, many...

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