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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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  • Improved Biomarkers and Clinical Outcome Measures, 2016
    Developing a Molecular Imaging Tool That Binds to Alpha-Synuclein and Inhibits Its Formation

    Study Rationale:
    Amyloid fibrils play a key role in Parkinson's disease (PD). Studying their formation from self‐assembled aggregates (protein clumps) would help aid the development of a valuable...

  • Target Advancement Program, 2016
    Alpha-Synuclein Aggregates as an Inducer of Glial Senescence: Potential Role in Parkinson's Disease

    Study Rationale:                             
    Cellular senescence (deterioration) is a critical factor of biological aging that occurs in almost all peripheral tissues but little is known about its...

  • Therapeutic Pipeline Program, 2016
    Highly Selective M1 Muscarinic Receptor Positive Allosteric Modulators for the Treatment of Parkinson's Dementia

    Study Rationale:                             
    Up to 80% of individuals with Parkinson’s develop Parkinson’s disease dementia (PDD), which is characterized by a progressive loss of memory and a decline...

  • Target Advancement Program, 2016
    Genetic Modulation of LAMP-2A in PD Models

    Study Rationale:                   
    Brain cells use surveillance mechanisms to get rid of toxic proteins, such as mutant alpha-synuclein, that otherwise would accumulate and kill them. We have...

  • Target Advancement Program, 2016
    Mechanism of TFEB Dysfunction by GBA1 Mutation in Human iPSC-derived Dopaminergic Neurons

    Study Rationale:
    People who have mutations in the GBA1 gene have a higher chance of developing Parkinson's disease (PD) but it is unclear why. We found that GBA1 mutations affect the cellular ability...

  • PPMI Data Challenge, 2016
    Subtyping Parkinson’s Disease with Deep Learning Models (2016 PPMI Data Challenge Winner)

    Study Rationale:
    As Parkinson's disease (PD) is highly heterogeneous, identifying coherent PD subtypes is crucial for understanding the underlying mechanism of the disease and designing and testing...

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