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Funded Studies

Dynamic Modeling and Prediction of Parkinsonian Symptom Progression

Study Rationale: Parkinson’s disease (PD) affects different people in different ways. We believe that these differences can offer clues about what happens in the brain before people with PD even begin to notice symptoms. Understanding these different mechanisms should help researchers develop better, more targeted drugs for PD. In this study, we will analyze the brain scans of people with different types of symptoms over time. This approach will help us to understand what causes the array of symptoms associated with PD.

Hypothesis: We hypothesize that by assessing brain scans over time, we will learn to predict when a person will get specific PD symptoms before those symptoms occur.

Study Design: We will use brain scans that have been collected over many years beginning when individuals are in the earliest stages of PD. We will then use complicated mathematical tools called Disease Progression Models to determine whether we can predict the onset of different PD symptoms and how they will affect each other.

Impact on Diagnosis/Treatment of Parkinson’s disease: If successful, our approach should allow doctors to predict which PD symptoms an individual is most likely to experience and which medications will be most effective. It can also allow drug developers to determine which patients will be most likely to derive benefits from new types of treatments.

Next Steps for Development: We hope that if the study is successful, pharmaceutical companies will use our mathematical models to improve their drug testing.


  • Boris A. Gutman, PhD

    Los Angeles, CA United States

  • Jennifer G. Goldman, MD, MS

    Chicago, IL United States

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