Deep brain stimulation (DBS) is an effective neurosurgical intervention that can significantly reduce the burden of Parkinson’s disease, however, there is currently no reliable way to predict which patients will benefit most from the therapy. In this project, we are evaluating how useful MRI brain images are for predicting DBS effectiveness and risk for side-effects in patients with Parkinson’s disease. This work will have strong potential for improving the way clinicians choose the optimal DBS candidate and surgical brain target, while ensuring that resources are only directed toward patients who are most likely to benefit.
Fractal dimension on T1-w MRI predicts DBS outcomes:
- Read the paper "Read Boundary complexity of cortical and subcortical areas predicts deep brain stimulation outcomes in Parkinson’s disease" to see how structural MRI features, like fractal dimension, boost the predictive power of clinical models when forecasting post-DBS changes in medication burden.
- Watch PhD candidate Devin Schoen’s presentation exploring her Nature Communications–published work and how multi-contrast MRI biomarkers refine DBS outcome models.