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Abstract #4197

Fractal Dimension as a T1 Measure for Multiparametric Investigation of Outcome Variance Following Deep Brain Stimulation in Parkinson’s Disease

Devin Schoen1, Skyler Deutsch1, Sarah Wang2, Ian O. Bledsoe2, Jill L. Ostrem2, Philip A. Starr3, Doris D. Wang3, and Melanie A. Morrison1
1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2Neurology, University of California San Francisco, San Francisco, CA, United States, 3Neurological Surgery, University of California San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Parkinson's Disease, Parkinson's Disease, DBS, T1, multiparametric, fractal dimension, therapeutic outcomes, stimulation

Motivation: Improved prognostic criteria are needed to better understand outcome variance in response to DBS for Parkinson's disease. Leveraging routinely collected T1-w MRI could offer accessible predictive biomarkers.

Goal(s): In 129 patients, we sought to find features derivable from preoperative T1-w images to serve as informative biomarkers to explain variance in response to DBS.

Approach: Employing regional analysis and regression techniques, we examined the relationships between fractal dimension (FD), regional volumes, and post-DBS treatment responses.

Results: Analysis revealed distinct, significant correlations between FD and volumes with response to DBS, indicating their potential for complementary integration in a multi-parametric predictive tool.

Impact: Fractal dimension and volume, metrics derived from T1-w MRI, correlate with DBS response variance in PD patients. Integration of multi-parametric T1-w imaging features into prediction models could aid clinicians in candidate selection and treatment planning to ultimately improve patient-centric outcomes.

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Keywords