Category: Parkinson's Disease: Neuroimaging
Objective: To determine the clinical utility of neuroimaging markers to predict motor outcomes for deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson disease (PD).
Background: STN DBS is an effective treatment for PD, but outcomes are highly variable, necessitating robust predictors of effect for clinical decision-making. Traditionally, clinical variables have been used to predict DBS effect, most notably response to levodopa, with modest predictive power. More recent studies show that image-based markers such as including volumetric, functional and structural connectivity measures can predict response to STN DBS. However, whether imaging variables improve DBS prediction models beyond clinical variables alone remains unknown.
Method: 74 participants with STN DBS were identified with preoperative and postoperative motor ratings (UPDRS-III), good quality structural MRI and resting state functional connectivity MRI (rs-fcMRI). Demographic, clinical, neuropsychiatric and imaging data were collected. Predictive models of clinical outcome defined as improvement on motor ratings one year post-op were created using the clinical and imaging data and optimized using a stepwise multiple linear regression approach. The optimal clinical model was then compared with the model using optimal clinical variables plus optimal imaging variables with hierarchical linear regression.
Results: The optimal clinical model included 7 variables (adjusted R-square = 0.149), with only levodopa response being independently significant (p = 0.013). The optimal imaging model included 9 variables (adjusted R-square = 0.262), with left STN-GPI functional connectivity accounting for the most variance (p = 0.002). Comparison of best clinical versus best clinical + best imaging models demonstrated an increase in adjusted R-square of 0.149 to 0.348, significant F-change at p = 0.003.
Conclusion: In our cohort, both clinical and imaging variables predict DBS motor outcomes, but models that also include imaging variables significantly outperform clinical variables alone. Preoperative neuroimaging may be an effective tool in patient selection and outcomes prediction for clinical DBS.
To cite this abstract in AMA style:J. Younce, M. Campbell, J. Perlmutter, S. Norris. Functional connectivity and volumetric data improve prediction of DBS outcomes [abstract]. Mov Disord. 2020; 35 (suppl 1). https://www.mdsabstracts.org/abstract/functional-connectivity-and-volumetric-data-improve-prediction-of-dbs-outcomes/. Accessed December 7, 2023.
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