Machine learning explains response variability of deep brain stimulation on Parkinson's disease quality of life.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 39354049.
- Also identified by DOI 10.1038/s41746-024-01253-y and PMC identifier 11445542.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Improving health-related quality of life (QoL) is crucial for managing Parkinson's disease. However, QoL outcomes after deep brain stimulation (DBS) of the subthalamic nucleus (STN) vary considerably. Current approaches lack integration of demographic, patient-reported, neuroimaging, and neurophysiological data to understand this variability. This study used explainable machine learning to analyze multimodal factors affecting QoL changes, measured by the Parkinson's Disease Questionnaire (PDQ-39) in 63 patients, and quantified each variable's contribution. Results showed that preoperative PDQ-39 scores and upper beta band activity (>20 Hz) in the left STN were key predictors of QoL changes. Lower initial QoL burden predicted worsening, while improvement was associated with higher beta activity. Additionally, electrode positions along the superior-inferior axis, especially relative to the z = -7 coordinate in standard space, influenced outcomes, with improved and worsened QoL above and below this marker. This study emphasizes a tailored, data-informed approach to optimize DBS treatment and improve patient QoL.