A recent study in Nature Communications revealed that deep brain stimulation can be tailored to address specific Parkinson’s disease symptoms by targeting distinct pathways.
Researchers looked at 237 patients across 5 health care centers, focusing on different white matter tracts linked to tremor, bradykinesia, rigidity, and axial symptoms. Clinically, the study reported a baseline score of 44.59% and an average improvement of 51.71% in UPDRS-III scores for all patients.
Key findings included:
- Tremor: Improvement with stimulation of tracts was tied to the primary motor cortex and cerebellum.
- Axial symptoms: Improvement with stimulation of tracts was linked to the supplementary motor cortex and brainstem.
- Bradykinesia and rigidity: Improvement with tracts was linked to the supplementary motor and premotor cortices, respectively.
When subjected to 10-fold cross-validations, all symptom tract models, except for the tremor tract model, explained statistically significant amounts of variance; bradykinesia (R = 0.20, p = 0.02), rigidity (R = 0.20, p = 0.02), and axial symptoms (R = 0.22, p = 0.01). In an independent validation cohort of 93 patients, the algorithm's estimates correlated significantly with empirical improvements (R = 0.37, p = 0.0006).
The algorithm using the symptom-response tracts was developed to personalize deep brain stimulation treatment based on individual patient symptoms. Researchers noted the approach could maximize stimulation settings, enhancing patient outcomes and reducing the trial-and-error period in deep brain stimulation programming.
They also emphasized the importance of symptom-specific network models in deep brain stimulation, suggesting a shift towards personalized treatment strategies for Parkinson’s disease.
A full list of disclosures can be found in the original study.