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Personalizing Dual-Target Cortical Stimulation with Bayesian Parameter Optimization Successfully Treats Central
Evan M Dastin-van Rijn1, Seth D König2,3, Danielle Carlson2,3
1Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Brain Sciences
|January 21, 2022
Summary
This study introduces a personalized neuromodulation technique for central pain disorders. Dual cortical stimulation, guided by Bayesian optimization, effectively eliminated severe facial pain in a treatment-resistant case.
Area of Science:
- Neurology
- Neurosurgery
- Pain Management
Background:
- Central pain disorders, including central post-stroke pain, present significant treatment challenges.
- Pharmacological and established neuromodulation techniques often fail in refractory cases.
- Novel targets and optimized neuromodulation are needed for effective pain management.
Observation:
- A patient with intractable left-sided facial pain post-thalamic stroke experienced debilitating pain and side effects from prior treatments.
- A novel dual cortical stimulation approach targeting the motor cortex (M1) and dorsolateral prefrontal cortex (dlPFC) was employed.
- Bayesian optimization was utilized to personalize stimulation parameters and target locations.
Findings:
- The dual cortical stimulation successfully reduced the patient's pain from 8.5/10 to 0/10 during hospitalization.
- Pain levels remained at or below 2/10 one month post-procedure.
- Simultaneous M1 and dlPFC stimulation was optimal, with no seizure induction or cognitive decline observed.
Implications:
- This personalized neuromodulation strategy offers a promising new avenue for treating severe central pain disorders.
- Bayesian optimization enables systematic and individualized treatment parameter selection.
- The approach may extend to other functional neurological disorders requiring neuromodulation.
