Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

Background: Deep-brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Methods: Eleven patients (chronic pain=5, major depressive disorder=5, obsessive-compulsive disorder=1) underwent inpatient testing using stereoelectroencephalography (sEEG) and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. Results: Effect sizes (Cohen's d) of stimulation-induced symptom score changes ranged from approximately -1.5 to +3.0. The standard deviation of sham trial responses was strongly correlated with the standard deviation of stimulation responses (r=0.86, p < 0.001). Power analysis (using a paired-t test) showed that, for large effect sizes (>=1.1), around 10 trials should be used per stimulation site. Furthermore, we show that 12-15 sham trials were needed to robustly estimate sham variability. Conclusions: The workflow presented is adaptable to any indication and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the approach outlined can be used to find personalized, unbiased, and clinically efficacious stimulation sites.