Related Experiment Video
Updated: Jan 7, 2026
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
Published on: August 12, 2018
Toward Individualized Deep Brain Stimulation: A Stereoelectroencephalography-Based Workflow for Neurostimulation
Jeremy Saal1, Kelly Kadlec2, Anusha B Allawala1
1UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
Objectives:
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. Thus, we have used inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for long-term implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets.
Materials And Methods:
Fourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using 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 (Hedges' g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The SD of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p < 0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately ten sham trials were needed to robustly estimate sham variability. Power analysis (using a paired t-test) showed that for effect sizes ≥1.1, approximately ten trials should be used per stimulation site for sufficiently powered results.
Conclusions:
The presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.
