Predicting monopolar local field potential power from bipolar recordings in deep brain stimulation

Deep brain stimulation (DBS) is an established therapy for neurological disorders such as Parkinson's disease (PD). Modern DBS devices can record local field potentials (LFPs) to guide DBS therapy. LFPs from these devices are typically limited to bipolar configurations to suppress common-mode noise and reject artifacts. However, bipolar recordings also attenuate some local physiological signals. Methods that convert bipolar to monopolar power offer more spatially precise estimates of LFPs. Herein, we develop a model to estimate monopolar power from bipolar recordings.

This retrospective study analyzed 64 patients with PD undergoing STN (11) or GPi (53) DBS implantation. Intraoperatively, LFPs were recorded from all contacts and filtered. Bipolar montages were generated for each combination. Power spectral density (PSD) was calculated from each monopolar and bipolar signal, averaged over canonical frequency bands, and processed as log PSD. A common set of bipolar configurations was selected to minimize the Condition Number (CN), maximizing model stability. Monopolar and bipolar powers were related using robust OLS regression. Observations were randomly partitioned into training and validation sets.

Sixty-four PD patients yielded 640 observations. The configuration group with the lowest CN (7.45) was {C₀₃, C₁₂, C₂₃}. The models demonstrated adjusted R²s of 0.9015, 0.9055, 0.8853, and 0.8764, and RMSEs (dB) of 3.2663, 3.2801, 3.5815, and 3.7035 when predicting C0, C1, C2, and C3 (N = 500; all p < 0.0001). The STN, GPi, and combined cohorts performed comparably. Weights transferred from the combined model to the validation set retained high performance.

This study demonstrates that monopolar LFP power can be accurately estimated from bipolar power using a linear regression model with strong generalizability across targets and validation sets. This approach offers a hardware-agnostic solution to spatially disambiguate signals and better inform DBS programming and adaptive stimulation in chronically implanted devices.