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Predicting Monopolar Local Field Potential Power From Bipolar Recordings in Deep Brain Stimulation
Chance Fleeting1, Griffin Lamp2, Kara A Johnson3
1Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, US; J. Crayton Pruitt Department of Biomedical Engineering, University of Florida, Gainesville, FL, US; Department of Neurology, University of Florida, Gainesville, FL, US.
Objectives:
Deep brain stimulation (DBS) is an established therapy for neurologic disorders such as Parkinson disease. 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 physiologic 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.
Materials And Methods:
This retrospective study analyzed 64 leads in 50 patients with Parkinson Disease undergoing subthalamic nucleus (11) or globus pallidus internus (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 ordinary least square regression. Observations were randomly partitioned into training and validation sets.
Results:
A total of 64 leads yielded 640 observations. The configuration with the lowest CN (7.45) was {C03, C12, C23}. The models showed 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). Weights transferred from the training set to the validation set, as well as subthalamic nucleus, globus pallidus internus, and hemisphere-specific subsets, retained high performance.
Conclusions:
This study shows 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.
