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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.
This study developed a model to estimate monopolar local field potential (LFP) power from bipolar recordings in deep brain stimulation (DBS). This method accurately reconstructs LFP signals for improved DBS therapy guidance.
Area of Science:
- Neuroscience
- Biomedical Engineering
Background:
- Deep brain stimulation (DBS) is a key therapy for Parkinson disease.
- Modern DBS devices record local field potentials (LFPs) to optimize treatment.
- Current bipolar LFP recordings limit signal precision by attenuating physiologic data.
Purpose of the Study:
- To develop a model for estimating monopolar LFP power from bipolar recordings.
- To improve spatial resolution of LFP signals for DBS therapy.
- To offer a hardware-agnostic method for signal disambiguation.
Main Methods:
- Retrospective analysis of 64 leads in 50 Parkinson disease patients.
- Recorded intraoperative LFPs, generated bipolar montages, and calculated power spectral density.
- Used robust ordinary least square regression to model the relationship between bipolar and monopolar power, with data split into training and validation sets.
Main Results:
- A linear regression model accurately predicted monopolar LFP power from bipolar recordings.
- Models achieved high adjusted R-squared values (up to 0.9055) and low RMSEs (as low as 3.2663 dB).
- Model performance remained strong across different DBS targets and validation sets.
Conclusions:
- Monopolar LFP power can be accurately estimated from bipolar recordings using a linear regression model.
- This approach enhances spatial signal disambiguation for DBS.
- The method provides a versatile solution for informing DBS programming and adaptive stimulation.
