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Neuromodulation Technology at the Neural InterfaceNeurosciencePeer-reviewed journal article

Differentiating filter-induced oscillations from physiological stimulation-evoked potentials in intracranial recordings

Luka ZivkoviciD, Srdjan SumaraciD, David CromptoniD, William D. HutchisoniD, Andres M. LozanoiD, Suneil Kumar KaliaiD, Luka MiloseviciD

PDFDOIPreprint

Abstract

IntroductionStimulation-evoked potentials (SEPs), recorded both during and after deep brain stimulation (DBS) surgery, have shown promise for guiding DBS targeting and programming. However, filtering protocols applied to stimulation trains produce an artifact we call a filter-induced oscillation (FIO) which closely mimics physiological SEPs. Hence, we outline the mechanistic origins of this distortion and describe a means of differentiating it from valid SEP activity. MethodsWe recorded in 18 patients undergoing DBS surgery targeting the subthalamic nucleus or globus pallidus internus. We stimulated target nuclei with cathode-first (CF) and anode-first (AF) pulses to record native SEPs, and in white matter tracts (null condition). Recordings were subsequently filtered to illustrate FIO. Next, we filtered harmonic frequencies of an artificial stimulation train to demonstrate FIO origins. Finally, FIO was deliberately generated in white matter recordings with a notch filter, and its behaviour contrasted with SEPs during AF and CF stimulation. ResultsFiltering stimulation trains produced FIOs that depended on filter order and corner frequency. We also showed that FIO emerges from filter-induced attenuations of harmonic frequencies which compose stimulation trains, producing oscillations of like frequency around pulses. Finally, FIOs reverse in polarity depending on AF or CF stimulation, whereas SEPs do not. ConclusionsGiven the potential for widespread adoption of SEPs in DBS targeting and programming, safe analytical protocols are imperative to avoid the induction of processing-related artifacts which can be misinterpreted as biological signals. Here we establish the necessary theory for identifying FIOs and tuning analytical pipelines to avoid their generation.

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