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Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a clinically effective tool for treating medically refractory Parkinson’s disease (PD), but its neural mechanisms remain debated. Previous work has demonstrated that STN DBS results in evoked potentials (EPs) in the primary motor cortex (M1), suggesting that modulation of cortical physiology may be involved in its therapeutic effects. Due to technical challenges presented by high-amplitude DBS artifacts, these EPs are often measured in response to low-frequency stimulation, which is generally ineffective at PD symptom management. This study aims to characterize STN-to-cortex EPs seen during clinically relevant high-frequency STN DBS for PD. Intraoperatively, we applied STN DBS to 6 PD patients while recording electrocorticography (ECoG) from an electrode strip over the ipsilateral central sulcus. Using recently published techniques, we removed large stimulation artifacts to enable quantification of STN-to-cortex EPs. Two cortical EPs were observed – one synchronized with DBS onset and persisting during ongoing stimulation, and one immediately following DBS offset, here termed the “start” and the “end” EPs respectively. The start EP is, to our knowledge, the first long-latency cortical EP reported during ongoing high-frequency DBS. The start and end EPs differ in magnitude (p < 0.05) and latency (p < 0.001), and the end, but not the start, EP magnitude has a significant relationship (p < 0.001, adjusted for random effects of subject) to ongoing high gamma (80–150 Hz) power during the EP. These contrasts may suggest mechanistic or circuit differences in EP production during the two time periods. This represents a potential framework for relating DBS clinical efficacy to the effects of a variety of stimulation parameters on EPs.

High gamma oscillations (70–150 Hz; HG) are rapidly evolving, spatially localized neurophysiological signals that are believed to be the best representative signature of engaged neural populations. The HG band has been best characterized from invasive electrophysiological approaches such as electrocorticography because of the increased signal-to-noise ratio that results when by-passing the scalp and skull. Despite the recent observation that HG activity can be detected non-invasively by electroencephalography (EEG), it is unclear to what extent EEG can accurately resolve the spatial distribution of HG signals during active task engagement. We have overcome some of the limitations inherent to acquiring HG signals across the scalp by utilizing individual head anatomy in combination with an inverse modeling method. We applied a linearly constrained minimum variance (LCMV) beamformer method on EEG data during a motor imagery paradigm to extract a time-frequency spectrogram at every voxel location on the cortex. To confirm spatially distributed patterns of HG responses, we contrasted overlapping maps of the EEG HG signal with blood oxygen level dependence (BOLD) functional magnetic resonance imaging (fMRI) data acquired from the same set of neurologically normal subjects during a separate session. We show that scalp-based HG band activity detected by EEG during motor imagery spatially co-localizes with BOLD fMRI data. Taken together, these results suggest that EEG can accurately resolve spatially specific estimates of local cortical high frequency signals, potentially opening an avenue for non-invasive measurement of HG potentials from diverse sets of neurologically impaired populations for diagnostic and therapeutic purposes.