Abstract
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.
Highlights
The high gamma (HG) band (70–150 Hz) is a rapidly evolving, spatially localized signal (Crone et al, 1998; Miller et al, 2009) that is thought to be associated with local neuronal processing (Manning et al, 2009; Miller et al, 2009)
We applied a linearly constrained minimum variance (LCMV) beamformer method on EEG data during a motor imagery paradigm to extract a timefrequency 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 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 functional magnetic resonance imaging (fMRI) data
Summary
The HG band (70–150 Hz) is a rapidly evolving, spatially localized signal (Crone et al, 1998; Miller et al, 2009) that is thought to be associated with local neuronal processing (Manning et al, 2009; Miller et al, 2009). This high frequency activity has been found across the cortex reflecting local computation across a number of functional domains including sensory processing, attention, memory, and movement control. The high frequency band has a Abbreviations: BCI, brain-computer interface; BEM, boundary element model; BOLD, blood oxygen level dependence; CAR, common average reference; EEG, electroencephalogram; EMG, electromyography; fMRI, functional magnetic resonance imaging; HG, high gamma
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