title: Non-invasive detection of high gamma band activity during motor imagery.
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.
authors.affiliation: Graduate Program in Neuroscience, Department of Neurobiology and Behavior, University of Washington Seattle, WA, USA ; Center for Sensorimotor Neural Engineering, University of Washington Seattle, WA, USA ; Department of Computer Science and Engineering, University of Washington Seattle, WA, USA.
authors.forename: Melissa M
authors.initials: MM
authors.lastname: Smith
authors.affiliation: Department of Radiology, University of Washington Seattle, WA, USA.
authors.forename: Kurt E
authors.initials: KE
authors.lastname: Weaver
authors.affiliation: Center for Sensorimotor Neural Engineering, University of Washington Seattle, WA, USA ; Department of Radiology, University of Washington Seattle, WA, USA ; Department of Neurology, University of Washington Seattle, WA, USA.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Grabowski
authors.affiliation: Graduate Program in Neuroscience, Department of Neurobiology and Behavior, University of Washington Seattle, WA, USA ; Center for Sensorimotor Neural Engineering, University of Washington Seattle, WA, USA ; Department of Computer Science and Engineering, University of Washington Seattle, WA, USA.
authors.forename: Rajesh P N
authors.initials: RP
authors.lastname: Rao
authors.affiliation: Center for Sensorimotor Neural Engineering, University of Washington Seattle, WA, USA ; Department of Neurological Surgery, University of Washington Seattle, WA, USA.
authors.forename: Felix
authors.initials: F
authors.lastname: Darvas
keywords: EEG; fMRI; high gamma; motor imagery