Non-invasive detection of high gamma band activity during motor imagery
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 electroc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-10-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00817/full |
| _version_ | 1830359308430213120 |
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| author | Melissa M Smith Kurt E Weaver Thomas J Grabowski Rajesh P. N . Rao Felix eDarvas |
| author_facet | Melissa M Smith Kurt E Weaver Thomas J Grabowski Rajesh P. N . Rao Felix eDarvas |
| author_sort | Melissa M Smith |
| collection | DOAJ |
| description | 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 (ECoG) 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 beamformer (LCMV) 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 BOLD 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 |
| first_indexed | 2024-12-20T02:56:50Z |
| format | Article |
| id | doaj.art-2214ec09c4a0470ca124ee3328c799b9 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-20T02:56:50Z |
| publishDate | 2014-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-2214ec09c4a0470ca124ee3328c799b92022-12-21T19:55:52ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612014-10-01810.3389/fnhum.2014.00817100506Non-invasive detection of high gamma band activity during motor imageryMelissa M Smith0Kurt E Weaver1Thomas J Grabowski2Rajesh P. N . Rao3Felix eDarvas4University of WashingtonUniversity of WashingtonUniversity of WashingtonUniversity of WashingtonUniversity of WashingtonHigh 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 (ECoG) 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 beamformer (LCMV) 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 BOLD 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 purposeshttp://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00817/fullEEGfMRIMotor Imageryhigh gammanon-invasive |
| spellingShingle | Melissa M Smith Kurt E Weaver Thomas J Grabowski Rajesh P. N . Rao Felix eDarvas Non-invasive detection of high gamma band activity during motor imagery Frontiers in Human Neuroscience EEG fMRI Motor Imagery high gamma non-invasive |
| title | Non-invasive detection of high gamma band activity during motor imagery |
| title_full | Non-invasive detection of high gamma band activity during motor imagery |
| title_fullStr | Non-invasive detection of high gamma band activity during motor imagery |
| title_full_unstemmed | Non-invasive detection of high gamma band activity during motor imagery |
| title_short | Non-invasive detection of high gamma band activity during motor imagery |
| title_sort | non invasive detection of high gamma band activity during motor imagery |
| topic | EEG fMRI Motor Imagery high gamma non-invasive |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2014.00817/full |
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