Open AccessPrefrontal high gamma during a magnetoencephalographic working memory task
Author(s) -
Carver Frederick W.,
Rubinstein Dani Y.,
Gerlich Alan H.,
Fradkin Samantha I.,
Holroyd Tom,
Coppola Richard
Publication year - 2019
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.24489
Subject(s) - magnetoencephalography , working memory , prefrontal cortex , neuroscience , task (project management) , beta (programming language) , psychology , alpha (finance) , resting state fmri , cognition , electroencephalography , computer science , developmental psychology , construct validity , management , economics , programming language , psychometrics
In human electrophysiology research, the high gamma part of the power spectrum (~>60 Hz) is a relatively new area of investigation. Despite a low signal‐to‐noise ratio, evidence exists that it contains significant information about activity in local cortical networks. Here, using magnetoencephalography (MEG), we found high gamma activity when comparing data from an n‐back working memory task to resting data in a large sample of normal volunteers. Initial analysis of power spectra from 0‐back, 2‐back, and rest trials showed three frequency bands exhibiting task‐related differences: alpha, beta, and high gamma. Unlike alpha and beta, the high gamma spectrum was broad, without a peak at a single frequency. In addition, power in high gamma was highest for the 2‐back and lowest during rest, while the opposite pattern occurred in the other bands. Beamformer source localization of each of the three frequency bands revealed a distinct set of sources for high gamma. These included several regions of prefrontal cortex that exhibited greater power when both n‐back conditions were compared to rest. A subset of these regions had more power when the 2‐back was compared to 0‐back, which indicates a role in working memory performance. Our results show that high gamma will be important for understanding cortical processing during cognitive and other tasks. Furthermore, data from human intracortical recordings suggest that high gamma is the aggregate of spiking in local cortical networks, which implies that MEG could serve to bridge experimental modalities by noninvasively observing task‐related modulation of spiking rates.