Open AccessIdentifying spatially overlapping local cortical networks with MEG
Author(s) -
Duncan Keith Kawabata,
Hadjipapas Avgis,
Li Sheng,
Kourtzi Zoe,
Bagshaw Andy,
Barnes Gareth
Publication year - 2010
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.20912
Subject(s) - magnetoencephalography , visual cortex , neuroscience , hum , pattern recognition (psychology) , stimulus (psychology) , local field potential , electroencephalography , oscillation (cell signaling) , psychology , artificial intelligence , physics , nuclear magnetic resonance , computer science , chemistry , cognitive psychology , biochemistry , art , performance art , art history
Recent modelling studies (Hadjipapas et al. [2009]: Neuroimage 44:1290‐1303) have shown that it may be possible to distinguish between different neuronal populations on the basis of their macroscopically measured (EEG/MEG) mean field. We set out to test whether the different orientation columns contributing to a signal at a specific cortical location could be identified based on the measured MEG signal. We used 1.5deg square, static, obliquely oriented grating stimuli to generate sustained gamma oscillations in a focal region of primary visual cortex. We then used multivariate classifier methods to predict the orientation (left or right oblique) of the stimuli based purely on the time‐series data from this one location. Both the single trial evoked response (0–300 ms) and induced post‐transient power spectra (300–2,300 ms, 20–70 Hz band) due to the different stimuli were classifiable significantly above chance in 11/12 and 10/12 datasets respectively. Interestingly, stimulus‐specific information is preserved in the sustained part of the gamma oscillation, long after perception has occurred and all neuronal transients have decayed. Importantly, the classification of this induced oscillation was still possible even when the power spectra were rank‐transformed showing that the different underlying networks give rise to different characteristic temporal signatures. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.