NDLI: Spatiotemporal analysis of the appearance of gamma-band Microstates in resting state MEG

Content Provider	IEEE Xplore Digital Library
Author	Kelsey, M. Prior, F.W. Larson-Prior, L.J.
Copyright Year	2015
Description	Author affiliation: Sch. of Med., Mallinckrodt Inst. of Radiol., Washington Univ., St. Louis, MO, USA (Kelsey, M.; Prior, F.W.; Larson-Prior, L.J.)
Abstract	Spatiotemporal analysis of EEG signal has revealed a rich set of methods to quantify neuronal activity using spatially global topographic templates, called Microstates. These methods complement more traditional spectral analysis, which uses band limited source data to determine defining differences in band power and peak characteristics. The high sampling rate and increased resistance to high frequency noise of MEG data offers an opportunity to explore the utility of spatiotemporal analysis over a wider spectrum than in EEG. In this work, we explore the utility of representing band limited MEG source data using established microstate techniques, especially in gamma frequency bands - a range yet unexplored using these techniques. We develop methods for gauging the goodness-offit achieved by resultant microstate templates and demonstrate sensor-level dispersion characteristics across wide-band signals as well as across signals filtered by canonical bands. These analyses reveal that, while high-frequency-band derived microstate templates are visually lawful, they fail to exhibit important explained variance and dispersion characteristics present in low- and full-band data necessary to meet the requirements of a microstate model.
Starting Page	2637
Ending Page	2640
File Size	1571082
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318933
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Surfaces Electroencephalography Dispersion Data models Brain modeling Spatiotemporal phenomena Broadband communication
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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