NDLI: Post-processing for spectral coherence of magnetoencephalogram background activity: Application to Alzheimer's disease

Content Provider	IEEE Xplore Digital Library
Author	Escudero, J. Anastasiou, A. Fernandez, A.
Copyright Year	2014
Description	Author affiliation: Sch. of Eng., Univ. of Edinburgh, Edinburgh, UK (Escudero, J.) \|\| Coll. of Med., Swansea Univ., Swansea, UK (Anastasiou, A.) \|\| Dept. de Psiquiatria y Psicologia Medica, Univ. Complutense de Madrid, Madrid, Spain (Fernandez, A.)
Abstract	Estimating the connectivity between magnetoencephalogram (MEG) signals provides an excellent opportunity to analyze whole brain functional integration across a spectrum of conditions from health to disease. For this purpose, spectral coherence has been used widely as an easy-to-interpret metric of signal coupling. However, a number of systematic effects may influence the estimations of spectral coherence and subsequent inferences about brain activity. In this pilot study, we focus on the potentially confounding effects of the field spread and the on-going dynamic temporal variability inherent in the signals. We propose two simple post-processing approaches to account for these: 1) a jack-knife procedure to account for the variance in the estimation of spectral coherence; and 2) a detrending technique to reduce its dependence on sensor proximity. We illustrate the effect of these techniques in the estimation of MEG spectral coherence in the α band for 36 patients with Alzheimer's disease and 26 control subjects.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	6345
Ending Page	6348
File Size	1346694
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6945079
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Coherence Alzheimer's disease Electroencephalography Couplings Magnetic recording
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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