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01.04.2012 | Original Article

MEG-SIM: A Web Portal for Testing MEG Analysis Methods using Realistic Simulated and Empirical Data

verfasst von: C. J. Aine, L. Sanfratello, D. Ranken, E. Best, J. A. MacArthur, T. Wallace, K. Gilliam, C. H. Donahue, R. Montaño, J. E. Bryant, A. Scott, J. M. Stephen

Erschienen in: Neuroinformatics | Ausgabe 2/2012

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Abstract

MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes (http://cobre.mrn.org/megsim/). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis.

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Titel: MEG-SIM: A Web Portal for Testing MEG Analysis Methods using Realistic Simulated and Empirical Data
verfasst von: C. J. Aine
L. Sanfratello
D. Ranken
E. Best
J. A. MacArthur
T. Wallace
K. Gilliam
C. H. Donahue
R. Montaño
J. E. Bryant
A. Scott
J. M. Stephen
Publikationsdatum: 01.04.2012
Verlag: Springer-Verlag
Erschienen in: Neuroinformatics / Ausgabe 2/2012
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-011-9132-z

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