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Title: A Signal-Processing Pipeline for Magnetoencephalography Resting-State Networks
Authors: Mantini, Dante ×
Della Penna, S.
Marzetti, L.
de Pasquale, F.
Pizzella, V.
Corbetta, M.
Romani, G.L. #
Issue Date: 2011
Publisher: Mary Ann Liebert, Inc. Publishers
Series Title: Brain Connectivity vol:1 issue:1 pages:49-59
Abstract: To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level reconstruction
of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been documented
by means of a dedicated processing pipeline: MEG recordings are decomposed by independent
component analysis (ICA) into artifact and brain components (ICs); next, the channel maps associated with the
latter ones are projected into the source space and the resulting voxel-wise weights are used to linearly combine
the IC time courses. An extensive description of the proposed pipeline is provided here, along with an assessment
of its performances with respect to alternative approaches. The following investigations were carried out: (1) ICA
decomposition algorithm. Synthetic data are used to assess the sensitivity of the ICA results to the decomposition
algorithm, by testing FastICA, INFOMAX, and SOBI. FastICA with deflation approach, a standard solution, provides
the best decomposition. (2) Recombination of brain ICs versus subtraction of artifactual ICs (at the channel level).
Both the recombination of the brain ICs in the sensor space and the classical procedure of subtracting the artifactual
ICs from the recordings provide a suitable reconstruction, with a lower distortion using the latter approach.
(3) Recombination of brain ICs after localization versus localization of artifact-corrected recordings. The brain IC recombination
after source localization, as implemented in the proposed pipeline, provides a lower source-level signal
distortion. (4) Detection of RSNs. The accuracy in source-level reconstruction by the proposed pipeline is confirmed
by an improved specificity in the retrieval of RSNs from experimental data.
ISSN: 2158-0014
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Group Neurophysiology
× corresponding author
# (joint) last author

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