A signal-processing pipeline for magnetoencephalography resting-state networks

Dante Mantini, Stefania Della Penna, Laura Marzetti, Francesco De Pasquale, Vittorio Pizzella, Maurizio Corbetta, Gian Luca Romani

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

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.

Original languageEnglish
Pages (from-to)49-59
Number of pages11
JournalBrain connectivity
Volume1
Issue number1
DOIs
StatePublished - 2011

Keywords

  • artifact reduction
  • functional connectivity
  • independent component analysis (ICA)
  • magnetoencephalography (MEG)
  • resting-state network (RSN)
  • signal processing
  • source localization

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