A Signal-Processing Pipeline for Magnetoencephalography Resting-State Networks Dante Mantini, 1–3 Stefania Della Penna, 1,2 Laura Marzetti, 1,2 Francesco de Pasquale, 1,2 Vittorio Pizzella, 1,2 Maurizio Corbetta, 1,2,4,5 and Gian Luca Romani 1,2 Abstract To study functional connectivity using magnetoencephalographic (MEG) data, the high-quality source-level re- construction of brain activity constitutes a critical element. MEG resting-state networks (RSNs) have been docu- mented 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, pro- vides 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 artifac- tual 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 recom- bination 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. Key words: artifact reduction; functional connectivity; independent component analysis (ICA); magnetoencepha- lography (MEG); resting-state network (RSN); signal processing; source localization Introduction F unctional connectivity investigations based on functional magnetic resonance imaging (fMRI) docu- mented that brain activity in the resting state is spatially orga- nized in a finite set of coherent patterns, namely resting-state networks (RSNs) (Fox and Raichle, 2007). The general concept that cortical and thalamo-cortical networks present specific os- cillatory signatures has generated a growing interest in linking fMRI RSNs and simultaneous electrophysiological measures (Laufs et al., 2003; Mantini et al., 2007). Even more interesting is the possibility to analyze intrinsic brain activity and to re- trieve RSNs directly from electrophysiological techniques, such as electroencephalography (EEG) and magnetoencepha- lography (MEG). Independent component analysis (ICA) on short-time Fourier transforms of resting-state MEG/EEG sig- nals has been proposed to find sources of intrinsic rhythmic ac- tivity within the cortex (Hyva ¨rinen et al., 2010). Although the results of this method on resting-state MEG data provided meaningful brain sources, no pattern of long-range connectiv- ity clearly resembling those obtained from fMRI data were found. Recently, MEG RSNs have been documented (de Pas- quale et al., 2010) using a processing pipeline based on tempo- ral ICA (Hironaga and Ioannides, 2007) for the source-level reconstruction of resting-state activity. However, it remains unknown whether alternative processing pipelines could be ef- fectively used for the detection of MEG RSNs and also to what extent the RSN results depend on the pipeline used. 1 Institute for Advanced Biomedical Technologies and 2 Department of Neuroscience and Imaging, ‘‘G. D’Annunzio University’’ Foundation, Chieti, Italy. 3 Laboratory for Neuro-Psychophysiology, K.U. Leuven Medical School, Leuven, Belgium. Departments of 4 Neurology and 5 Radiology, Washington University, St. Louis, Missouri. BRAIN CONNECTIVITY Volume 1, Number 1, 2011 ª Mary Ann Liebert, Inc. DOI: 10.1089/brain.2011.0001 49