Computational protocol: Do You Know What I Mean? Brain Oscillations and the Understanding of Communicative Intentions

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Protocol publication

[…] Magnetoencephalographic data were down-sampled to 341 Hz and analyzed by using an independent components analysis (ICA) approach detailed elsewhere (Mantini et al., ). Briefly, the algorithm automatically classifies the ICs and identifies artifactual components and components of brain origin.The number of artifactual ICs depends on the quality of each recording. On average, the algorithm identified 12 ± 4 artifact related components. Artifact components typically included hardware or environmental-injected noise, bad channels, contamination from high noise levels, and physiologic artifacts such as magnetocardiogram, eye blinks, and movements. To determine which ICs represented artifact, a classification procedure based on: (i) IC spectral properties; (ii) IC statistical properties; and (iii) comparison of the IC time courses with the corresponding time courses of the ECG, EOG, is adopted. See Supplemental Information in de Pasquale et al. () for details. A particularly important advantage of ICA based artifact rejection is that all of the 24 recorded trials for each condition and each subject are preserved, thus a reliable number of trials is maintained for the next step of the analysis. Usually, ICA based pipelines rely on the subtraction of artifactual ICs to increase the signal-to-noise ratio. Here, an alternative approach also used in other works by our group (de Pasquale et al., ; Betti et al., ; Marzetti et al., ) is pursued. The approach is based on reconstructing MEG signals by recombining only the ICs of brain origin. On average, the algorithm identified 15 ± 5 ICs of brain origin, each contributed by the activity of one or more dipole sources or patches (e.g., two sources with no time lag typically contribute to the same IC). Of course, in ICA based approaches a trade-off between including unwanted and excluding wanted signals has to be faced and selecting approximately 15 brain ICs is a reasonable compromise. This strategy has been shown to improve SNR in Mantini et al. ().After the decomposition through the fastICA algorithm with deflation approach and the classification steps, non-artifactual IC topographies were input to the weighted minimum-norm least squares (WMNLSs) linear inverse algorithm (Fuchs et al., ) implemented in Curry 6.0 (Neuroscan) and the corresponding source topography was localized. In this step, the volume conductor model was given by an individual boundary element method (BEM) (Fuchs et al., ) and the source space was modeled by a Cartesian 3D grid bounded by the subject anatomy as derived from individual MRI.Single subject source-space topographies were thus mapped onto a standard Montreal Neurological Institute (MNI) stereotaxic space by an affine transformation to allow spatial comparison across subjects. For each grid voxel the activity along each direction, and for each time sample, was obtained as a linear combination of non-artifactual IC time courses weighted by their related source-space topographies.Source activity magnitude was finally derived from the Cartesian components at each voxel; i.e., square root of the sum of the squared components. [...] Reaction time of pointing motor response was analyzed by a one-way ANOVA to identify possible statistical differences within the two communicative goals (declarative vs. imperative). Statistical analysis was performed using Statistica 6.1 software (Statsoft Italia Srl 2003). […]

Pipeline specifications

Software tools fastICA, Statistica
Applications Miscellaneous, Magnetic resonance imaging
Organisms Homo sapiens
Diseases Brain Diseases