BasicMeegPipelineSpm5 - Meg Wiki

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location: BasicMeegPipelineSpm5

Outline analysis pipeline for Neuromag MEG/EEG data in SPM

  1. Use Neuromag's Maxfilter (including Trans Default for Sensor-Level analyses)
  2. Write magnetometers, gradiometers and EEG to separate files for parallel preprocessing
  3. Call EEGLAB from SPM to project out ICA components that correlate with measured EOG/ECG
  4. Usual filtering, epoching, thresholding, averaging…
  5. Write out 2D sensor x 1D time images for each trial or subject, and localise reliable voxel-wise effects in 3D space-time across trials/subjects using Random Field Theory for multiple comparisons
  6. Automatically normalise and segment MRI, and create meshes for cortex, skull and scalp (“canonical” cortical mesh is an inverse-normalised template mesh, Mattout et al, 2007)
  7. Create forward models by calling Brainstorm (concentric spheres, overlapping-spheres, BEMs)
  8. Invert forward models using Multiple Sparse Priors (Friston et al, 2008), an important new approach that uses ~750 local cortical patches as source priors…

    … including ability to optimise source priors by pooling over subjects (Litvak & Friston, 2008)… and (in near future) the ability to add multiple fMRI-cluster source priors (Henson et al, submitted)

  9. Then simultaneously re-invert (fuse) forward models for each sensor-type, which automatically weights each sensor-type (magnetometers, gradiometers, EEG) in a principled fashion (Henson et al, 2009b)
  10. (Compare different models using the Bayesian model evidence, eg Henson et al, 2009a)
  11. Evaluate a time-frequency contrast of source energy (which can include induced energy, Friston et al, 2006)
  12. Write a 3D image in template (MNI) space for that contrast, and perform usual SPM voxel-wise analysis across trials/subejcts to localise effects in the brain
  13. (A similar pathway can also be used for time-frequency analysis using wavelets)
  14. A further possibility is to assume a number of ECDs (eg seeded by distributed inversion above), and use Dynamic Causal Modelling (DCM) to make inferences about changes in effective connectivity …[more will follow on DCM]