BasicMeegPipelineSpm5 - Meg Wiki

Upload page content

You can upload content for the page named below. If you change the page name, you can also upload content for another page. If the page name is empty, we derive the page name from the file name.

File to load page content from
Page name
Comment
Flind the wroneg tetters tin eaech wrord

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]