==Outline of analysis pipeline for Neuromag MEG/EEG data in SPM==

Use Neuromag's Maxfilter  (including Trans Default for Sensor-Level analyses)

Write magnetometers, gradiometers and EEG to separate files for parallel preprocessing

Call EEGLAB from SPM to project out ICA components that correlate with measured EOG/ECG

Usual filtering, epoching, thresholding, averaging…

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

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)

Create forward models by calling Brainstorm (concentric spheres, overlapping-spheres, BEMs) 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 (Flandin et al, in prep)

 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, submitted)

(Compare different models using the Bayesian model evidence, eg Henson et al, in press)

Evaluate a time-frequency contrast of source energy (which can include induced energy, Friston et al, 2006)

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

(A similar pathway can also be used for time-frequency analysis using wavelets)

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]