=== Outline analysis pipeline for Neuromag MEG/EEG data in SPM ===
 1. Use Neuromag's Maxfilter  (including Trans Default for Sensor-Level analyses)
 1. Write magnetometers, gradiometers and EEG to separate files for parallel preprocessing
 1. Call EEGLAB from SPM to project out ICA components that correlate with measured EOG/ECG
 1. Usual filtering, epoching, thresholding, averaging…
 1. 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
 1. 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)
 1. Create forward models by calling Brainstorm (concentric spheres, overlapping-spheres, BEMs)
 1. 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)
 1. 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)
 1. (Compare different models using the Bayesian model evidence, eg Henson et al, 2009a)
 1. Evaluate a time-frequency contrast of source energy (which can include induced energy, Friston et al, 2006)
 1. 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
 1. (A similar pathway can also be used for time-frequency analysis using wavelets)
 1. 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]