82013-03-08 10:02:45localhostconverted to 1.6 markup72009-07-13 11:15:55RikHenson62009-01-27 13:11:32RikHenson52009-01-27 12:29:01RikHenson42009-01-27 12:28:46RikHenson32009-01-27 12:28:40RikHenson22009-01-27 12:28:22RikHenson12009-01-27 12:27:53RikHenson

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 (Henson et al, submitted) 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) (Compare different models using the Bayesian model evidence, eg Henson et al, 2009a) 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]