Basic MRI Pre-Processing Workflow
For any experiment where you wish to localize data to the brain, the first step is MRI pre-processing. First, MEG data must be co-registered to the space of the MRI, either by manually placing fiducial points on the MRI, or through a semi-automated method where a digital head shape is aligned with a head surface. (Other algorithmic techniques are possible, these will be discussed later). For the purpose of source space reconstruction, the head can be modeled either as a collection of spheres, one per channel, (MultiSphere) or in a realistic fashion using the Nolte model.
Basic Resting State MEG processing
Basic preprocessing of resting state MEG data includes filtering, and possibly artifact removal. Removing artifacts could consist of eliminating bad segments, or a more comprehensive process like ICA. When examining resting state data, the end goals is usually to examine either static measures of power, or connectivity. For connectivity, the output of SAM is a continuous time series, usually the Hilbert envelope of a band limited signal. Following calculation of this time series, other routines (such as ICA, seed based correlation, etc.) can be used to derive connectivity between regions.
Basic Task Based MEG Pipeline
In a task based analysis, you start with raw MEG data, as wells as data from the ADC channels - triggers, stimuli, and responses. Both of these must be pre-processed. Once your MEG data is marked appropriately, you can begin a SAM analysis. If you are interested in time-locked (evoked or event-related) signals, you can use either sam_4d or sam_4dc or sam_ers or sam_ersc, depending on exactly what you want as output. Alternatively, if you do not expect your signals to be time-locked, you can examine changes in induced power using sam_3d or sam_3dc.
Localizing Epileptiform Activity
Advanced Coregistration Pipeline
MRI["Structural MRI"] --> fids["Place Approximate
Fiducials"]; MRI--> FreeSurfer
FreeSurfer --> FSNormals.py style FSNormals.py fill:#fcf click FSNormals.py "https://megcore.nih.gov/index.php/FSNormals.py" "sam documentation"
MEG-->sam_cov style sam_cov fill:#fcf click sam_cov "https://megcore.nih.gov/index.php/Sam_cov" "sam documentation"
sam_cov-->sam_coreg sam_coreg--> sam_wts style sam_coreg fill:#fcf click sam_coreg "https://megcore.nih.gov/index.php/sam_coreg" "sam documentation" style sam_wts fill:#fcf click sam_wts "https://megcore.nih.gov/index.php/sam_wts" "sam documentation"
- Create covariance matrices using sam_cov.
- Compute beamformer weights with sam_wts.
- sam_3d uses the weights to compute volumetric images of activity estimates.
- View them with AFNI.
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