Difference between revisions of "Suggested Pipelines"

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style sam_ers fill:#fcf
 
style sam_ers fill:#fcf
 
click sam_ers "https://megcore.nih.gov/index.php/sam_ers_and_sam_ersc" "sam_ers/sam_ersc documentation"
 
click sam_ers "https://megcore.nih.gov/index.php/sam_ers_and_sam_ersc" "sam_ers/sam_ersc documentation"
 
style sam_4d fill:#fcf
 
click sam_4d "https://megcore.nih.gov/index.php/sam_4d_and_sam_4dc" "sam_4d/sam_4dc documentation"
 
end
 
end
 
}}
 
}}
   
   
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style sam_4d fill:#fcf
 
click sam_4d "https://megcore.nih.gov/index.php/sam_4d_and_sam_4dc" "sam_4d/sam_4dc documentation"
 
   
   

Revision as of 14:08, 20 March 2019

Master Pipeline

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






  1. Create covariance matrices using sam_cov.
  2. Compute beamformer weights with sam_wts.
  3. sam_3d uses the weights to compute volumetric images of activity estimates.
  4. View them with AFNI.
  5. It didn't work, go back and try again.
  6. Nope, still didn't work, try this instead.