Difference between revisions of "Suggested Pipelines"

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== Advanced Coregistration Pipeline ==
 
== Advanced Coregistration Pipeline ==
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{{#mermaid:graph LR
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MRI["Structural MRI"] --> fids["Place Approximate <br> Fiducials"];
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MRI--> FreeSurfer
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subgraph MRIproc
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FreeSurfer --> FSNormals.py
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style FSNormals.py fill:#fcf
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click FSNormals.py "https://megcore.nih.gov/index.php/FSNormals.py" "sam documentation"
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end
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MEG-->sam_cov
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style sam_cov fill:#fcf
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click sam_cov "https://megcore.nih.gov/index.php/Sam_cov" "sam documentation"
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subgraph MEGproc
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sam_cov-->sam_coreg
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sam_coreg--> sam_wts
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style sam_coreg fill:#fcf
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click sam_coreg "https://megcore.nih.gov/index.php/sam_coreg" "sam documentation"
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style sam_wts fill:#fcf
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click sam_wts "https://megcore.nih.gov/index.php/sam_wts" "sam documentation"
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end
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{{#mermaid:graph LR
 
{{#mermaid:graph LR

Revision as of 14:50, 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

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

{{#mermaid:graph LR

 MRI["Structural MRI"] --> fids["Place Approximate 
Fiducials"]; MRI--> FreeSurfer

subgraph MRIproc

 FreeSurfer --> FSNormals.py
 style FSNormals.py fill:#fcf
 click FSNormals.py "https://megcore.nih.gov/index.php/FSNormals.py" "sam documentation"

end

 MEG-->sam_cov
 style sam_cov fill:#fcf
 click sam_cov "https://megcore.nih.gov/index.php/Sam_cov" "sam documentation"

subgraph MEGproc

 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"

end






  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.