Suggested Pipelines: Difference between revisions
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{{#mermaid:graph LR |
{{#mermaid:graph LR |
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MEG Data --> Filtering |
MEG Data --> Filtering; |
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Filtering --> Art["Artifact Removal"] |
Filtering --> Art["Artifact Removal"]; |
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subgraph Power |
subgraph Power |
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Filtering --> sam_3d |
Filtering --> sam["sam_3d/sam_3dc"]; |
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Filtering --> sam_3dc |
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end |
end |
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subgraph Connectivity |
subgraph Connectivity |
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Filtering --> sam_power |
Filtering --> sam_power; |
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end |
end |
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}} |
}} |
Revision as of 10:19, 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
When examining resting state data, the end goals is usually to examine either static measures of
- 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.
- It didn't work, go back and try again.
- Nope, still didn't work, try this instead.