Suggested Pipelines: Difference between revisions
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== Basic MRI Pre-Processing Workflow == |
== Basic MRI Pre-Processing Workflow == |
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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. |
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{{#mermaid:graph LR |
{{#mermaid:graph LR |
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Revision as of 09:12, 20 March 2019
Master Pipeline
{{#mermaid:graph LR
DoStuff --> DoMoreStuff; DoMoreStuff --> DoEvenMoreStuff; DoEvenMoreStuff --> PublishNaturePaper;
}}
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.
{{#mermaid:graph LR subgraph MRI Preprocessing
MRI["Structural MRI"] --> fids["Place Fiducials"]; MRI["Structural MRI"] --> shape["Register point clouds"]; fids --> hull["Process with orthohull"]; shape --> hull;
style hull fill:#fcf click hull "https://megcore.nih.gov/index.php/Head_Localization_and_MRI_Coregistration" "orthohull documentation"
end
subgraph MultiSphere
hull --> localSpheres; localSpheres --> MultiSphere["default.hdm"];
end
subgraph Nolte
hull --> Nolte["hull.shape"];
end }}
{{#mermaid:graph LR
marks --> Covariance;
subgraph MEG Preprocessing
raw[Raw MEG data] --> filter[Basic Filtering]; adc[Raw ADC/PPT
data] --> ThresholdDetect; ThresholdDetect --> marks[Create Markers]; filter --> meg[MEG Data];
end
subgraph MEG Data Statistics
meg --> Filter[Band-pass
filter]; Filter --> Covariance;
end
}}
{{#mermaid:graph LR subgraph Synthetic Aperture Magnetometry
Covariance --> Beamformer; head[Head Model] --> Beamformer; Beamformer --> image["3D Images"];
end }}
{{#mermaid:graph LR subgraph SAM Workflow
sam_cov --> sam_wts; sam_wts --> sam_3d; sam_3d --> AFNI; AFNI --> sam_wts; AFNI --> sam_cov;
style sam_cov fill:#fcf click sam_cov "https://megcore.nih.gov/index.php/Sam_cov" "sam documentation" style sam_wts fill:#fcf click sam_wts "https://megcore.nih.gov/index.php/Sam_wts" "sam documentation" style sam_3d fill:#fcf click sam_3d "https://megcore.nih.gov/index.php/Sam_3d" "sam documentation" style AFNI fill:#fcf click AFNI "https://afni.nimh.nih.gov/" "The AFNI website"
end }}
- 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.