Suggested Pipelines

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 }}

Basic Resting State MEG processing

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.

{{#mermaid:graph LR

subgraph PreProcessing

 MEG["MEG Data"] --> Filtering;
 Filtering --> Art["Artifact Removal"];

end

subgraph Power

 Art --> sam["sam_3d/sam_3dc"];

 style sam fill:#fcf
 click sam "https://megcore.nih.gov/index.php/Sam_3d_and_sam_3dc" "sam_3d/sam_3dc documentation"

end subgraph Connectivity

 Art --> sam_power;
 style sam_power fill:#fcf
 click sam_power "https://megcore.nih.gov/index.php/Sam_power" "sam_power documentation"

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 }}

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.