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

 MRI["Structural MRI"] --> fids["Place Fiducials"];
 MRI["Structural MRI"] --> shape["Register point clouds"];

subgraph MRI Preprocessing

 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

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.

{{#mermaid:graph LR

   MEG["MEG Data"] --> Filtering;

subgraph PreProcessing

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

end

subgraph SAM PreProcessing

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

end

subgraph Power

 sam_wts --> 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

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

end }}

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.

{{#mermaid:graph LR

   MEG["MEG Data"] --> Filtering;

subgraph MEG PreProcessing

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

end

 ADC["ADC/Trigger Channels"] --> thresholdDetect;

subgraph ADC PreProcessing

 thresholdDetect --> add_markers;

end

subgraph SAM PreProcessing

 add_markers--> sam_cov;
 Art --> sam_cov;
 sam_cov --> sam_wts;
 style sam_cov fill:#fcf
 style sam_wts fill:#fcf
 click sam_cov "https://megcore.nih.gov/index.php/Sam_cov" "sam_cov documentation"
 click sam_wts "https://megcore.nih.gov/index.php/Sam_wts" "sam_wts documentation"

end

subgraph Induced Power

 sam_wts --> sam_3d["sam_3d/sam_3dc"];
 style sam_3d fill:#fcf
 click sam_3d "https://megcore.nih.gov/index.php/sam_3d_and_sam_3dc" "sam_3d/sam_3dc documentation"

end

subgraph Evoked/Event Related

 sam_wts --> sam_ers["sam_ers/sam_ersc"];
 sam_wts --> sam_4d["sam_4d/sam_4dc"];
 style sam_ers fill:#fcf
 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 }}

Localizing Epileptiform Activity

{{#mermaid:graph LR

MEG

   MEG["MEG Data"] --> sam_cov;

subgraph Phase I: Filter 20-70Hz

 sam_cov --> sam_wts;
 sam_wts --> sam_epi;

end

sam_epi --> NIFTIPeak["NIFTIPeak
Target
Identification"] NIFTIPeak --> sam_cov2

subgraph Phase II: Filter 1-150Hz

 sam_cov2 --> sam_wts2
 sam_wts2 --> DataEditor

end

 DataEditor --> Spike["Spike Identification"]

}}

{{#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.