Difference between revisions of "Suggested Pipelines"

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== Localizing Epileptiform Activity ==
== Localizing Epileptiform Activity ==

This describes localizing epileptiform activity from continuous datasets using excess kurtosis. First, a SAM analysis is performed with a relatively narrow bandwidth, 20-70Hz. The program sam_epi is used to produce images of kurtosis, and NIFTIpeak is used to find extrema in the images that may indicate voxels which contain spikes. Once targets are identified, the SAM analysis is essentially run again, this time with a wide bandwidth, on only those specific targets. DataEditor can read the weights for those targets and compute "virtual sensors" at those voxel locations. The epileptologist can then examine the time series and mark epileptic spikes.


{{#mermaid:graph LR
{{#mermaid:graph LR
Line 146: Line 148:
}}
}}


== Advanced Coregistration SAM Pipeline ==


There is a limit to the accuracy with which MEG and MRI data can be coregistered using fiducial markers alone. One significant confounding issue is that the brain will be in a slightly different position inside the head in a seated position vs. a supine position. This pipeline describes using sam_coreg to refine the approximate fiducial placements. Once sam_coreg has been performed, the user can either do a traditional analysis using the SAM tools discussed previously, or make use of the programs that leverage the cortical data (patch_wts and roi_wts).




{{#mermaid:graph LR
{{#mermaid:graph LR


MRI["Structural MRI"] --> fids["Place Approximate <br> Fiducials"];
marks --> Covariance;


subgraph MEG Preprocessing
subgraph MRIproc
fids --> FreeSurfer
raw[Raw MEG data] --> filter[Basic Filtering];
FreeSurfer --> FSNormals.py
adc[Raw ADC/PPT<br>data] --> ThresholdDetect;
⚫
style FSNormals.py fill:#fcf
ThresholdDetect --> marks[Create Markers];
⚫
click FSNormals.py "https://megcore.nih.gov/index.php/FSNormals.py" "FSNormals.py documentation"
filter --> meg[MEG Data];
end
end


FSNormals.py --> sam_coreg
subgraph MEG Data Statistics
⚫
MEG-->sam_cov
meg --> Filter[Band-pass<br>filter];
⚫
style sam_cov fill:#fcf
Filter --> Covariance;
⚫
click sam_cov "https://megcore.nih.gov/index.php/Sam_cov" "sam_cov documentation"
end


⚫
subgraph MEGproc
}}
⚫
sam_cov-->sam_coreg

sam_coreg--> patch_wts
{{#mermaid:graph LR
sam_coreg--> roi_wts
subgraph Synthetic Aperture Magnetometry
Covariance --> Beamformer;
sam_coreg--> sam_wts
style sam_coreg fill:#fcf
head[Head Model] --> Beamformer;
click sam_coreg "https://megcore.nih.gov/index.php/sam_coreg" "sam_coreg documentation"
Beamformer --> image["3D Images"];
style patch_wts fill:#fcf
click patch_wts "https://megcore.nih.gov/index.php/patch_wts" "patch_wts documentation"
style roi_wts fill:#fcf
click roi_wts "https://megcore.nih.gov/index.php/roi_wts" "roi_wts documentation"
style sam_wts fill:#fcf
click sam_wts "https://megcore.nih.gov/index.php/sam_wts" "sam_wts documentation"
end
end
}}


subgraph SAM
{{#mermaid:graph LR
sam_wts-->sam_3d["sam_3d or sam_3dc"]
⚫
subgraph SAM Workflow
sam_wts-->sam_4d["sam_4d or sam_4dc"]
⚫
sam_cov --> sam_wts;
sam_wts --> sam_3d;
sam_wts-->sam_ers["sam_ers or sam_ersc"]
sam_wts-->sam_power["sam_power"]
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
style sam_3d fill:#fcf
click sam_3d "https://megcore.nih.gov/index.php/Sam_3d" "sam documentation"
click sam_3d "https://megcore.nih.gov/index.php/sam_3d_and_sam_3dc" "sam_3d documentation"
style AFNI fill:#fcf
style sam_4d fill:#fcf
click AFNI "https://afni.nimh.nih.gov/" "The AFNI website"
click sam_4d "https://megcore.nih.gov/index.php/sam_4d_and_sam_4dc" "sam_4d documentation"
style sam_ers fill:#fcf
click sam_ers "https://megcore.nih.gov/index.php/roi_wts" "sam_ers documentation"
style sam_power fill:#fcf
click sam_power "https://megcore.nih.gov/index.php/sam_power" "sam_power documentation"
end
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.

Latest revision as of 15:29, 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

This describes localizing epileptiform activity from continuous datasets using excess kurtosis. First, a SAM analysis is performed with a relatively narrow bandwidth, 20-70Hz. The program sam_epi is used to produce images of kurtosis, and NIFTIpeak is used to find extrema in the images that may indicate voxels which contain spikes. Once targets are identified, the SAM analysis is essentially run again, this time with a wide bandwidth, on only those specific targets. DataEditor can read the weights for those targets and compute "virtual sensors" at those voxel locations. The epileptologist can then examine the time series and mark epileptic spikes.

Advanced Coregistration SAM Pipeline

There is a limit to the accuracy with which MEG and MRI data can be coregistered using fiducial markers alone. One significant confounding issue is that the brain will be in a slightly different position inside the head in a seated position vs. a supine position. This pipeline describes using sam_coreg to refine the approximate fiducial placements. Once sam_coreg has been performed, the user can either do a traditional analysis using the SAM tools discussed previously, or make use of the programs that leverage the cortical data (patch_wts and roi_wts).

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