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| = Tutorial 27: Advanced scripting = | = Tutorial 28: Scripting = |
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| This tutorial explains how to use the Brainstorm scripting interface to run a full analysis, from the raw recordings to the source reconstruction. It is based on a median nerve stimulation experiment recorded at the Montreal Neurological Institute in 2011 with a CTF MEG 275 system. The sample dataset contains 6 minutes of recordings at 1200Hz for one subject and includes 100 stimulations of each arm. The tutorial follows the analysis steps detailed in the three advanced tutorials in the category [[http://neuroimage.usc.edu/brainstorm/Tutorials|Processing continuous recordings]]. You should read them before reading this tutorial, to have the explanations that go with the analysis steps. |
The previous tutorials explained how to use Brainstorm in an interactive way to process one subject with two acquisition runs. In the context of a typical neuroimaging study, you may have tens or hundreds of subjects to process in the same way, it is unrealistic to do everything manually. Some parts of the analysis can be processed in batches with no direct supervision, others require more attention. This tutorial introduces tools and tricks that will help you assemble an efficient analysis pipeline. |
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| == Creating the analysis pipeline == Select the menu File > Create new protocol. Name it "'''TutorialScript'''" and select the options: |
== Workflow == This section proposes a standard workflow for processing a full group study with Brainstorm. It contains the same steps of analysis as the introduction tutorials, but separating what can be done automatically from what should be done manually. |
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| * "'''No, use individual anatomy'''", * "'''Yes, use one channel file per subject'''". |
* '''Prototype''': Start by processing one or two subjects completely '''interactively''' (exactly like in the introduction tutorials). Use the few pilot subjects that you have for your study to prototype the analysis pipeline and check manually all the intermediate stages. Take notes of what you're doing along the way, so that you can later write a script that reproduces the same operations. * '''Anatomical fiducials''': Set NAS/LPA/RPA and compute the MNI transformation for each subject. * '''Segmentation''': Run FreeSurfer/BrainSuite to get surfaces and atlases for all the subjects. * '''File > Batch MRI fiducials''': This menu prompts for the selection of the fiducials for all the subjects and saves a file '''fiducials.m''' in each segmentation folder. You will not have to redo this even if you have to start over your analysis from the beginning. * '''Script''': Write a loop that calls the process '''Import anatomy folder''' for all the subjects. * '''Alternatives''': Create and import the subjects one by one and set the fiducials at the import time. Or use the default anatomy for all the subjects (or use [[Tutorials/TutWarping|warped templates]]). |
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| To start building your analysis pipeline, just click on the "'''Run'''" button in the Process1 tab. We don't need any file in input, as we are going to select the files to import in the script itself. Then add all the processes listed below. The output of each process is the input of the following one, this is why the order of the processes is important. | * '''Script 1''': Pre-processing: Loop on the subjects and the acquisition runs. * '''Create link to raw files''': Link all the acquisition runs and the noise recordings to the database * '''Event markers''': Read and group triggers from digital and analog channel, fix stimulation delays * '''Evaluation''': Power spectrum density on all the recordings to evaluate their quality. * '''Pre-processing''': Notch filter, sinusoid removal, band-pass filter. * '''Evaluation''': Power spectrum density on all the recordings to make sure the filters worked well * '''Cleanup''': Delete the links to the original files (the filtered ones are copied in the database). * '''Detect artifacts''': Detect heartbeats, Detect eye blinks, Remove simultaneous * '''Compute SSP''': Heartbeats, Blinks (this selects the first component of each decomposition) * '''Compute ICA''': If you have some types of artifacts you'd like to remove with ICA. * '''Screenshots''': Check the MRI/sensors registration, PSD before and after corrections, SSP. * '''Export the report to HTML''': One report per subject, or one report for all the subjects. * '''Manual inspection''': For each run. * '''Check the reports''': Information messages (number of events, errors and warnings) and screen captures (registration problems, especially obvious noisy channels, incorrect SSP topographies) * Mark the bad channels * '''Fix the SSP''': For the suspicious runs, open the file viewer, adjust the list of blink and cardiac events, remove and recompute the SSP decompositions, manually select the components. * '''Detect other artifacts''': Run the process on all the runs of all the subjects at once * '''Mark bad segments''': Review the detected artifacts, keep only the ones you want to remove, and then mark the event type as BAD. Review quickly the rest of the file and check that there are no other important artifacts. * '''Additional SSP''': If you find one type of artifact that repeats (typically saccades and SQUID jumps), you can create additional SSP projectors, either with the "SSP: Generic" or directly from the topographies (right-click on the topography figure > Create SSP). * '''Script 2''': Epoching, averaging, sources, time-frequency, etc. * Importing * Averaging * Noise covariance: * Head model * Sources * Time-frequency * Statistics * '''Screenshots''': Averages (time series, topographies, sources at one time point). * Manual inspection: * Check the reports: Check the number of imported epochs in each condition, check the averaged * Regions of interest: If not using predefined regions from an atlas. * Script 3: ROI-based analysis, additional statistics. |
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| === Import anatomy > Import anatomy folder === * Folder to import: sample_raw/Anatomy File format: "FreeSurfer folder" * Fiducials: Copy what is indicated below. This is a reason it is usually easier to do this step in interactive mode, and then run only the script starting from the next step. * Input: None; Output: None |
== Script generation == http://neuroimage.usc.edu/brainstorm/Tutorials/PipelineEditor |
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| {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_import_freesurfer.gif|process_import_freesurfer.gif|class="attachment"}} | == Script edition == - Add loops, load files, ... |
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| === Import recordings > Create link to raw file === * File to import: Select the folder sample_raw/Data/subj001_somatosensory_20111109_01_AUX-f.ds * Input: None; Output: Raw file |
Loops: http://neuroimage.usc.edu/forums/showthread.php?2429-Problem-using-tags |
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| {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_import_data_raw.gif|process_import_data_raw.gif|class="attachment"}} === Pre-process > Notch filter === Input: Raw file ; Output: Raw file (new) {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_sin_remove.gif|process_sin_remove.gif|class="attachment"}} === Events > Detect eye blinks === Input: Raw file ; Output: Raw file {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_evt_detect_eog.gif|process_evt_detect_eog.gif|class="attachment"}} === Events > Compute SSP: eye blinks === Input: Raw file ; Output: Raw file {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_ssp_eog.gif|process_ssp_eog.gif|class="attachment"}} === Import recordings > Import MEG/EEG : Events === Input: Raw file ; Output: 199 epochs in 2 conditions {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_import_data_event.gif|process_import_data_event.gif|class="attachment"}} === Pre-process > Remove DC offset === Input: 199 epochs ; Output: 199 epochs {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_baseline.gif|process_baseline.gif|class="attachment"}} === Pre-process > Add time offset === Input: 199 epochs ; Output: 199 epochs {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_timeoffset.gif|process_timeoffset.gif|class="attachment"}} === Sources > Compute noise covariance === Since the epochs are currently selected and pre-processed: we can use them to estimate the noise covariance matrix before we move on with the calculation of the average. Input: 199 epochs ; Output: 199 epochs {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_noisecov.gif|process_noisecov.gif|class="attachment"}} === Average > Average files === Input: 199 epochs ; Output: 2 averages {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_average.gif|process_average.gif|class="attachment"}} === File > Save snapshot: Sensors/MRI registration === Input: 2 averages ; Output: 2 averages {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_snapshot.gif|process_snapshot.gif|class="attachment"}} === File > Save snapshot: Recordings time series === Input: 2 averages ; Output: 2 averages {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_snapshot2.gif|process_snapshot2.gif|class="attachment"}} === Sources > Compute head model === Input: 2 averages ; Output: 2 averages {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_headmodel.gif|process_headmodel.gif|class="attachment"}} === Sources > Compute sources === Input: 2 averages ; Output: all the source files (1 raw + 2 average + 199 epochs = 202 files) {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=process_inverse.gif|process_inverse.gif|class="attachment"}} == Save the pipeline == === Save in current workspace === Use the menus on top of the pipeline editor to save this list of processes on your computer. The menu "Save > New..." will create an entry readily available in your Brainstorm installation in the Load section of the same menu. {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=savePipeline.gif|savePipeline.gif|class="attachment"}} |
== File manipulation == * Modify a structure manually: Export to Matlab/Import from Matlab * File manipulation: file_short, file_fullpath, in_bst_*... * Documentation of all file structures: point at the appropriate tutorials * Select files from the database (with bst_get and processes) |
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| {{{ % Script generated by Brainstorm v3.2 (22-Jul-2014) % Input files sFiles = []; SubjectNames = {... 'Subject01'}; RawFiles = {... 'C:\Work\RawData\Tutorials\sample_raw\Anatomy', ... 'C:\Work\RawData\Tutorials\sample_raw\Data\subj001_somatosensory_20111109_01_AUX-f.ds'}; % Start a new report bst_report('Start', sFiles); % Process: Import anatomy folder sFiles = bst_process('CallProcess', 'process_import_anatomy', ... sFiles, [], ... 'subjectname', SubjectNames{1}, ... 'mrifile', {RawFiles{1}, 'FreeSurfer'}, ... 'nvertices', 15000, ... 'nas', [127, 212, 123], ... 'lpa', [55, 124, 119], ... 'rpa', [200, 129, 114], ... 'ac', [129, 137, 157], ... 'pc', [129, 113, 157], ... 'ih', [129, 118, 209]); % Process: Create link to raw file sFiles = bst_process('CallProcess', 'process_import_data_raw', ... sFiles, [], ... 'subjectname', SubjectNames{1}, ... 'datafile', {RawFiles{2}, 'CTF'}, ... 'channelreplace', 1, ... 'channelalign', 1); % Process: Notch filter: 60Hz 120Hz 180Hz sFiles = bst_process('CallProcess', 'process_notch', ... sFiles, [], ... 'freqlist', [60, 120, 180], ... 'sensortypes', 'MEG, EEG', ... 'read_all', 0); % Process: Detect eye blinks sFiles = bst_process('CallProcess', 'process_evt_detect_eog', ... sFiles, [], ... 'channelname', 'EEG058', ... 'timewindow', [], ... 'eventname', 'blink'); % Process: Detect heartbeats sFiles = bst_process('CallProcess', 'process_evt_detect_ecg', ... sFiles, [], ... 'channelname', 'EEG057', ... 'timewindow', [], ... 'eventname', 'cardiac'); % Process: SSP EOG: blink sFiles = bst_process('CallProcess', 'process_ssp_eog', ... sFiles, [], ... 'eventname', 'blink', ... 'sensortypes', 'MEG, EEG', ... 'usessp', 0); % Process: Import MEG/EEG: Events sFiles = bst_process('CallProcess', 'process_import_data_event', ... sFiles, [], ... 'subjectname', SubjectNames{1}, ... 'condition', '', ... 'eventname', 'left, right', ... 'timewindow', [], ... 'epochtime', [-0.1, 0.3], ... 'createcond', 1, ... 'ignoreshort', 1, ... 'usectfcomp', 1, ... 'usessp', 1, ... 'freq', [], ... 'baseline', [-0.1, -0.0008333333333]); % Process: Add time offset: -4.20ms sFiles = bst_process('CallProcess', 'process_timeoffset', ... sFiles, [], ... 'offset', -0.0042, ... 'overwrite', 1); % Process: Compute noise covariance sFiles = bst_process('CallProcess', 'process_noisecov', ... sFiles, [], ... 'baseline', [-0.1042, 0], ... 'dcoffset', 1, ... 'method', 1, ... % Full noise covariance matrix 'copycond', 0, ... 'copysubj', 0); % Process: Average: By condition (subject average) sFiles = bst_process('CallProcess', 'process_average', ... sFiles, [], ... 'avgtype', 3, ... 'avg_func', 1, ... % Arithmetic average: mean(x) 'keepevents', 0); % Process: Snapshot: Sensors/MRI registration sFiles = bst_process('CallProcess', 'process_snapshot', ... sFiles, [], ... 'target', 1, ... % Sensors/MRI registration 'modality', 1, ... % MEG (All) 'orient', 1, ... % left 'time', 0, ... 'contact_time', [0, 0.1], ... 'contact_nimage', 12, ... 'comment', 'MEG/MRI Registration'); % Process: Snapshot: Recordings time series sFiles = bst_process('CallProcess', 'process_snapshot', ... sFiles, [], ... 'target', 5, ... % Recordings time series 'modality', 1, ... % MEG (All) 'orient', 1, ... % left 'time', 0, ... 'contact_time', [0, 0.1], ... 'contact_nimage', 12, ... 'comment', 'Evoked response'); % Process: Compute head model sFiles = bst_process('CallProcess', 'process_headmodel', ... sFiles, [], ... 'comment', '', ... 'sourcespace', 1, ... 'meg', 3, ... % Overlapping spheres 'eeg', 3, ... % OpenMEEG BEM 'ecog', 2, ... % OpenMEEG BEM 'seeg', 2, ... 'openmeeg', struct(... 'BemFiles', {{}}, ... 'BemNames', {{'Scalp', 'Skull', 'Brain'}}, ... 'BemCond', [1, 0.0125, 1], ... 'BemSelect', [1, 1, 1], ... 'isAdjoint', 0, ... 'isAdaptative', 1, ... 'isSplit', 0, ... 'SplitLength', 4000)); % Process: Compute sources sFiles = bst_process('CallProcess', 'process_inverse', ... sFiles, [], ... 'comment', '', ... 'method', 1, ... % Minimum norm estimates (wMNE) 'wmne', struct(... 'NoiseCov', [], ... 'InverseMethod', 'wmne', ... 'ChannelTypes', {{}}, ... 'SNR', 3, ... 'diagnoise', 0, ... 'SourceOrient', {{'fixed'}}, ... 'loose', 0.2, ... 'depth', 1, ... 'weightexp', 0.5, ... 'weightlimit', 10, ... 'regnoise', 1, ... 'magreg', 0.1, ... 'gradreg', 0.1, ... 'eegreg', 0.1, ... 'ecogreg', 0.1, ... 'seegreg', 0.1, ... 'fMRI', [], ... 'fMRIthresh', [], ... 'fMRIoff', 0.1, ... 'pca', 1), ... 'sensortypes', 'MEG, MEG MAG, MEG GRAD, EEG', ... 'output', 1); % Kernel only: shared % Save and display report ReportFile = bst_report('Save', sFiles); bst_report('Open', ReportFile); }}} |
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| {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=report1.gif|report1.gif|class="attachment"}} {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawScript?action=AttachFile&do=get&target=report2.gif|report2.gif|class="attachment"}} <<EmbedContent("http://neuroimage.usc.edu/bst/get_prevnext.php?prev=Tutorials/Connectivity")>> |
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Tutorial 28: Scripting
[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE]
Authors: Francois Tadel, Elizabeth Bock, Sylvain Baillet
The previous tutorials explained how to use Brainstorm in an interactive way to process one subject with two acquisition runs. In the context of a typical neuroimaging study, you may have tens or hundreds of subjects to process in the same way, it is unrealistic to do everything manually. Some parts of the analysis can be processed in batches with no direct supervision, others require more attention. This tutorial introduces tools and tricks that will help you assemble an efficient analysis pipeline.
Workflow
This section proposes a standard workflow for processing a full group study with Brainstorm. It contains the same steps of analysis as the introduction tutorials, but separating what can be done automatically from what should be done manually.
Prototype: Start by processing one or two subjects completely interactively (exactly like in the introduction tutorials). Use the few pilot subjects that you have for your study to prototype the analysis pipeline and check manually all the intermediate stages. Take notes of what you're doing along the way, so that you can later write a script that reproduces the same operations.
Anatomical fiducials: Set NAS/LPA/RPA and compute the MNI transformation for each subject.
Segmentation: Run FreeSurfer/BrainSuite to get surfaces and atlases for all the subjects.
File > Batch MRI fiducials: This menu prompts for the selection of the fiducials for all the subjects and saves a file fiducials.m in each segmentation folder. You will not have to redo this even if you have to start over your analysis from the beginning.
Script: Write a loop that calls the process Import anatomy folder for all the subjects.
Alternatives: Create and import the subjects one by one and set the fiducials at the import time. Or use the default anatomy for all the subjects (or use warped templates).
Script 1: Pre-processing: Loop on the subjects and the acquisition runs.
Create link to raw files: Link all the acquisition runs and the noise recordings to the database
Event markers: Read and group triggers from digital and analog channel, fix stimulation delays
Evaluation: Power spectrum density on all the recordings to evaluate their quality.
Pre-processing: Notch filter, sinusoid removal, band-pass filter.
Evaluation: Power spectrum density on all the recordings to make sure the filters worked well
Cleanup: Delete the links to the original files (the filtered ones are copied in the database).
Detect artifacts: Detect heartbeats, Detect eye blinks, Remove simultaneous
Compute SSP: Heartbeats, Blinks (this selects the first component of each decomposition)
Compute ICA: If you have some types of artifacts you'd like to remove with ICA.
Screenshots: Check the MRI/sensors registration, PSD before and after corrections, SSP.
Export the report to HTML: One report per subject, or one report for all the subjects.
Manual inspection: For each run.
Check the reports: Information messages (number of events, errors and warnings) and screen captures (registration problems, especially obvious noisy channels, incorrect SSP topographies)
- Mark the bad channels
Fix the SSP: For the suspicious runs, open the file viewer, adjust the list of blink and cardiac events, remove and recompute the SSP decompositions, manually select the components.
Detect other artifacts: Run the process on all the runs of all the subjects at once
Mark bad segments: Review the detected artifacts, keep only the ones you want to remove, and then mark the event type as BAD. Review quickly the rest of the file and check that there are no other important artifacts.
Additional SSP: If you find one type of artifact that repeats (typically saccades and SQUID jumps), you can create additional SSP projectors, either with the "SSP: Generic" or directly from the topographies (right-click on the topography figure > Create SSP).
Script 2: Epoching, averaging, sources, time-frequency, etc.
- Importing
- Averaging
- Noise covariance:
- Head model
- Sources
- Time-frequency
- Statistics
Screenshots: Averages (time series, topographies, sources at one time point).
- Manual inspection:
- Check the reports: Check the number of imported epochs in each condition, check the averaged
- Regions of interest: If not using predefined regions from an atlas.
- Script 3: ROI-based analysis, additional statistics.
Script generation
http://neuroimage.usc.edu/brainstorm/Tutorials/PipelineEditor
Script edition
- Add loops, load files, ...
Loops: http://neuroimage.usc.edu/forums/showthread.php?2429-Problem-using-tags
File manipulation
- Modify a structure manually: Export to Matlab/Import from Matlab
- File manipulation: file_short, file_fullpath, in_bst_*...
- Documentation of all file structures: point at the appropriate tutorials
- Select files from the database (with bst_get and processes)
Export as script
Use the menu "Generate .m script" to create a Matlab script that would have the exact same result as running this analysis pipeline from the Brainstorm interface.
This script is also available in the Brainstorm distribution: brainstorm3/toolbox/script/tutorial_raw.m
Report viewer
Click on Run to start the script.
As this process is taking screen captures, do not use your computer for something else at the same time: if another window covers the Brainstorm figures, it will not capture the right images.
At the end, the report viewer is opened to show the status of all the processes, the information messages, the list of input and output files, and the screen captures. The report is saved in your home folder ($home/.brainstorm/reports). If you close this window, you can get it back with the menu File > Report viewer.