Releases: CoBrALab/RABIES
0.5.5
Main updates:
- It is now possible to compute any connectivity analysis (including seed based or dual regression) in nativespace
- with --bold_only, it is now possible to also generate preprocessed timeseries in nativespace, which are resampled to the original EPI space (no distortion correction is applied)
- The number of threads allocated to ITK commands is now properly handled by the
--num_ITK_threadsparameter. In previous version, ITK commands such as antsRegistration or antsMotionCorr would use all threads available. - The calculation of the frequency spectrum in data diagnosis now takes into account censored frames, by simulating missing data points using the Lombscargle method.
Changes to the command line interface:
--bold_nativespacecan be selected to re-define the nativespace as the original EPI space as opposed to the anatomical space obtained by registration of the EPI to the anat.--resampling_spaceparameter now handles whether preprocessed timeseries are generated in nativespace, commonspace or both. By default, timeseries are only generated in commonspace now as opposed to both.--resample_to_commonspaceparameter at the analysis stage now enables resampling the connectivity maps to commonspace if those were computed on nativespace timeseries--resample_to_commonspaceparameter at the confound correction stage enables resampling cleaned timeseries to commonspace after cleaning, if the--nativespace_analysisparameter was selected. This is useful if one wants to conduct confound correction in nativespace, and then compute group-ICA (which requires commonspace input)--brainmap_percent_thresholdcontrols the percent of voxels included when generating thresholded brain maps at the analysis pipeline stage. This determines the computation of the Dice overlap measure of network specificity.--plot_seed_frequenciesallows to plot the frequency spectrum from specific seed timecourses at the analysis pipeline stage when using--data_diagnosis--num_ITK_threadscontrols the number of ITK threads allocated per node that runs ITK commands.- It is now possible to disable MGIP when running MELODIC with
--group_ica - replaced
--detrending_orderwith flexible--detrendingparameter supporting custom polynomial orders and time intervals. Users can now specify any detrending order and time interval over which the trend is calculated.
0.5.4
Bugs fixed:
- all image headers are now inspected to ensure that image dimension is consistent, since this can lead to inconsistent resampling dimensions after preprocessing. If there is inconsistent image dimensions, the user must specify the resampling dimensionality with --commonspace_resampling or --nativespace_resampling
Changes to CLI parameters:
- every parameter for computing a unbiased template now includes the 'stages' sub-parameter, where one can specify the series of registration stages (i.e. rigid, affine and/or non-linear) carried for generating the template
- every registration parameter now includes winsorize_lower_bound and winsorize_upper_bound parameters that regulate the % intensity upper/lower bounds that is ignored by ANTs registration
- --log_transform is a novel option for apply a log transformation to image intensities prior to all registration operations, which can support registration for images with unusual intensity distributions
- --WM/CSF/vascular_mask and --labels are now OPTIONAL inputs - if a template file is inputted differing from the DSURQE default, then these parameters will be set to be empty unless they are provided with an explicit input. If these inputs are missing, the main pipeline will run, but downstream operations that require those inputs won't be run.
Other:
- the global signal is not plotted in the template diagnosis and saved as an output temporal feature
- Nipype/numpy/scipy packages were upgrated to speed up nipype workflow build
***Ongoing bugs with 0.5.4
- The default DSURQE template file cannot be read properly, and thus --anat_template must be provided manually.
- The 'run' iterables are not managed properly, meaning that if there are at least 2 different runs for one session, the workflow will crash. This will not happen if using the --bold_only parameter.
0.5.3
0.5.2
- The antsApplyTransform resampling interpolator, used for resampling of functional data after preprocessing, can now be customized with the
--interpolationparameter. By default, RABIES uses Linear instead of BSpline, since empirical results suggest that functional connectivity is decreased using BSpline. - It is no longer needed to specify each sub-parameter for a CLI flag, e.g.
--bold_inho_cor method=Affine,otsu_thresh=2,multiotsu=falsecan now be--bold_inho_cor method=Affineif one only wish to change the registration method relative to the default parameters. --group_avg_priorenables using the group average connectivity as a reference 'prior' for conducting--data_diagnosis- When defining the commonspace resampling resolution based on input images, an error will now be thrown if inconsistent image resolutions are detected, in which case it will be required to manually input a resampling resolution with
--commonspace_resamplingto bypass the error
0.5.1
Documentation:
- new data quality assessment documentation page, which documents how the reports generated using --data_diagnosis at the analysis stage can inform quality control at the analysis stage
- improved guidelines for RABIES developers
New parameters:
- --includion_ids/--exclusion_ids: these new parameters allow to specify which list of scan should be included/excluded, at any stage of the pipeline
- --bids_filter: allow to specify which BIDS filters to use to select the functional and anatomical files of interest
- --oblique2card: new option to modify the affine in oblique images so these image don't raise an error at later stages
- --inherit_unbiased_template: this novel option allows providing the path to preprocessing outputs from a previous RABIES run, and use the already-generated unbiased template and register images directly onto it instead of creating a new one
Docker container and testing:
- important re-writing of the Dockerfile. The container is much smaller, using only minimal requirements from ANTs, AFNI and FSL, and constructing conda environment based on exact dependencies
- container built and maintained on Github https://github.com/CoBrALab/RABIES/pkgs/container/rabies instead of docker hub
- testing with error_check_rabies.py is more complete (i.e. now tests almost all parameters across pipeline stages), and can take in custom commands to test. Complete testing is also conducted during container build
- we've attached to this release a pre-built singularity image for version 0.5.1 (the file is 1.8Gb). This image can be downloaded and used directly instead of building the container from scratch using singularity.
0.3.3
0.2.1
- Introduced new QC visual outputs for the denoising steps as well as some temporal diagnosis (tSTD,tSNR)
- all data outputs from the analysis are in .csv or .nii.gz formats
- upgraded generic registration scripts to latest version as implemented in https://github.com/CoBrALab/minc-toolkit-extras/blob/master/antsRegistration_affine_SyN.sh
- introduction of a new bias field correction strategy which relies on iterative otsu masking for more robust correction of EPIs
0.2.0
RABIES image processing workflow was extended to include confound regression and some basic functional connectivity analysis within a unified workflow.
The novel features, as well as their usage and outputs, are all described in the README.
0.1.2
Improved the memory specifications for running in parallel through SGE and MultiProc.
Changed the resampling the options to user-defined resampling dimensions in native and/or common spaces.
Can now specify the data type of output files to control for file size.
Fixed an issue where the STC option couldn't be turned off.
The detection of dummy scans to generate a reference EPI volume is now optional, and is turned off by default.
The boolean parser options are now controlled through action=store_true
0.1.1
Fixed bugs from the previous version on commonspace registration. Now provides complete installation of nifti format for the DSURQE template (without mnc2nii conversion) and provides a vascular mask as well for confound regression. The docker container is available on Docker hub and can be downloaded as a singularity image.