From fa54521261e902285f5eb7a10454e34e7cf49b90 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 3 Oct 2023 15:26:57 -0400 Subject: [PATCH 01/15] introducing novel guidelines for developers --- docs/contributing.md | 154 +++++++++++++++++++++++++++++++++++++++++++ docs/index.md | 2 +- 2 files changed, 155 insertions(+), 1 deletion(-) create mode 100644 docs/contributing.md diff --git a/docs/contributing.md b/docs/contributing.md new file mode 100644 index 0000000..93765cd --- /dev/null +++ b/docs/contributing.md @@ -0,0 +1,154 @@ +# Contributing to RABIES + +RABIES aims to provide an accessible tool responding to growing needs across the preclinical fMRI community. This effort should be community-driven, and community involvement will be paramount in achieving this goal in several respects: +- Adapting and maintaining **accessibility** for users across the broader community +- **Reproducibility and transparency**, as well as scientific scrutiny and rigor +- Defining and incorporating **best practices** across the different aspects of image processing and analysis, as well as quality control +- Leveraging appropriate expertise for the **integration of new tools** + +Suggestions for improvements can be shared using the Github [issues system](https://github.com/CoBrALab/RABIES/issues) and [discussion board](https://github.com/CoBrALab/RABIES/discussions). Contributions from developers are welcomed and encouraged. This page provides preliminary guidelines for getting started as a RABIES developer, and covers: setting a developer environment, submitting a pull request, testing and debugging, and basic instructions for adding a new module to the pipeline. We recommend discussing your proposed updates on the Github discussion board or issues prior to creating a pull request. Thank you for your support! + +## Dev environment + +For development, it is recommend to install RABIES locally, as this will make the testing and debugging process smoother. This requires installing the dependencies listed in dependencies.txt, and then installing RABIES in an appropriate python environment (e.g. using anaconda) from the Github repository. This can be done by cloning the repository, and then running ```python setup.py install```. + +### ...using a container + +It is possible to run operations using a container to avoid installing dependencies manually (however, it won't be possible to use an interface for debugging (e.g. Spyder)). This can be with `docker exec`. First, an instance of the container must be opened, which can be done by including `-d --entrypoint sh --name mycontainer` when calling `docker run`. The paths to access from the container must be set with `-v`. Here's an example: +```sh +docker run -it -v $PWD:/work_dir -v /path_to_local_RABIES_package:/RABIES:ro \ +--rm --entrypoint sh -d --name mycontainer rabies:local_testing +``` +You can then execute commands from inside the container as follows (`mycontainer` corresponds to the name set above): +```sh +docker exec mycontainer micromamba run $COMMAND +``` +To test for error, `$COMMAND` can correspond to `error_check_rabies.py --complete`. + +**Upgrading the RABIES package**: to test your updates, you must re-install RABIES inside the container. Below is a method to do so: +```sh +mkdir -p /tmp/RABIES +# copy all files from your upgraded package inside the container (/RABIES must be related to your local package with -v) +rsync -avz /RABIES/* /tmp/RABIES/. +# re-install package +cd /tmp/RABIES +python setup.py install +``` +These can be compiled into a .sh script to execute in place of `$COMMAND` above. + +## Instructions to create a pull request + +1. On github, fork the RABIES repository to have your own copy. +2. Clone your repository to carry out local modifications and testing. Use the `--recursive` option to download the submodules together with the main RABIES package. +3. Create and checkout into a new branch with `git checkout -b my_new_branch` (provide a sensible name for the branch). You are ready to make modifications to the code. +4. Testing and debugging: install your updated version of the package with ```python setup.py install```, using a proper dev environment (see above). Your can test the workflow with specific parameters by editing the ```debug_workflow.py``` script, and executing in debug mode with Spyder (see below). Before commiting changes, make sure that running ```error_check_rabies.py --complete``` completes with no error. +5. Commit and push your modifications to Github, and create a pull request from your forked repo to the original. + +## Interactive debugging with Spyder and debug_workflow.py + +Here are some recommendations for debugging using Spyder: +1. open the debug_workflow.py file in Spyder +2. find the scrips with your local installation to add breakpoints for debugging. Using `import rabies; os.path.abspath(rabies.__file__)` will provide the path to the __init__.py file of your installed package, and from there you can find file of interest and add a breakpoint where desired. +3. execute debug_workflow.py in debug mode, and run until it finds the breakpoint, and debug from there. + +## Creation of a new module and integration within a Nipype workflow + +RABIES' workflow is structured using Nipype (for more info on Nipype, see online [documentation](https://nipype.readthedocs.io/en/latest/) and [tutorial](https://miykael.github.io/nipype_tutorial/)). Preferably, a new function should be created as a Nipype interface, which has the following syntax: + +```python +from nipype.interfaces.base import ( + traits, TraitedSpec, BaseInterfaceInputSpec, + File, BaseInterface +) +class NewInterfaceInputSpec(BaseInterfaceInputSpec): + # you must select an appropriate input type with traits.type (can be Dict, File, Int, ...) + input_str = traits.Str(exists=True, mandatory=True, + desc="An input string.") + +class NewInterfaceOutputSpec(TraitedSpec): + out_file = File( + exists=True, desc="An output file.") + + +class NewInterface(BaseInterface): + """ + Describe your module. + """ + + input_spec = NewInterfaceInputSpec + output_spec = NewInterfaceOutputSpec + + def _run_interface(self, runtime): + input_str = self.inputs.input_str + + ''' + YOUR CODE + ''' + + setattr(self, 'out_file', out_file) + + return runtime + + def _list_outputs(self): + return {'out_file': getattr(self, 'out_file')} + + +``` + +You can then create a Nipype node for your interface: +```python +from .other_script import NewInterface # import your interface if from a different script +from nipype.pipeline import engine as pe + +new_interface_node = pe.Node(NewInterface(), + name='new_interface') +``` + +Instead of an interface, it is also possible to create a Nipype node from any python function: +```python +from nipype.pipeline import engine as pe +from nipype.interfaces.utility import Function + +new_function_node = pe.Node(Function(input_names=['input_1', 'input_2', ...], + output_names=['output_1', 'output_2', ...], + function=NewFunction), + name='new_function') +``` + +After creating a node which can carry the desired operation, it must be integrated within a workflow by linking up the inputs and outputs with other nodes. Below is an example of a simple workflow which conducts slice-timing correction: + +```python +from nipype.pipeline import engine as pe +from nipype.interfaces.utility import Function +from nipype.interfaces import utility as niu + +# this function creates and return a Nipype workflow which conducts slice timing correction +def init_bold_stc_wf(name='bold_stc_wf'): + + workflow = pe.Workflow(name=name) # creating a new Nipype workflow + # creating an intermediate node for storing inputs to the workflow + inputnode = pe.Node(niu.IdentityInterface( + fields=['bold_file']), name='inputnode') + # creating an intermediate node for storing outputs to the workflow + outputnode = pe.Node(niu.IdentityInterface( + fields=['stc_file']), name='outputnode') + + # preparing the node conducting STC + slice_timing_correction_node = pe.Node(Function(input_names=['in_file', 'tr', 'tpattern', 'stc_axis', + 'interp_method', 'rabies_data_type'], + output_names=[ + 'out_file'], + function=slice_timing_correction), + name='slice_timing_correction', mem_gb=1.5*opts.scale_min_memory) + + # linking up the inputnode to provide inputs to the STC node, and outputs from STC to the outputnode of the workflow + workflow.connect([ + (inputnode, slice_timing_correction_node, [('bold_file', 'in_file')]), + (slice_timing_correction_node, + outputnode, [('out_file', 'stc_file')]), + ]) + return workflow + +``` + +This example demonstrates the basic syntax of a Nipype workflow. Most likely, a new interface will be integrated as part of a pre-existing workflow (instead of creating a new one), in which case the right nodes must be linked up with the new interface. diff --git a/docs/index.md b/docs/index.md index 848cf6a..b01895a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -15,5 +15,5 @@ analysis.md analysis_QC.md outputs.md metrics.md -additional.md +contributing.md bibliography.md From da15ce95b5a1f92edcf4747ff342dcfb1cf61c9e Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 3 Oct 2023 15:28:32 -0400 Subject: [PATCH 02/15] Link contributions in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9c9d0e2..f899c30 100644 --- a/README.md +++ b/README.md @@ -64,4 +64,4 @@ If you need support in using the software or experience issues that are not docu ## Contributing to RABIES -RABIES is under continuous development to keep up with evolving demand and ongoing research. We welcome suggestions for improvements using the [Github issue system](https://github.com/CoBrALab/RABIES/issues). If you're interested in contributing code, you can reach out on the [Github discussion](https://github.com/CoBrALab/RABIES/discussions) and we welcome contributions as pull requests. +**Read our dedicated [documentation](https://rabies.readthedocs.io/en/latest/contributing.html)** From 1ad6371f76c4290e046e2a60db496645f4612bdc Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 3 Oct 2023 15:29:22 -0400 Subject: [PATCH 03/15] upgrade package version for documentation. --- docs/conf.py | 2 +- docs/requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 5246a4e..8a7abbf 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -22,7 +22,7 @@ author = 'CoBrALab' # The full version, including alpha/beta/rc tags -release = '0.4.6' +release = '0.5.0' # -- General configuration --------------------------------------------------- diff --git a/docs/requirements.txt b/docs/requirements.txt index 146ad56..f3687c8 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -5,4 +5,4 @@ sphinx-rtd-theme sphinxcontrib-bibtex sphinxcontrib-programoutput==0.17 jinja2==3.1.1 -rabies==0.4.7 \ No newline at end of file +rabies==0.5.0 \ No newline at end of file From 41f4cd14320b789b255ba341eb5d8fe6e0aba830 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 3 Oct 2023 15:30:18 -0400 Subject: [PATCH 04/15] Unused packages. --- rabies/preprocess_pkg/utils.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/rabies/preprocess_pkg/utils.py b/rabies/preprocess_pkg/utils.py index f4094a7..1f85f30 100644 --- a/rabies/preprocess_pkg/utils.py +++ b/rabies/preprocess_pkg/utils.py @@ -1,7 +1,3 @@ -import os -import pathlib -import SimpleITK as sitk -import numpy as np from nipype.interfaces.base import ( traits, TraitedSpec, BaseInterfaceInputSpec, File, BaseInterface From 634386c9472e16d7ab1c63a8b3e9e686e9231e86 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 3 Oct 2023 15:31:21 -0400 Subject: [PATCH 05/15] Recommendation for addressing permission issues with docker --- docs/running_the_software.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/running_the_software.md b/docs/running_the_software.md index f4ff077..59b899d 100644 --- a/docs/running_the_software.md +++ b/docs/running_the_software.md @@ -189,7 +189,7 @@ docker run -it --rm \ -v $PWD/preprocess_outputs:/preprocess_outputs/ \ gabdesgreg/rabies:tagname -p MultiProc preprocess /input_BIDS/ /preprocess_outputs/ --apply_STC --TR 1.2 --commonspace_reg masking=true,brain_extraction=false,template_registration=SyN,fast_commonspace=false ``` -The syntax in Docker is very similar to Singularity, except that `-B` is replaced by `-v`, and further parameters may be needed (e.g. `-it`, `--rm`). Note that 'tagname' should be replaced by the proper RABIES version you are using (e.g. 0.4.8). +The syntax in Docker is very similar to Singularity, except that `-B` is replaced by `-v`, and further parameters may be needed (e.g. `-it`, `--rm`). When using Docker, there may be issues with writing permissions, and you may need to explicitely allow permissions on the output folder (e.g. `chmod 777 preprocess_outputs/`). Note that 'tagname' should be replaced by the proper RABIES version you are using (e.g. 0.4.8). ## Additional Resources From 58196b4daee17e97204e059a016001c598608bb2 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Wed, 4 Oct 2023 15:08:49 -0400 Subject: [PATCH 06/15] Boilerplate is more succinct and updated to latest functionalities. --- rabies/boilerplate.py | 61 ++++++++++++++++++++++--------------------- 1 file changed, 31 insertions(+), 30 deletions(-) diff --git a/rabies/boilerplate.py b/rabies/boilerplate.py index 0e6b2be..c18e1f5 100644 --- a/rabies/boilerplate.py +++ b/rabies/boilerplate.py @@ -13,7 +13,7 @@ def preprocess_boilerplate(opts): i=1 # define references - rabies_ref='Desrosiers-Gregoire, G., Devenyi, G. A., Grandjean, J., & Chakravarty, M. M. (2022). Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI. bioRxiv.' + rabies_ref='Desrosiers-Gregoire, G., Devenyi, G. A., Grandjean, J., & Chakravarty, M. M. (2023). Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI. bioRxiv.' afni='Cox, R. W. (1996). AFNI: software for analysis and visualization of functional \ magnetic resonance neuroimages. Computers and Biomedical research, 29(3), 162-173.' avants_2011='Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033–2044.' @@ -66,26 +66,22 @@ def preprocess_boilerplate(opts): # common space alignment if not opts.bold_only: - reg_image='structural images, which were acquired along the EPI scans,' + reg_image='structural images' else: reg_image='volumetric EPI scans' - methods+=f"To conduct common space alignment, {reg_image} are initially corrected for \ + methods+=f"To derive an alignment to common space, {reg_image} are initially corrected for \ inhomogeneities and " if not opts.commonspace_reg['fast_commonspace']: references[avants_2011]=i i+=1 - methods+=f"then registered together to allow the alignment of different MRI acquisitions. This registration is conducted by generating \ -an unbiased data-driven template through the iterative nonlinear registration of each image to the dataset consensus average, where the average \ -gets updated at each iteration to provide an increasingly representative dataset template \ -(https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction) [{references[avants_2011]}]. The finalized template after the last \ -iteration provides a representative alignment of each MRI session to a template that shares the acquisition properties of the dataset \ -(e.g. brain shape, FOV, anatomical contrast, ...), which makes it a stable registration target for cross-subject alignment. After aligning the MRI \ -sessions, this newly-generated unbiased template is then itself registered, using a {define_registration(opts.commonspace_reg['template_registration'])} registration, to an external reference atlas to provide both an anatomical segmentation \ -and a common space comparable across studies defined from the provided reference atlas." + methods+=f"then registered together to allow the alignment of different acquisition sessions. This is done by generating \ +an unbiased data-driven template through the iterative nonlinear registration of each image to a dataset average, where the average \ +is improved at each iteration \ +(https://github.com/CoBrALab/optimized_antsMultivariateTemplateConstruction) [{references[avants_2011]}]. After aligning the acquisition \ +sessions, this newly-generated unbiased template is then itself registered, using a {define_registration(opts.commonspace_reg['template_registration'])} registration, to an external reference atlas." else: # Fast commonspace alignment - methods+=f"then registered individually to an external reference atlas, with a {define_registration(opts.commonspace_reg['template_registration'])} registration, to \ -provide both an anatomical segmentation and a common space comparable across studies defined from the provided reference atlas. " + methods+=f"then aligned individually to an external reference atlas using a {define_registration(opts.commonspace_reg['template_registration'])} registration. " # STC if opts.apply_STC: @@ -118,7 +114,7 @@ def preprocess_boilerplate(opts): using a {define_registration(opts.bold2anat_coreg['registration'])} registration to the anatomical scan from the same MRI session, which allows to calculate the required \ geometrical transforms for recovering brain anatomy [{references[sdc_nonlinear]}]. " else: - sdc_str="[NO SUSCEPTIBILITY DISTORTION CORRECTION CONDUCTED]" + sdc_str="[NO SUSCEPTIBILITY DISTORTION CORRECTION CONDUCTED] " references[fmriprep]=i i+=1 @@ -142,7 +138,7 @@ def preprocess_boilerplate(opts): methods+=sdc_str methods+=resampling_str - ref_string='' + ref_string='\n' for key in references.keys(): ref_string+=f"[{references[key]}] {key} \n" return methods,ref_string @@ -154,7 +150,7 @@ def confound_correction_boilerplate(opts): i=1 # define references - rabies_ref='Desrosiers-Gregoire, G., Devenyi, G. A., Grandjean, J., & Chakravarty, M. M. (2022). Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI. bioRxiv.' + rabies_ref='Desrosiers-Gregoire, G., Devenyi, G. A., Grandjean, J., & Chakravarty, M. M. (2023). Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI. bioRxiv.' aroma='Pruim, R. H., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015). ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage, 112, 267-277.' Lindquist='Lindquist, M. A., Geuter, S., Wager, T. D., & Caffo, B. S. (2019). Modular preprocessing pipelines can reintroduce artifacts into fMRI data. Human Brain Mapping, 40(8), 2358–2376.' mathias='Mathias, A., Grond, F., Guardans, R., Seese, D., Canela, M., & Diebner, H. H. (2004). Algorithms for spectral analysis of irregularly sampled time series. Journal of Statistical Software, 11(1), 1–27.' @@ -191,6 +187,10 @@ def confound_correction_boilerplate(opts): i+=1 methods+=f"Using the DVARS measure[{references[power2012]}] of temporal fluctuations in global signal, timepoints presenting outlier DVARS values, characteristic of confounds, were removed. \ This was conducted by iteratively removing frames which present outlier DVARS values above or below 2.5 standard deviations until no more outliers are detected. " + if opts.match_number_timepoints: + methods+=f"Following censoring, only a total of {opts.frame_censoring['minimum_timepoint']} frames was retained in each scan through random selection among the remaining frames. This was done \ +to enforce equal degrees of freedom across scans despite an inconsistent number of frames removed through censoring. " + methods+="Next, " else: methods+="First, " @@ -234,13 +234,10 @@ def confound_correction_boilerplate(opts): i+=1 references[mathias]=i i+=1 - methods+=f"Next, frequency filtering requires particular considerations when frame censoring was \ -previously applied, which results in missing timepoints. Conventional filters cannot account for missing data, \ -and to address this issue, Power and colleagues introduced a method for simulating missing timepoints [{references[power2014]}]. \ -This method relies on an adaptation of the Lomb-Scargle periodogram, which allows estimating the frequency composition of \ -timeseries with missing data points, and from that estimation, missing timepoints can be simulated while preserving the \ -frequency profile [{references[mathias]}]. Missing data is thereby simulated with preserved frequency properties for both \ -BOLD timeseries and nuisance regressors prior to frequency filtering. " + methods+=f"To apply frequency filters despite missing data points (i.e. following the frame censoring step), \ +missing data was simulated based on a method introduced by Power and colleagues, where data is simulated \ +using the Lomb-Scargle periodogram to preserve the frequency profile of the timeseries before applying \ +the filters[{references[power2014]},{references[mathias]}]. " methods+=f"Following the simulation, " else: @@ -250,8 +247,7 @@ def confound_correction_boilerplate(opts): references[power2014]=i i+=1 methods+=f"{filter} filtering({freq}Hz) was applied using a 3rd-order Butterworth filter, and {opts.edge_cutoff} seconds \ -is removed at each edge of the timeseries to account for edge artefacts following filtering [{references[power2014]}]. The removal \ -of 30 seconds and the use of a 3rd order filter was selected based on the visualization of edge artefacts with simulated data. " +is removed at each edge of the timeseries to account for edge artefacts following filtering [{references[power2014]}]. " if len(opts.conf_list) > 0: references[Lindquist]=i @@ -310,18 +306,23 @@ def confound_correction_boilerplate(opts): if len(str_list)>1: conf_list_str+=f' and {str_list[-1]}' - methods+=f"Selected nuisance regressors were then used for confound regression. More specifically, using ordinary least square regression, \ {conf_list_str} were modelled at each voxel and regressed from the data. " # variance standardization - if opts.image_scaling=="background_noise": - methods+=f"To normalize variance, each image was seperately scaled according to its background noise. " + if opts.image_scaling=="grand_mean_scaling": + methods+=f"Timeseries were converted to percent BOLD fluctuations by dividing by the grand mean signal across voxels, \ +and multiplying by 100. " + elif opts.image_scaling=="voxelwise_mean": + methods+=f"Each voxel was individually converted to percent BOLD fluctuations by dividing by the temporal mean and multiplying by 100. " elif opts.image_scaling=="global_variance": methods+=f"To normalize variance, each image was seperately scaled according to its total variance. " elif opts.image_scaling=="voxelwise_standardization": methods+=f"To normalize variance, each voxel was individually standardized to unit variance. " - + if opts.scale_variance_voxelwise: + methods+=f"Additionally, each voxel was set to equal temporal variance while preserving the total variance of the entire image. \ +This allows to obtain a spatially-homogeneous variance distribution, and mitigate influence from confounds [{references[rabies_ref]}]. " + # Spatial smoothing if opts.smoothing_filter is not None: if not nilearn in references.keys(): @@ -330,7 +331,7 @@ def confound_correction_boilerplate(opts): methods+=f"Finally, a spatial Gaussian smoothing filter(nilearn.image.smooth_img)[{references[nilearn]}] was applied at {opts.smoothing_filter}mm full-width at half maximum (FWHM). " methods+='\n' - ref_string='' + ref_string='\n' for key in references.keys(): ref_string+=f"[{references[key]}] {key} \n" return methods,ref_string From b09f63a35f2d5104c58f7f51543e945fc9e254b5 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Mon, 9 Oct 2023 15:31:12 -0400 Subject: [PATCH 07/15] Prepared main documentation page for data quality assessment. --- docs/_static/refs.bib | 128 +++++++++++++++++++++++++++++++++---- docs/analysis_QC.md | 45 ++++++++++++- docs/outputs.md | 17 +++-- docs/pics/QC_framework.png | Bin 0 -> 1985594 bytes rabies/parser.py | 21 +++--- 5 files changed, 183 insertions(+), 28 deletions(-) create mode 100644 docs/pics/QC_framework.png diff --git a/docs/_static/refs.bib b/docs/_static/refs.bib index 537e580..ca97d70 100644 --- a/docs/_static/refs.bib +++ b/docs/_static/refs.bib @@ -1,6 +1,79 @@ % Generated by Paperpile. Check out https://paperpile.com for more information. % BibTeX export options can be customized via Settings -> BibTeX. +@UNPUBLISHED{Desrosiers-Gregoire2023-jm, + title = "Rodent Automated Bold Improvement of {EPI} Sequences ({RABIES)}: + A standardized image processing and data quality platform for + rodent {fMRI}", + author = "Desrosiers-Gregoire, Gabriel and Devenyi, Gabriel A and + Grandjean, Joanes and Mallar Chakravarty, M", + abstract = "Functional magnetic resonance imaging (fMRI) in rodents holds + great potential for advancing our understanding of brain + networks. Unlike the human fMRI community, there remains no + standardized resource in rodents for image processing, analysis + and quality control, posing significant reproducibility + limitations. Our software platform, Rodent Automated Bold + Improvement of EPI Sequences (RABIES), is a novel pipeline + designed to address these limitations for preprocessing, quality + control, and confound correction, along with best practices for + reproducibility and transparency. We demonstrate the robustness + of the preprocessing workflow by validating performance across + multiple acquisition sites and both mouse and rat data. Building + upon a thorough investigation into data quality metrics across + acquisition sites, we introduce guidelines for the quality + control of network analysis and offer recommendations for + addressing issues. Taken together, the RABIES software will allow + the emerging community to adopt reproducible practices and foster + progress in translational neuroscience. \#\#\# Competing Interest + Statement The authors have declared no competing interest.", + journal = "bioRxiv", + pages = "2022.08.20.504597", + month = sep, + year = 2023, + keywords = "RABIES documentation", + language = "en" +} + +@ARTICLE{Avants2008-rx, + title = "Symmetric diffeomorphic image registration with + cross-correlation: evaluating automated labeling of elderly and + neurodegenerative brain", + author = "Avants, B B and Epstein, C L and Grossman, M and Gee, J C", + abstract = "One of the most challenging problems in modern neuroimaging is + detailed characterization of neurodegeneration. Quantifying + spatial and longitudinal atrophy patterns is an important + component of this process. These spatiotemporal signals will aid + in discriminating between related diseases, such as + frontotemporal dementia (FTD) and Alzheimer's disease (AD), which + manifest themselves in the same at-risk population. Here, we + develop a novel symmetric image normalization method (SyN) for + maximizing the cross-correlation within the space of + diffeomorphic maps and provide the Euler-Lagrange equations + necessary for this optimization. We then turn to a careful + evaluation of our method. Our evaluation uses gold standard, + human cortical segmentation to contrast SyN's performance with a + related elastic method and with the standard ITK implementation + of Thirion's Demons algorithm. The new method compares favorably + with both approaches, in particular when the distance between the + template brain and the target brain is large. We then report the + correlation of volumes gained by algorithmic cortical labelings + of FTD and control subjects with those gained by the manual + rater. This comparison shows that, of the three methods tested, + SyN's volume measurements are the most strongly correlated with + volume measurements gained by expert labeling. This study + indicates that SyN, with cross-correlation, is a reliable method + for normalizing and making anatomical measurements in volumetric + MRI of patients and at-risk elderly individuals.", + journal = "Med. Image Anal.", + volume = 12, + number = 1, + pages = "26--41", + month = feb, + year = 2008, + keywords = "RABIES documentation", + language = "en" +} + @ARTICLE{Avants2011-av, title = "A reproducible evaluation of {ANTs} similarity metric performance in brain image registration", @@ -633,7 +706,7 @@ @ARTICLE{Power2017-wn pages = "609--625", month = feb, year = 2017, - keywords = "Sources of BOLD signal;RABIES documentation", + keywords = "RABIES documentation", language = "en" } @@ -700,6 +773,49 @@ @ARTICLE{Biswal2010-pi language = "en" } +@ARTICLE{Zerbi2015-nl, + title = "Mapping the mouse brain with {rs-fMRI}: An optimized pipeline + for functional network identification", + author = "Zerbi, Valerio and Grandjean, Joanes and Rudin, Markus and + Wenderoth, Nicole", + abstract = "The use of resting state fMRI (rs-fMRI) in translational + research is a powerful tool to assess brain connectivity and + investigate neuropathology in mouse models. However, despite + encouraging initial results, the characterization of consistent + and robust resting state networks in mice remains a + methodological challenge. One key reason is that the quality of + the measured MR signal is degraded by the presence of structural + noise from non-neural sources. Notably, in the current pipeline + of the Human Connectome Project, a novel approach has been + introduced to clean rs-fMRI data, which involves automatic + artifact component classification and data cleaning (FIX). FIX + does not require any external recordings of physiology or the + segmentation of CSF and white matter. In this study, we + evaluated the performance of FIX for analyzing mouse rs-fMRI + data. Our results showed that FIX can be easily applied to mouse + datasets and detects true signals with 100\% accuracy and true + noise components with very high accuracy (>98\%), thus reducing + both within- and between-subject variability of rs-fMRI + connectivity measurements. Using this improved pre-processing + pipeline, maps of 23 resting state circuits in mice were + identified including two networks that displayed default mode + network-like topography. Hierarchical clustering grouped these + neural networks into meaningful larger functional circuits. + These mouse resting state networks, which are publicly + available, might serve as a reference for future work using + mouse models of neurological disorders.", + journal = "Neuroimage", + publisher = "Elsevier", + volume = 123, + pages = "11--21", + month = dec, + year = 2015, + keywords = "Artifact detection; Data cleaning; Hierarchical clustering; + Independent component analysis; Mouse; Resting state fMRI;Rodent + fMRI;RABIES documentation", + language = "en" +} + @ARTICLE{Power2012-ji, title = "Spurious but systematic correlations in functional connectivity {MRI} networks arise from subject motion", @@ -747,16 +863,6 @@ @MISC{Friston1996-sa keywords = "MRI methods/Image processing;RABIES documentation" } -@ARTICLE{Avants2009-mn, - title = "Advanced normalization tools ({ANTS})", - author = "Avants, Brian B and Tustison, Nick and Song, Gang", - journal = "Insight J.", - volume = 2, - pages = "1--35", - year = 2009, - keywords = "MRI methods/Image processing;RABIES documentation" -} - @ARTICLE{Esteban2019-rs, title = "{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}", author = "Esteban, Oscar and Markiewicz, Christopher J and Blair, Ross W diff --git a/docs/analysis_QC.md b/docs/analysis_QC.md index 80fee37..865ea24 100644 --- a/docs/analysis_QC.md +++ b/docs/analysis_QC.md @@ -2,7 +2,6 @@ (analysis_QC_target)= - ```{toctree} --- maxdepth: 3 @@ -11,3 +10,47 @@ nested_docs/scan_diagnosis.md nested_docs/group_stats.md nested_docs/distribution_plot.md nested_docs/optim_CR.md +``` + +Data quality can have serious impacts on analysis outcomes, leading to false findings. Rodent imaging can suffer from spurious effects on connectivity measures if potential confounds are not well accounted for, or acquisition factors, such as anesthesia levels, can influence network activity (refs to Jo Multisite + RABIES preprint). To support interpretability, troubleshooting and reproducible research, RABIES includes a set of reports for conducting data quality assessment in individual scans and conducting quality control prior to network analysis at the group level. The reports are designed most specifically to evaluate the detectability of canonical brain networks and the impact of potential confounds (motion, physiological instabilities, and more). + +This page describes how to generate the reports, our guidelines for conducting quality network of network analysis, and how to include those reports in a publication. + +## Generating the reports + +At the analysis stage of the pipeline, the `--data_diagnosis` option can be selected to generate the data quality reports. To generate the report, ICA components must also be provided with `--prior_maps` and a set of components corresponding to confounds must be selected using `--conf_prior_idx` (see further details below). Connectivity can be evaluated for both dual regression and seed-based connectivity: +* **For [dual regression](DR_target)**: dual regression is always conducted using the set components from `--prior_maps`, since certain features are derived from confound components defined in `--conf_prior_idx`. On the other hand, connectivity will be evaluated in the reports for each network included in `--bold_prior_idx`. +* **For [seed-based connectivity](SBC_target)**: reports will be generated for each seed provided to `--seed_list`. However, each seed needs to be supplemented with a reference network map (a 3D Nifti file for each seed, provided with `--seed_prior_list`) which should represent the expected connectivity for the canonical network corresponding to that seed. +
+The set of reports are generated in the `data_diagnosis_datasink/` (details [here](diagnosis_datasink_target)). The interpretation of each report is described within its dedicated documentation page, and include: + +* [Spatiotemporal diagnosis](diagnosis_target): this qualitative report generated for each scan regroups a set of temporal and spatial features allowing to characterize the specific origin of data quality issues. +* [Distribution plots](dist_plot_target): quantitative report displaying the distribution of scans along measures of: specificity of network connectivity, network amplitude (for dual regression), and confound measures. Visualizing the dataset distribution can help identify outliers. +* [Group statistics](group_stats_target): for a given network, this group-level report regroups brain maps for visualizing cross-scan variability in connectivity and the group-wise correlation between connectivity and confounds. + +### Classification of group ICA components + +Ideally, the ICA components should be derived directly from the dataset analyzed by using [group ICA](ICA_target), although a [pre-computed set](https://zenodo.org/record/5118030/files/melodic_IC.nii.gz) is available by default. Newly-generated components must be visually inspected to identify the set of components corresponding to confound sources (which is inputted with `--conf_prior_idx`). This can be done by visualizing the group_melodic.ica/melodic_IC.nii.gz file, or using the automatically-generated FSL report in group_melodic.ica/report. Similarly, components corresponding to networks of interest can be identified and inputted with `--bold_prior_idx`. + +Classifying components requires careful considerations, and we recommend a conservative inclusion (i.e. not every components need to be classified, only include components which have clear feature delineating a network or a confound). Consult {cite}`Zerbi2015-nl` or {cite}`Desrosiers-Gregoire2023-jm` for more information on classifying ICA components in rodents, or the [pre-computed set](https://zenodo.org/record/5118030/files/melodic_IC.nii.gz) can be consulted as reference (the defaults for `--bold_prior_idx` and `--conf_prior_idx` correspond to the classification of these components). + +## Guidelines for analysis quality control + +![](pics/QC_framework.png) + +Below are our recommendations for how the set of quality reports can be used identify and control for the impact of data quality issues on downstream group analysis. Although the reports may be used for a breadth of applications, these guidelines are formulated most specifically for a standard resting-state fMRI design aiming to compare network connectivity between subjects or groups. In particular, the following guidelines aim to identify features of spurious or absent connectivity, remove scans where these features are prominent to avoid false results (e.g. connectivity difference is driven by motion), and determine whether these issues may confound group statistics. + +1. Inspect the [spatiotemporal diagnosis](diagnosis_target) for each scan. Particular attention should be given to the 4 main quality markers defining [categories of scan quality](quality_marker_target), and whether features of spurious or absent connectivity are prominent. +2. If spurious or absent connectivity is prominent in a subset of scans, these scans should be detected and removed to mitigate false results. This is done by setting thresholds using `--scan_QC_thresholds` for scan-level measures of network specificity and confound correlation. These measures are documented in the [distribution plots](dist_plot_target), and the specific measures for each scan ID can be consulted in the CSV file accompanying the plot. Using this CSV file, sensible threshold values can be selected for delineating scans with spurious or absent connectivity. Additionally, for dual regression analysis, `--scan_QC_thresholds` can be used to automatically detect and remove scans which present outlier values in network amplitude, which can be an indicator of spurious connectivity {cite}`Nickerson2017-gq`. By applying `--scan_QC_thresholds`, these scans won't be included for generating the group statistical report (thus the reports must re-generated after defining `--scan_QC_thresholds`). +3. Finally, the [group statistical report](group_stats_target) can be consulted to identify the main driven of variability in connectivity across scans, and whether it relates primarily to network activity or to confounds. + +If significant issues are found from this evaluation, the design of the confound correction stage may be revisited to improve quality outcomes (see [dedicated documentation](optim_CR)). + +**Disclaimer**: Although these guidelines are meant to support identifying analysis pitfalls and improve research transparency, they are not meant to be prescriptive. The judgement of the experimenter is paramount in the adopting adequate practices (e.g. network detectability may not always be expected, if studying the impact of anesthesia or inspecting a visual network in blind subjects), and the conversation surrounding what should constitute proper standards for resting-state fMRI is evolving. + +### Reporting in a publication + +All figures from the report are generated in PNG (or SVG) format, and can be shared along a publication for data transparency. Ideally, a version of the spatiotemporal diagnosis can be shared for each scan used in deriving connectivity results, together with a group statistical report and its affiliated distribution plot for each groups/datasets if the analysis involves comparing connectivity differences across subjects and/or group. +
+The set of ICA components classified as networks and confounds should be reported appropriately (e.g. melodic_IC.nii.gz file can be shared with its associated component classification). If certain scan inclusion/exclusion criteria were selected based on the quality control guidelines described above, it is particularly important to describe the observations motivating these criteria and make the associated reports readily accessible for consultation (e.g. the set of spatiotemporal diagnosis files for scans displaying spurious/absent connectivity and motivated setting a particular QC threshold with `--scan_QC_thresholds`). If the design of confound correction was defined using these tools, this should also be appropriately reported. + diff --git a/docs/outputs.md b/docs/outputs.md index d0d9462..8b414dc 100644 --- a/docs/outputs.md +++ b/docs/outputs.md @@ -79,13 +79,18 @@ Outputs from analyses will be found in the `analysis_datasink/`, whereas outputs - `NPR_extra_timecourse_csv/`: timecourses associated to each components from NPR_extra_filename (diagnosis_datasink_target)= - `data_diagnosis_datasink/`: - - `figure_temporal_diagnosis/`: PNG file which displays scan-level temporal features from `--data_diagnosis` - - `figure_spatial_diagnosis/`: PNG file which displays scan-level spatial features from `--data_diagnosis` + - `figure_temporal_diagnosis/`: figure which displays scan-level temporal features from the [spatiotemporal diagnosis](diagnosis_target) + - `figure_spatial_diagnosis/`: figure which displays scan-level spatial features from the [spatiotemporal diagnosis](diagnosis_target) - `analysis_QC/`: group-level features of data quality from `--data_diagnosis` - - `DR{component #}_QC_maps.png`: The _QC_maps.png files are PNGs displaying statistical maps relevant to analysis quality control. The DR refers to dual regression analysis, and the {component #} is relating the file to one of the BOLD components specified in `--prior_bold_idx` - - `DR{component #}_QC_stats.csv`: a follow-up to _QC_maps.png which allows for the quantitative categorization of data quality outcomes in Desrosiers-Gregoire et al. (in prep.) - - `seed_FC{seed #}_QC_maps.png`: same statistical maps as with `DR{component #}_QC_maps.png`, but for seed-based connectivity analysis - - `seed_FC{seed #}_QC_stats.csv`: same measures as with `DR{component #}_QC_maps.png`, but for seed-based connectivity analysis + - `sample_distributions/`: contains the [distribution plots](dist_plot_target) + - `{analysis}_sample_distribution.png`: the distribution plot for a given network analysis + - `{analysis}_outlier_detection.csv`: a CSV which associates the measures displayed in the distribution plot with corresponding scan IDs + - `parametric_stats/`: [group statistical report](group_stats_target) for analysis quality control (using parametric measures) + - `DR{component #}_QC_maps.png`: The _QC_maps.png files are displaying statistical maps relevant to analysis quality control. The DR refers to dual regression analysis, and the {component #} is relating the file to one of the BOLD components specified in `--prior_bold_idx` + - `DR{component #}_QC_stats.csv`: a follow-up to _QC_maps.png which allows for the quantitative categorization of data quality outcomes in Desrosiers-Gregoire et al. (in prep.) + - `seed_FC{seed #}_QC_maps.png`: same statistical maps as with `DR{component #}_QC_maps.png`, but for seed-based connectivity analysis + - `seed_FC{seed #}_QC_stats.csv`: same measures as with `DR{component #}_QC_maps.png`, but for seed-based connectivity analysis + - `non_parametric_stats/`: same as `parametric_stats/`, but using non-parametric measures - `temporal_info_csv/`: CSV file containing the data plotted with `figure_temporal_diagnosis/` - `spatial_VE_nii/`: Nifti file with the confound regression percentage variance explained (R^2) at each voxel - `CR_prediction_std_nii/`: Nifti file with the confound regression variance explained at each voxel diff --git a/docs/pics/QC_framework.png b/docs/pics/QC_framework.png new file mode 100644 index 0000000000000000000000000000000000000000..1f0dde51f77092192042c8e7740661d1956c2a1e GIT binary patch literal 1985594 zcmZ^L2RxVU`}Q|!SnZ(@i4-B3$!N+dWkyD_cd|l8ODZebv#hd5kxeDCBU?tvCOez= 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a/docs/nested_docs/distribution_plot.md +++ b/docs/nested_docs/distribution_plot.md @@ -2,7 +2,7 @@ (dist_plot_target)= -![](pics/distribution_plot.png) +![](../pics/distribution_plot.png) The distribution plot allows visualizing the distribution of data quality measures across the dataset, where measures of network connectivity (specificity and amplitude) are contrasted with measures of confounds across samples (each point in the plot is a scan). Data points labelled in gray were removed using `--scan_QC_thresholds`, where the gray dotted lines correspond to the QC thresholds selected for network specificity (Dice overlap) and DR confound correlation. Among the remaining samples and for each metric separately, scan presenting outlier values were detected based on a modified Z-score threshold (set with `--outlier_threshold`, 3.5 by default) and labelled in orange. The derivation of the quality metrics is described in details on the [metrics documentation](dist_plot_metrics).
    @@ -11,6 +11,6 @@ The report was designed to subserve two main functions: 1. Inspect that network ## Scan-level thresholds based on network specificity and confound temporal correlation -![](pics/scan_QC_thresholds.png) +![](../pics/scan_QC_thresholds.png) The measures of network specificity (using Dice overlap) and temporal correlation with confounds (where confound timecourses are extracted using confound components specified with `--conf_prior_idx` and measured through dual regression) were defined in {cite}`Desrosiers-Gregoire2023-jm` for conducting scan-level QC (the figure above is reproduced from the study). They were selected as ideal measures for quantifying issues of network detectability and spurious connectivity (the figure above demonstrate how [categories of scan quality outcomes](quality_marker_target) can be distinguished with these metrics), and applying inclusion thresholds to select scans which respect assumptions for network detectability and minimal effects from confounds. diff --git a/docs/pics/scan_QC_thresholds.png b/docs/pics/scan_QC_thresholds.png index 54485e22653ece37cf16a358a6529f7a498fd6c9..8c948961c3d4881bb828835bfa8046f78ce3cbec 100644 GIT binary patch literal 808953 zcmZU*2UwF=`#(-!-%9PDks!&BXf`EWPTb3395dt!UB1C3ngvd(twUrGbdju*d zBP=08BoLxy&xmY7g0fcbH`}gXLz#qpRUbheA<5QN|`|q1auML8Me-sTez7u2( z^$3FB4RGg!!{N%Fe%^s@cOSYdLjxe$v}@=1_m$jG)Q_9~ge0$p? zPUbh&vm}@If3$p?`rV~pk4OFI(V$W^ugHTOlpXo-4IZ-y3FMJ$SqBS*eIy+fRZ1tdVbiJ; z#lp~iZxr{*=np77zW3LUejQUJ39RllFiRzHb3K8@rZ3} zOKtaig%dn1%L00SCV!S7Q$!JFirBbvZ&+L!@D2bT)u8QLU01S zQ8#4`p}^bY5ZA3N4H8%fmQpoMGOl98I`1%jFCLmTH 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b/docs/nested_docs/optim_CR.md index 3e3aa4e..bd674b8 100644 --- a/docs/nested_docs/optim_CR.md +++ b/docs/nested_docs/optim_CR.md @@ -1,30 +1,15 @@ -# Selection of confound correction strategy +# Optimization of confound correction strategy -This page relates the set of confound correction strategies to relevant quality issues observed through the scan diagnosis and group quality reports, and is designed to support selecting ideal additional corrections to improve analysis quality. +(optim_CR)= -In the table below, the correction strategies are listed in the recommended order of priority (i.e. which one should be attempted first). The confound correction operations are described elsewhere in the [confound correction pipeline](confound_pipeline_target). +On this page is a procedure for improving confound correction design based on observations from the data quality assessment reports. These recommendations were originally developed in {cite}`Desrosiers-Gregoire2023-jm`, and consist of a stepwise protocol where confound correction is improved incrementally while referring to data quality reports and the table found on this page, relating data quality features to corresponding corrections. The protocol is as follows: +1. Initiate a **minimal** confound correction, and generate data quality reports at the analysis stage. Correction should be minimal at first to mitigate potential issues of over-correction, where network activity itself can be removed by excessive correction. A minimal correction can consist of applying frame censoring using framewise displacement and the regression of 6 motion parameters together with spatial smoothing. +2. Evaluation of the data quality reports (as described in the [guidelines on the main page](analysis_QC_target)) +3. The most sensible additional correction is selected based on the observations and using the table below. +4. The confound correction pipeline stage is re-run with **one** additional correction at a time, and the data quality reports are re-evaluated. Only a single correction is tested at a time so its impact can be evaluated, and the correction is only kept if there were beneficial impacts. +5. Repeat 3. and 4. until desirable quality outcomes are met, or no further options are left for confound correction. -| Correction strategy (# priority order) | Data quality issue(s) | Additional considerations | -|-----------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Framewise displacement censoring (#1)
      `--frame_censoring FD_censoring` | Spikes in the framewise displacement timecourse. | Generally recommended, since frames exceeding motion thresholds can be too
      corrupted for recovery post-processing, and represent an important concern for
      spurious effects {cite}`Power2012-ji`. | -| 6 motion parameters regression (#2)
      `--conf_list mot_6` | Fluctuations in the 6 motion parameters associated to shifts in
      signal from the carpet plot and/or network timecourses. | Generally recommended, unless there is a specific contra-indication (e.g. unreliable
      motion estimation). | -| DVARS censoring (#3)
      `--frame_censoring DVARS_censoring` | DVARS spikes (not explained by framewise displacement) are
      affecting the network timecourses. | | -| Highpass (#4)
      `--highpass` | Global signal drifts can be observed from the carpet plot. | It is recommended to ~30sec at each edge of the timeseries with highpass. Other
      strategies may be considered before highpass if the timeseries are already short. | -| ICA-AROMA (#5)
      `--ica_aroma` | Disturbances in the $BOLD_{SD}$ and/or $GS_{cov}$ maps. | - The dimensionality of the ICA decomposition should not be unreasonably high
      (ideally ~10-20 components per scan). This can be assessed by inspecting the
      AROMA report in *confound_correction_datasink/aroma_out*. If dimensionality is
      high, AROMA can be re-attempted with a pre-defined number of components
      `dim=X`.
      - Classification accuracy can be unpredictable and vary across datasets. Make sure
      the correction did not impact network detectability, and ideally, inspect the AROMA
      classification report. | -| aCompCor regression (#6)
      `--conf_list aCompCor_percent`/`aCompCor_5` | - Disturbances in the $BOLD_{SD}$ and/or $GS_{cov}$ maps.
      - Low network detectability
      - High scan-level conf. corr. | - This regression strategy is considered first, as it is most likely to yield important
      improvements for both motion and physiological confounds.
      - aCompCor can be performed by either regressing timecourses from the first 5 PCA
      component, or from the set of first components explaining 50% of the variance. If the
      later is selected, the decomposition should be inspected to ensure that the
      dimensionality is sensible. | -| WM/CSF regression (#7)
      `--conf_list WM_signal CSF_signal` | - Physiological or global signatures in the $BOLD_{SD}$ and/or
      $GS_{cov}$ maps.
      - High scan-level conf. corr. | The regression of WM/CSF signal can be considered in combination with aCompCor
      is disturbances persist in the $GS_{cov}$. | -| 24 motion parameters regression (#8)
      `--conf_list mot_24` | - Motion visible from the 6 motion parameters.
      - Motion signatures in the $BOLD_{SD}$ and/or $GS_{cov}$ maps
      or with the group inter-scan SD.
      - High group correlation with mean framewise displacement. | - Extra motion regression is considered later, as previous correction strategies may
      be more efficient at correcting motion.
      - Consider the impact on tDOF. | -| Homogenize $BOLD_{SD}$ (#9)
      `--scale_variance_voxelwise` | - Disturbances in the $BOLD_{SD}$ and/or $GS_{cov}$ maps are
      persisting despite previous corrections.
      - Low network detectability. | This technique is considered after regression strategies, as it does not remove
      confounds, but only scales down the contribution of high-variance voxels. | -| Lowpass (#10)
      `--lowpass` | High spectral power observed in the high frequency range. | - Lowpass is considered later as it can reduce network detectability.
      - Lowpass will increase temporal correlations, and thus conf. corr. measures will be
      inflated. Thresholds for this metric must be reconsidered accordingly. | -| Global signal regression (#11)
      `--conf_list global_signal` | High confound correlations at the scan or group level. | Global signal removal can have an important impact on network detectability, likely
      removing network activity as detected by $GS_{cov}$. Larger sample sizes are likely
      necessary to retain sufficient statistical power. | -| Outlier removal (#12)
      `--scan_list` | Outliers are observed from the datasets distribution plots and are
      driving a group-wise confound correlation. | The scan ID of outliers can be identified in `data_diagnosis_datasink/analysis_QC`
      `/analysis_QC/sample_distributions/*_outlier_detection.csv`, and should then
      removed from the group by specifying `--scan_list` | -| tDOF matching (#13)
      `--match_number_timepoints` | tDOF is correlated with connectivity at the group level. | This correction will likely reduce tDOF in several scans to enforce the same number
      across scan. This is considered last to attempt preserving tDOF with other
      corrections. | +The table below offers guidance for prioritizing additional corrections based on observations from the data quality reports. The confound correction workflow and the various strategies available are described elsewhere in the [confound correction pipeline](confound_pipeline_target). - - -## Examples of quality issues from - -* DVARS spike -* GS/BOLDsd disturbances -* +![](../pics/CR_optimization_table.png) \ No newline at end of file diff --git a/docs/pics/CR_optimization_table.png b/docs/pics/CR_optimization_table.png new file mode 100644 index 0000000000000000000000000000000000000000..6987deda905325f37d4caed75b4548316e1635ef GIT binary patch literal 1114407 zcmd?R1y`2q+BS+^V50~k21G{murvx1HL^rV>qWg!u{O$73X=>^*ocie35E1?PdxJ3Mz?9=j16UHhv+0Hm=7j z63-7w;D4L$Tv9copx``0{;aZ&IAw_!_nDtpF;_6XWp1T?PnW{V%8FCp#L!Gf`;IQB z={>!G5fNGnih~pq=gwTU4jgK>Qdgc@Sy>Sl-+uT0Ly!IKmn$@s68GPB4i_vL%w!c*~cJmHo>lU(_mHN2mwJIb|~(zesY@f z?-w@jj*~iu`}Z?QOiWZ7q}c2{WUTyRH_Kr=yAxQ@AJb=Nts^2LRMR!o)w|l-Uc2#- z^|IL+cN!5Mu96-U7}(X-<%yT&UNbT=v2k<%NFHu$JGghRkA2_{3JM#a;NZl}%#-33 zbq>aH4`>WmT!#DmODgWZr6@b#!lIhYxZ*)YL2=J~eo|ljU{OWIE2q+R`7+sWJf`|JN@T>kI7!2bWoW~~d4iW;09>wNy~*_)uCAW3ibn5PVm zDo-|3myX7?Yq|b4)Av{RX0$q1Z(y*$9xZtC)o1HV3mh4SBN zJDoS(XgJe7Us6&c>i&)3yIlZ#Lb_r{lvd5hy^E3u=an0x6e2?GOz zW$B((Jo-Nw9mc-yRhl`<$QXU6{qu~?g>8{>%1N}EkG5{zIw#<_P%c)$^zX^V|E;R$ zCVHu9X$>q|apRuB!Hyw%kL|i^ViauWx_xRRr1sK_*`F+kdX&>OKQ-ttWMSZ{bHHKp z#{w^%NOa#{OXclp)%VvQ;6~#|gl#9I(~X-VnL_Nx#i}-2H8rH@P0e_puodS6U%R&$ z=JA;sff|F_Sc|GetHC;s&&Su*L`eBar@c9Hv9!3DiQ(3x4b*=Z=XFJO3D+z-zHAb& zz=qx)sFHOrf>SF$UOqyyd9FOdmQPZr*fp(Y=dN98w|^YYTbgdN5gW}i>)lOD8@p!1*1qxXGD!)EHy$1y zr9NCqwo?N!7K3&7%*@KBMD+Fb8u zcH5XJC#qk$bLYJ)!>L5G-pUdTQgCpv?a+tfnpj1)tnBQbId%8l)YK8*zsu2!+FteH z((*=q#I46UQ_;mbEKKuPk-n^OF1Msk<2*D?}o z4H?AjRSfGBYk!1Yw3z6T@nTh!tc_FladUHHRgCo0&=CR7k z%8Jf)c?^6HeH0iJG+`gygc0E|Zpl?`N;j^4vSqL5>(_7JoqrK~;T5exb9UMRo31w^ zA#6vFp4Zmae(vHTXKtR_(a~`=_9{zlysCn6Q@Y~U=hS8*%R>z*jcJBHx=ke|5_l?4 z&oRwwdA4bmL%%||?B$GB`EbMF``ZUXRwL{f3J>V2_3p^ZNC6GM+zv{E(EA@_736DJ`EtIM&a#JQJlIJ9Y>;%%|!$VK(yf z^OcGZ2;MvAv^3=tT9@x&mz9~RlAffV^^W6OI!)2+{Cp{X3>9PZmMztoXN&gF>!>-^ zRq)_mAt529o-7`nB0OKed|^I$QsvE?H`#YPoyqi(X^;uvzh}N^dhzVg&+6*xl)YF< z)cf}7M=K_(t4>c(&lumJ7)k0ZcB4zr%grTiMm7alW81^(C|SC?F%IR#z;>revde$_ zp<8_Y`c*Z(xw+Xa@9r838;jvaK8<|)hUDRnj(7LEOS?N?jwJs2rBWNKSoWQ&sH#d9 zS5dC;=kt2~`g)9Vf|{bR<wBF=RAlMD*ux=@WmIt&t^3E@Gmm(UejQi1f26wT z6&;^>pIn)Y!!=so+i&};qndKtL;5ko^IaaYsRp&I0a6>hn6I22&7E*vzjeRwU|t9w zVX$!9sTzMiLnBufrFh>9FKI%TmlgtobNc#jD^PTkfBYUQF4H@=G}Fp=`};x6F?+Ef z83%Hg6Fn6iX@CCQ30U&sY-02Tu#_(B#>Oki%nTgw5YQ3vZ(FkcXWg9{(isC zxq_wXYchc+uLzj@K0X{LAMyNFxo@bb-E?1=o7jmVVf)!TiM{DZMMX7EE&h2FDed>} z#a^z){q?yvW|$P2ZKoFBS8zXj_RODK$N5%OZ~}%mPG@LnsH?X(>Vr;kBjx6$`N5=t zri>)rrouw;i>~{B4AjQ)-Tby)BlFHDbLkH^zVg~m-p-rr32?UUF7>ilnzslrtK_}@ zkwV7z_$Rp>>#=uYOOpy%L*;*Rt}0<`)X|ydhPD?zjl2Ehol-I70g+$Zoqiv{{_%4T+TteW+_UGtz9(nxCg#j#`weS9O4D|Hxj#JU{UOIB&<@>pP zUf$l3B_2$JWn2ZR^UF?MZ!Yp0*0FP_CZ9{WRgqjnzkmNd`{BhV;0Ih>!C4Je`f9Y? 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z?8S>0U(el1bc6p_mtx3LM-CJDn=8{$brA9efjM&Ihz3m(^vcg8?Q`tnOBbK1O(l9ASi8AEWa_ISF?%fe^_Cy2MRiqq7a(~F z8{Q7|2O<###b0mH8p7V&JbD2@9Td|(Qjs7K4%BSyE%Wa;-3a}YqU?_n1hGPi7s7dl zwTveJ@1LVB3VITEKTl6j%!Y>4+WN(IX9YBd9jGvb`>wYr5BZ{C>fk>+;sb=!v)qC*-v%>GqhtXN5#qg)pV&$X; M( literal 0 HcmV?d00001 From 7be660945bbf818b56ed87aef5235527f1ea46c9 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 10 Oct 2023 19:35:27 -0400 Subject: [PATCH 11/15] documentation for the group statistical report. --- docs/analysis_QC.md | 2 +- docs/metrics.md | 6 +- docs/nested_docs/group_stats.md | 13 +- docs/nested_docs/optim_CR.md | 2 +- docs/pics/CR_optimization_table.png | Bin 1114407 -> 0 bytes docs/pics/CR_optimization_table.svg | 1 + docs/pics/group_stats_QC.svg | 1565 +++++++++++++++++++++++++++ 7 files changed, 1585 insertions(+), 4 deletions(-) delete mode 100644 docs/pics/CR_optimization_table.png create mode 100644 docs/pics/CR_optimization_table.svg create mode 100644 docs/pics/group_stats_QC.svg diff --git a/docs/analysis_QC.md b/docs/analysis_QC.md index 865ea24..f9836d6 100644 --- a/docs/analysis_QC.md +++ b/docs/analysis_QC.md @@ -7,8 +7,8 @@ maxdepth: 3 --- nested_docs/scan_diagnosis.md -nested_docs/group_stats.md nested_docs/distribution_plot.md +nested_docs/group_stats.md nested_docs/optim_CR.md ``` diff --git a/docs/metrics.md b/docs/metrics.md index 48eb4cb..a553f90 100644 --- a/docs/metrics.md +++ b/docs/metrics.md @@ -57,9 +57,13 @@ where $Y_{i,t}$ corresponds to the BOLD signal in brain voxel $i$ at timepoint $ (dist_plot_metrics)= * **Network amplitude (not computed for seed-based connectivity)**: The overall network amplitude is summarized by computing the L2-norm across the network connectivity map outputed from dual regression (i.e. linear coefficients from the [second regression ${\beta}_{SM}$](DR_target)) -* **Network specificity**: The network map (seed-based or dual regression) and the corresponding canonical network map are thresholded to include the top 4% of voxels with highest connectivity, and the overlap of the thresholded area is computed using Dice overlap. +* **Network specificity**: The network map (seed-based or dual regression) and the corresponding canonical network map are thresholded to include the top 4% of voxels with highest connectivity, and the overlap of the thresholded area is computed using Dice overlap. For dual regression, the 'canonical network' map will consist of the original ICA component corresponding to that network provided with `--prior_maps`, and for seed-based connectivity, the reference network maps are provided using the `--seed_prior_list` parameter. * **Dual regression confound correlation**: The timecourse for a single network (from a seed or dual regression) is correlated with the timecourse from each confound component (provided using `--prior_confound_idx`) modelled through dual regression, then the absolute mean correlation is computed to obtain the average amplitude of confound correlations for this specific network analysis. * **Total $CR_{SD}$**: The total standard deviation across the [predicted confound timeseries $Y_{CR}$](CR_target). * **Mean framewise displacement**: The mean framewise displacement computed across time (only including frames after censoring applied for confound correction). * **Temporal degrees of freedom**: The remaining degrees of freedom post-confound correction are calculated as `tDOF = Original number of timepoints - Number of censored timepoints - Number of AROMA components removed - Number of nuisance regressors`. +## Group statistical QC report +(group_QC_metrics)= +* **Specificity of network variability**: similarly to network specificity in the distribution plot, the network variability map and the corresponding canonical network map are thresholded to include the top 4% of voxels, and then the overlap is estimated using Dice overlap. +* **Mean confound correlation**: for each confound correlation map (either $CR_{SD}$, mean FD or tDOF), the mean is computed across voxels included within the thresholded area of the canonical network map, to obtain a mean correlation within the network's core region. \ No newline at end of file diff --git a/docs/nested_docs/group_stats.md b/docs/nested_docs/group_stats.md index a8583b0..aa8180c 100644 --- a/docs/nested_docs/group_stats.md +++ b/docs/nested_docs/group_stats.md @@ -1 +1,12 @@ -# Group stats \ No newline at end of file +# Group stats +![](../pics/group_stats_QC.svg) + +Inspecting scan-level features is insufficient to conclude that inter-scan variability in connectivity isn't itself impacted (which is of primary interest for group analysis). This final report is aimed at inspecting features of connectivity variability at the group level, and focuses on two aspects: + +1. **Specificity of network variability:** the standard deviation in connectivity across scan is computed voxelwise. This allows to visualize the spatial contrast of network variability. If primarily driven by network connectivity, the contrast should reflect the anatomical extent of the network of interest (as in the example above for the mouse somatomotor network), or otherwise may display spurious or absent features. For more details on the development of this metric, consult {cite}`Desrosiers-Gregoire2023-jm`. + - **Relationship to sample size**: {cite}`Desrosiers-Gregoire2023-jm` demonstrate that the contrast of the network variability map depends on sample size. If network connectivity is observed in individual scans, but not in this statistical report, increasing sample size may improve this contrast. +2. **Correlation with confounds:** Connectivity is correlated across subject, for each voxel, with either of the three measures of confound included: variance explained from confound correction at a given voxel ($CR_{SD}$, see [predicted confound timeseries $Y_{CR}$](CR_target)), mean framewise displacement (FD), or temporal degrees of freedom. This allows establishing the importance of the association with potential confounds. What constitute a 'concerning' correlation may depend on the study, and the effect size of interest (i.e. is the effect size of interest much higher or similar to the effect size of confounds?). + +**Quantitative CSV report**: A CSV file is also automatically generated along the figure, which records a quantitative assessment of these two aspects. More specifically, the overlap between the network variability map and the reference network map is measuring using Dice overlap, and for confound measures, the mean correlation is measured within the area of the network (consult the [metric details elsewhere](group_QC_metrics)). These measures can be referred to for a quantitative summary instead (although visualization is preferred, as the Dice overlap for network variability may not perfectly distinguish network and spurious features). + +**IMPORTANT**: the validity of this report is dependent on whether [scan-level assumptions](dist_plot_target) of network detectability and minimal confound effects are met. This is because either the lack of network activity or spurious effects in a subset of scan can drive 'apparent' network variability, since there will be differences in the presence VS absence of the network across scans, but these differences may be actually driven by data quality divergences. \ No newline at end of file diff --git a/docs/nested_docs/optim_CR.md b/docs/nested_docs/optim_CR.md index bd674b8..b5020b1 100644 --- a/docs/nested_docs/optim_CR.md +++ b/docs/nested_docs/optim_CR.md @@ -12,4 +12,4 @@ On this page is a procedure for improving confound correction design based on ob The table below offers guidance for prioritizing additional corrections based on observations from the data quality reports. 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From ff5265008f90a3c61592a859508a0762943f0b1b Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 10 Oct 2023 20:45:06 -0400 Subject: [PATCH 12/15] added link for group stats docu page --- docs/nested_docs/group_stats.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/nested_docs/group_stats.md b/docs/nested_docs/group_stats.md index aa8180c..c9c9269 100644 --- a/docs/nested_docs/group_stats.md +++ b/docs/nested_docs/group_stats.md @@ -1,4 +1,7 @@ # Group stats + +(group_stats_target)= + ![](../pics/group_stats_QC.svg) Inspecting scan-level features is insufficient to conclude that inter-scan variability in connectivity isn't itself impacted (which is of primary interest for group analysis). This final report is aimed at inspecting features of connectivity variability at the group level, and focuses on two aspects: From a6b258c4bead5aeff38ef195f1b38dc6df25cc07 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 10 Oct 2023 21:03:36 -0400 Subject: [PATCH 13/15] Changed the link to the tutorial repo --- docs/running_the_software.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/running_the_software.md b/docs/running_the_software.md index 59b899d..e9bfd53 100644 --- a/docs/running_the_software.md +++ b/docs/running_the_software.md @@ -195,7 +195,7 @@ The syntax in Docker is very similar to Singularity, except that `-B` is replace ## Additional Resources * **Workshop and tutorial for RABIES:** - * [Hands-on tutorial](https://github.com/grandjeanlab/INCF_preclinical/tree/main) on RABIES presented at the INCF Neuroinformatics Assembly 2023 (in the `rabies/` folder of the repository). + * [Hands-on tutorial](https://github.com/CoBrALab/RABIES_tutorial) on RABIES (originally developed for and presented at the INCF Neuroinformatics Assembly 2023). * A workshop providing a complete software overview was [recorded and posted online](https://www.youtube.com/watch?v=LZohKlUgycc&t=2766s&ab_channel=DouglasResearchCentre) on February 2023. * Conversion from Bruker raw to Nifti formats can be handled with [BrkRaw](https://brkraw.github.io/) (consult [associated documentation](https://github.com/CoBrALab/documentation/wiki/bruker2nifti-conversion) from the CoBrALab) * [CoBrALab recommendations](https://github.com/CoBrALab/documentation/wiki/Running-RABIES-on-niagara) for using compute canada. From fdb25cdd69d3aacaa0c0b909fd4f1e6852521d82 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 10 Oct 2023 21:19:48 -0400 Subject: [PATCH 14/15] Better mitigation of permission issues with docker --- docs/running_the_software.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/running_the_software.md b/docs/running_the_software.md index e9bfd53..ba5e933 100644 --- a/docs/running_the_software.md +++ b/docs/running_the_software.md @@ -184,12 +184,12 @@ The same logic applies at the analysis stage. ### Docker execution ```sh -docker run -it --rm \ +docker run -it --rm --user $(id -u) \ -v $PWD/input_BIDS:/input_BIDS:ro \ -v $PWD/preprocess_outputs:/preprocess_outputs/ \ gabdesgreg/rabies:tagname -p MultiProc preprocess /input_BIDS/ /preprocess_outputs/ --apply_STC --TR 1.2 --commonspace_reg masking=true,brain_extraction=false,template_registration=SyN,fast_commonspace=false ``` -The syntax in Docker is very similar to Singularity, except that `-B` is replaced by `-v`, and further parameters may be needed (e.g. `-it`, `--rm`). When using Docker, there may be issues with writing permissions, and you may need to explicitely allow permissions on the output folder (e.g. `chmod 777 preprocess_outputs/`). Note that 'tagname' should be replaced by the proper RABIES version you are using (e.g. 0.4.8). +The syntax in Docker is very similar to Singularity, except that `-B` is replaced by `-v`, and further parameters may be needed (e.g. `-it`, `--rm`). `--user $(id -u)` can be added to mitigate writing permission issues when using Docker. Note that 'tagname' should be replaced by the proper RABIES version you are using (e.g. 0.4.8). ## Additional Resources From e31fa06595448275a963be780a186587c12c8dc6 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Tue, 10 Oct 2023 21:41:27 -0400 Subject: [PATCH 15/15] Document motion_datasink --- docs/outputs.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/outputs.md b/docs/outputs.md index 8b414dc..446b840 100644 --- a/docs/outputs.md +++ b/docs/outputs.md @@ -46,8 +46,8 @@ Multiple datasink folders are generated during preprocessing for different outpu - `unbiased_to_atlas_warp/`: non-linear transforms for the alignment between unbiased template and the atlas in commonspace - `unbiased_to_atlas_inverse_warp/`: inverse of the non-linear transforms for the alignment between unbiased template and the atlas in commonspace -- `confounds_datasink/`: regroups data features which are later relevant for subsequent confound correction. - - `confounds_csv/`: a CSV file grouping a set of nuisance timeseries, which can be used for confound regression. The timeseries generated are detailed in the Nuisance timecourse estimation documentation (https://rabies.readthedocs.io/en/latest/preprocessing.html#nuisance-timecourse-estimation). +- `motion_datasink/`: files derivated from motion estimation + - `motion_params_csv/`: contains the 24 motion parameters which can be used as nuisance regressors at the confound correction pipeline stage. - `FD_csv/`: a CSV file with timescourses for either the mean or maximal framewise displacement (FD) estimations. - `FD_voxelwise/`: a Nifti image which contains framewise displacement evaluated at each voxel - `pos_voxelwise/`: a Nifti image which tracks the displacement (derived from the head motion realignment parameters) of each voxel across time

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These will\n" - "be fitted during Dual ICA and provide the BOLD components during --data_diagnosis.\n" + "Specify the indices for the priors corresponding to BOLD sources of interest from --prior_maps. \n" + "This will determine the set of networks analyzed for --data_diagnosis. \n" + "IMPORTANT: index counting starts at 0 (i.e. the first component is selected with 0, not 1) \n" "(default: %(default)s)\n" "\n" ) @@ -803,19 +804,19 @@ def get_parser(): default=[0, 2, 6, 7, 8, 9, 10, 11, 13, 14, 21, 22, 24, 26, 28, 29], help= - "Specify the indices for the confound components from --prior_maps. This is pertinent for the\n" - "--data_diagnosis outputs.\n" + "Specify the indices for the confound components from --prior_maps. This is pertinent for \n" + "evaluating features in the --data_diagnosis outputs.\n" + "IMPORTANT: index counting starts at 0 (i.e. the first component is selected with 0, not 1) \n" "(default: %(default)s)\n" "\n" ) analysis.add_argument( "--data_diagnosis", dest='data_diagnosis', action='store_true', help= - "This option carries out the spatiotemporal diagnosis as described in Desrosiers-Gregoire et al. \n" - "The diagnosis generates key temporal and spatial features both at the scan level and the group\n" - "level, allowing the identification of sources of confounds and data quality issues. We recommend \n" - "using this data diagnosis workflow, more detailed in the publication, to improve the control for \n" - "data quality issues and prevent the corruptions of analysis outputs.\n" + "Generates a set of data quality assessment reports as described in Desrosiers-Gregoire et al. 2023. \n" + "These reports aim to support interpreting connectivity results and account for data quality issues, \n" + "and include: 1-a scan-level qualitative diagnosis report, 2-a quantitative distribution report, 3-a \n" + "group-level statistical report for network analysis. \n" "(default: %(default)s)\n" "\n" ) @@ -856,7 +857,7 @@ def get_parser(): "Select this option to output the network correlation with the original image intensity (calculated \n" "during detrending) and the BOLDsd (signal variability). These two additional features allow to \n" "further inspect potential issues of signal amplitude scaling between scans. However, it is unclear \n" - "whether signal of interest (i.e. neural metabolism) contribute to each measure, so these outpu8ts should \n" + "whether signal of interest (i.e. neural metabolism) contribute to each measure, so these outputs should \n" "be interpreted with caution. \n" "(default: %(default)s)\n" "\n" From 3caa2b4c17ba7dded12d703ee7f7d9998118ec94 Mon Sep 17 00:00:00 2001 From: Gab-D-G Date: Mon, 9 Oct 2023 15:35:37 -0400 Subject: [PATCH 08/15] Documentation for distribution plot --- docs/metrics.md | 10 ++++++++++ docs/nested_docs/distribution_plot.md | 17 ++++++++++++++++- docs/pics/distribution_plot.png | Bin 0 -> 1017437 bytes docs/pics/scan_QC_thresholds.png | Bin 0 -> 856560 bytes 4 files changed, 26 insertions(+), 1 deletion(-) create mode 100644 docs/pics/distribution_plot.png create mode 100644 docs/pics/scan_QC_thresholds.png diff --git a/docs/metrics.md b/docs/metrics.md index 356bfb7..48eb4cb 100644 --- a/docs/metrics.md +++ b/docs/metrics.md @@ -53,3 +53,13 @@ where $Y_{i,t}$ corresponds to the BOLD signal in brain voxel $i$ at timepoint $ * **SBC network X**: The voxelwise correlation coefficients (pearson's r) estimated with seed-based connectivity (for the Xth seed provided for analysis with `--seed_list`). +## Distribution plot +(dist_plot_metrics)= + +* **Network amplitude (not computed for seed-based connectivity)**: The overall network amplitude is summarized by computing the L2-norm across the network connectivity map outputed from dual regression (i.e. linear coefficients from the [second regression ${\beta}_{SM}$](DR_target)) +* **Network specificity**: The network map (seed-based or dual regression) and the corresponding canonical network map are thresholded to include the top 4% of voxels with highest connectivity, and the overlap of the thresholded area is computed using Dice overlap. +* **Dual regression confound correlation**: The timecourse for a single network (from a seed or dual regression) is correlated with the timecourse from each confound component (provided using `--prior_confound_idx`) modelled through dual regression, then the absolute mean correlation is computed to obtain the average amplitude of confound correlations for this specific network analysis. +* **Total $CR_{SD}$**: The total standard deviation across the [predicted confound timeseries $Y_{CR}$](CR_target). +* **Mean framewise displacement**: The mean framewise displacement computed across time (only including frames after censoring applied for confound correction). +* **Temporal degrees of freedom**: The remaining degrees of freedom post-confound correction are calculated as `tDOF = Original number of timepoints - Number of censored timepoints - Number of AROMA components removed - Number of nuisance regressors`. + diff --git a/docs/nested_docs/distribution_plot.md b/docs/nested_docs/distribution_plot.md index e5307c8..da35ac7 100644 --- a/docs/nested_docs/distribution_plot.md +++ b/docs/nested_docs/distribution_plot.md @@ -1 +1,16 @@ -# Distribution plot \ No newline at end of file +# Distribution plot + +(dist_plot_target)= + +![](pics/distribution_plot.png) + +The distribution plot allows visualizing the distribution of data quality measures across the dataset, where measures of network connectivity (specificity and amplitude) are contrasted with measures of confounds across samples (each point in the plot is a scan). Data points labelled in gray were removed using `--scan_QC_thresholds`, where the gray dotted lines correspond to the QC thresholds selected for network specificity (Dice overlap) and DR confound correlation. Among the remaining samples and for each metric separately, scan presenting outlier values were detected based on a modified Z-score threshold (set with `--outlier_threshold`, 3.5 by default) and labelled in orange. The derivation of the quality metrics is described in details on the [metrics documentation](dist_plot_metrics). +
    + +The report was designed to subserve two main functions: 1. Inspect that network specificity is sufficient and the temporal correlation with confounds (i.e. DR confound corr.) minimal, and set thresholds for scan inclusion using `--scan_QC_thresholds` (top right subplot, more details on this below), and 2. complement the group statistical report to visualize the association between connectivity and the three confound measures included in the report ($CR_{SD}$, mean FD and tDOF). In the later case, it can be possible for instance to determine whether a group-wise correlation in statistical report is driven by outliers. + +## Scan-level thresholds based on network specificity and confound temporal correlation + +![](pics/scan_QC_thresholds.png) + +The measures of network specificity (using Dice overlap) and temporal correlation with confounds (where confound timecourses are extracted using confound components specified with `--conf_prior_idx` and measured through dual regression) were defined in {cite}`Desrosiers-Gregoire2023-jm` for conducting scan-level QC (the figure above is reproduced from the study). They were selected as ideal measures for quantifying issues of network detectability and spurious connectivity (the figure above demonstrate how [categories of scan quality outcomes](quality_marker_target) can be distinguished with these metrics), and applying inclusion thresholds to select scans which respect assumptions for network detectability and minimal effects from confounds. diff --git a/docs/pics/distribution_plot.png b/docs/pics/distribution_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..987f12f87b8eaa680e0c2a1115248f2d9120073b GIT binary patch literal 1017437 zcmeFZc{r49|35zW)8}rJRI+4AE7{4K{Z3ISlnU8G_GK)?*y%20uN1N`A%tWZjHRqe zNOoq(h_Mc1Uk2avx@PEp?%(gf?{R$p`dr7;k;gsG>pb7f>-~Da-lu{z)m7;avK>Sq z5cJotUAcom9Kj$EC-nF41>bQnxU?Vqb->}8t}_B5bQb#ahg-U$EBNLK)Kwjn7SbGr zHg+;YpwVa{D|;JfQ)34+A*7Q<97&E1d`J}f&|fYnOQaLxmNCk~7KyU8GCAccCL|^# zE^x{cg>rC~5f--F{ZG)(3YULIc@#RNqp1TTu; z6%mmU6$k$lk`xgay?F7(AD-C=#3{t}D}UW}i=W23dl{g;c__18UDns5igo7gn{7g) 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