The file narps_open/data/description/analysis_pipelines_full_descriptions.tsv contains the description provided by each team participating to NARPS.
It is a conversion into tsv format (tab-separated values) of the original .xlsx file published in NARPS, which allows easier parsing with python.
The file narps_open/data/description/analysis_pipelines_derived_descriptions.tsv contains for each team a set of programmatically usable data based on the textual descriptions of the previous file. This data is available in the derived sub dictionary (see examples hereafter).
The file narps_open/data/description/analysis_pipelines_comments.tsv contains for each team a set of comments made by the NARPS Open Pipelines team about reproducibility and exclusions of the pipeline. This data is available in the comments sub dictionary (see examples hereafter).
The class TeamDescription of module narps_open.data.description acts as a parser for these two files.
You can use the command-line tool as so. Option -t is for the team id, option -d allows to print only one of the sub parts of the description among : general, exclusions, preprocessing, analysis, categorized_for_analysis, derived, and comments. Options --json and --md allow to choose the export format you prefer between JSON and Markdown.
Tip
In the following examples, use narps_description or python narps_open/data/description indifferently to launch the command line tool.
narps_description -h
# usage: __init__.py [-h] -t TEAM [-d {general,exclusions,preprocessing,analysis,categorized_for_analysis,derived,comments}]
#
# Get description of a NARPS pipeline.
#
# options:
# -h, --help show this help message and exit
# -t TEAM, --team TEAM the team ID
# -d {general,exclusions,preprocessing,analysis,categorized_for_analysis,derived,comments}, --dictionary {general,exclusions,preprocessing,analysis,categorized_for_analysis,derived,comments}
# the sub dictionary of team description
# --json output team description as JSON
# --md output team description as Markdown
narps_description -t 2T6S --json
# {
# "general.teamID": "2T6S",
# "general.NV_collection_link": "https://neurovault.org/collections/4881/",
# "general.results_comments": "NA",
# "general.preregistered": "No",
# "general.link_preregistration_form": "We did not pre-register our analysis.",
# "general.regions_definition": "We employed the pre-hypothesized brain regions (vmPFC, vSTR, and amygdala) from Barta, McGuire, and Kable (2010, Neuroimage). Specific MNI coordinates are:\nvmPFC: x = 2, y = 46, z = -8\nleft vSTR: x = -12, y = 12, z = -6, right vSTR = x = 12, y = 10, z = -6\n(right) Amygdala: x = 24, y = -4, z = -18",
# "general.softwares": "SPM12 , \nfmriprep 1.1.4",
# "exclusions.n_participants": "108",
# "exclusions.exclusions_details": "We did not exclude any participant in the analysis",
# "preprocessing.used_fmriprep_data": "Yes",
# "preprocessing.preprocessing_order": "We used the provided preprocessed data by fMRIPprep 1.1.4 (Esteban, Markiewicz, et al. (2018); Esteban, Blair, et al. (2018); RRID:SCR_016216), which is based on Nipype 1.1.1 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502) and we additionally conducted a spatial smoothing using the provided preprocessed data set and SPM12. Here, we attach the preprocessing steps described in the provided data set. \nAnatomical data preprocessing\nThe T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using N4BiasFieldCorrection (Tustison et al. 2010, ANTs 2.2.0), and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using antsBrainExtraction.sh (ANTs 2.2.0), using OASIS as target template. Brain surfaces we
# ...
narps_description -t 2T6S -d general --json
# {
# "teamID": "2T6S",
# "NV_collection_link": "https://neurovault.org/collections/4881/",
# "results_comments": "NA",
# "preregistered": "No",
# "link_preregistration_form": "We did not pre-register our analysis.",
# "regions_definition": "We employed the pre-hypothesized brain regions (vmPFC, vSTR, and amygdala) from Barta, McGuire, and Kable (2010, Neuroimage). Specific MNI coordinates are:\nvmPFC: x = 2, y = 46, z = -8\nleft vSTR: x = -12, y = 12, z = -6, right vSTR = x = 12, y = 10, z = -6\n(right) Amygdala: x = 24, y = -4, z = -18",
# "softwares": "SPM12 , \nfmriprep 1.1.4",
# "general_comments": "NA"
# }
narps_description -t 2T6S --md
# # NARPS team description : 2T6S
# ## General
# * `teamID` : 2T6S
# * `NV_collection_link` : https://neurovault.org/collections/4881/
# * `results_comments` : NA
# * `preregistered` : No
# * `link_preregistration_form` : We did not pre-register our analysis.
# * `regions_definition` : We employed the pre-hypothesized brain regions (vmPFC, vSTR, and amygdala) from Barta, McGuire, and Kable (2010, Neuroimage). Specific MNI coordinates are:
# vmPFC: x = 2, y = 46, z = -8
# left vSTR: x = -12, y = 12, z = -6, right vSTR = x = 12, y = 10, z = -6
# (right) Amygdala: x = 24, y = -4, z = -18
# * `softwares` : SPM12 ,
# fmriprep 1.1.4
# * `general_comments` : NA
# ## Exclusions
# * `n_participants` : 108
# * `exclusions_details` : We did not exclude any participant in the analysis
# ## Preprocessing
# * `used_fmriprep_data` : Yes
# * `preprocessing_order` : We used the provided preprocessed data by fMRIPprep 1.1.4 (Esteban, Markiewicz, et al. (2018); Esteban, Blair, et al. (2018); RRID:SCR_016216), which is based on Nipype 1.1.1 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502) and we additionally conducted a spatial smoothing using the provided preprocessed data set and SPM12. Here, we attach the preprocessing steps described in the provided data set.
# Anatomical data preprocessing
# ...Of course the narps_open.data.description module is accessible programmatically, here is an example on how to use it:
from narps_open.data.description import TeamDescription
description = TeamDescription('2T6S') # Set the id of the team here
# Access the object as a dict
print(description)
description['general.teamID']
# Access sub dictionaries
description.general
description.exclusions
description.preprocessing
description.analysis
description.categorized_for_analysis
description.derived
description.comments
# Access values of sub dictionaries
description.general['teamID']
# Other keys in general are: ['teamID', 'NV_collection_link', 'results_comments', 'preregistered', 'link_preregistration_form', 'regions_definition', 'softwares', 'general_comments']
description.exclusions['n_participants']
# Other keys in exclusions are: ['n_participants', 'exclusions_details']
description.preprocessing['motion']
# Other keys in preprocessing are: ['used_fmriprep_data', 'preprocessing_order', 'brain_extraction', 'segmentation', 'slice_time_correction', 'motion_correction', 'motion', 'gradient_distortion_correction', 'intra_subject_coreg', 'distortion_correction', 'inter_subject_reg', 'intensity_correction', 'intensity_normalization', 'noise_removal', 'volume_censoring', 'spatial_smoothing', 'preprocessing_comments']
description.analysis['RT_modeling']
# Other keys in analysis are: ['data_submitted_to_model', 'spatial_region_modeled', 'independent_vars_first_level', 'RT_modeling', 'movement_modeling', 'independent_vars_higher_level', 'model_type', 'model_settings', 'inference_contrast_effect', 'search_region', 'statistic_type', 'pval_computation', 'multiple_testing_correction', 'comments_analysis']
description.categorized_for_analysis['analysis_SW_with_version']
# Other keys in categorized_for_analysis are: ['region_definition_vmpfc', 'region_definition_striatum', 'region_definition_amygdala', 'analysis_SW', 'analysis_SW_with_version', 'smoothing_coef', 'testing', 'testing_thresh', 'correction_method', 'correction_thresh_']
description.derived['n_participants']
# Other keys in derived are: ['n_participants', 'excluded_participants', 'func_fwhm', 'con_fwhm']