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pmid:33741990
The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses.
Lukas Snoek · Maite M van der Miesen · Tinka Beemsterboer · Andries van der Leij · Annemarie Eigenhuis · H Steven Scholte
Scientific data · 2021 doi:10.1038/s41597-021-00870-6
Paper
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Analysis
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Execution
absent
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33741990", "kind": "science-record", "revision": 6, "content_hash": "sha256:36f30ea9da28024f4ee923335ed4b74519cac31dc701365f8ac60193dd80c128", "created_at": "2026-11-01T15:41:29Z", "updated_at": "2026-11-01T21:24:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses.", "doi": "10.1038/s41597-021-00870-6", "pmid": "33741990", "authors": ["Lukas Snoek", "Maite M van der Miesen", "Tinka Beemsterboer", "Andries van der Leij", "Annemarie Eigenhuis", "H Steven Scholte"], "journal": "Scientific data", "year": 2021, "abstract": "We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.", "claims": [{"id": "claim-method", "statement": "To do so, using the nistats Python package, we ran mass-univariate first-level GLS models (using an AR1 noise model) based on a design matrix including task-regressors based on the events convolved with a canonical HRF function as well as a discrete cosine basis set functioning as a high-pass filter of 128 seconds and six motion regressors and computed first-level contrast maps for each subject which were subsequently analyzed in a random effects group-level (intercept) model, resulting in whole-brain z-value maps.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33741990", "locator": "sections[52] \"Functional (BOLD) scans\" paragraph 8", "quote": "To do so, using the nistats Python package, we ran mass-univariate first-level GLS models (using an AR1 noise model) based on a design matrix including task-regressors based on the events convolved with a canonical HRF function as well as a discrete cosine basis set functioning as a high-pass filter of 128 seconds and six motion regressors and computed first-level contrast maps for each subject which were subsequently analyzed in a random effects group-level (intercept) model, resulting in whole-brain z-value maps."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All code used for curating, annotating, and (pre)processing AOMIC are version-controlled using git and can be found in project-specific Github repositories within the NILAB-UvA Github organization: https://github.com/orgs/NILAB-UvA. Many pre and postprocessing steps were identical across datasets, so the code for these procedures is stored in a single repository: https://github.com/NILAB-UvA/AOMIC-common-scripts. Possible parameters are all hard-coded within the scripts, except for a single positional parameter pointing to the directory to be processed. For custom Python-based scripts, we used Python version 3.7. All code was developed on a Linux system with 56 CPUs (Intel Xeon E5-2680 v4, 2.40 GHz) and 126GB RAM running Ubuntu 16.04. All curation, preprocessing, and analyses were run on said Linux system, apart from the Fmriprep, MRIQC, and Freesurfer analyses, which were run in a Docker container provided by those software packages. Custom code was parallelized to run on multiple CPUs concurrently using the Python package joblib (https://joblib.readthedocs.io).\nFor curation, preprocessing, and analysis of the datasets, we used a combination of existing packages and custom scripts (written in Python or bash). To convert the data to the Brain Imaging Data Structure (BIDS), we used the in-house developed, publicly available software package bidsify (v0.3; https://github.com/NILAB-UvA/bidsify), which in turn uses the dcm2niix (v1.0.20181125)94 to convert the Philips PAR/REC files to compressed nifti files. In contrast to the data from PIOP1 and PIOP2 (which were converted to nifti using dcm2niix), r2aGUI (v2.7.0; http://r2agui.sourceforge.net/) was used to convert the data from ID1000. Because r2aGUI does not correct the gradient table of DWI scans for slice angulation, we used the angulation_correction_Achieva Matlab script (version December 29, 2007) from Jonathan Farrell to do so (available for posterity at https://github.com/NILAB-UvA/ID1000/blob/master/code/bidsify/DTI_gradient_table_ID1000.m). To remove facial characteristics from anatomical scans, we used the pydeface package (v.1.1.0)86. Finally, to convert the raw physiology files (i.e., Philips “SCANPHYSLOG” files) to BIDS, we used the in-house developed, publicly available Python package scanphyslog2bids (v0.1; https://github.com/lukassnoek/scanphyslog2bids). The outputs from the BIDS-conversion pipeline were checked using the bids-validator software package (v1.4.3).\nAnatomical and functional MRI preprocessing were done using Fmriprep (v1.4.1; see the Derivatives section for extensive information about Fmriprep’s preprocessing pipeline)42. For our DWI preprocessing pipeline, we used tools from the MRtrix3 package (www.mrtrix.org; v3.0_RC3)60 and FSL (v6.0.1)95. For the VBM and dual regression pipelines, we used FSL (v6.0.1)71–73. To create the files with Freesurfer-based metrics across all participants, we used Freesurfer version 6.0.096. Physiological nuisance regressors (RETROICOR and HRV/RVT regressors) were estimated using the TAPAS PhysIO Matlab package (v3.2.0)75.\nFirst-level functional MRI analyses for technical validation were implemented using the Python package nistats (v0.0.1b2)59 and nilearn (v0.6.2)59,97. For the inter-subject correlation analysis the Brain Imaging Analysis Kit was used (BrainIAK, http://brainiak.org, v0.10; RRID:SCR_014824)92. Plotting brain images was done using FSLeyes (v0.32)98 and plotting statistical plots was done using the Python packages seaborn99 and Matplotlib100.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33741990", "locator": "sections[64] \"Code availability\" paragraphs 1-5", "quote": "All code used for curating, annotating, and (pre)processing AOMIC are version-controlled using git and can be found in project-specific Github repositories within the NILAB-UvA Github organization: https://github.com/orgs/NILAB-UvA. Many pre and postprocessing steps were identical across datasets, so the code for these procedures is stored in a single repository: https://github.com/NILAB-UvA/AOMIC-common-scripts. Possible parameters are all hard-coded within the scripts, except for a single positional parameter pointing to the directory to be processed. For custom Python-based scripts, we used Python version 3.7. All code was developed on a Linux system with 56 CPUs (Intel Xeon E5-2680 v4, 2.40 GHz) and 126GB RAM running Ubuntu 16.04. All curation, preprocessing, and analyses were run on said Linux system, apart from the Fmriprep, MRIQC, and Freesurfer analyses, which were run in a Docker container provided by those software packages. Custom code was parallelized to run on multiple CPUs concurrently using the Python package joblib (https://joblib.readthedocs.io).\nFor curation, preprocessing, and analysis of the datasets, we used a combination of existing packages and custom scripts (written in Python or bash). To convert the data to the Brain Imaging Data Structure (BIDS), we used the in-house developed, publicly available software package bidsify (v0.3; https://github.com/NILAB-UvA/bidsify), which in turn uses the dcm2niix (v1.0.20181125)94 to convert the Philips PAR/REC files to compressed nifti files. In contrast to the data from PIOP1 and PIOP2 (which were converted to nifti using dcm2niix), r2aGUI (v2.7.0; http://r2agui.sourceforge.net/) was used to convert the data from ID1000. Because r2aGUI does not correct the gradient table of DWI scans for slice angulation, we used the angulation_correction_Achieva Matlab script (version December 29, 2007) from Jonathan Farrell to do so (available for posterity at https://github.com/NILAB-UvA/ID1000/blob/master/code/bidsify/DTI_gradient_table_ID1000.m). To remove facial characteristics from anatomical scans, we used the pydeface package (v.1.1.0)86. Finally, to convert the raw physiology files (i.e., Philips “SCANPHYSLOG” files) to BIDS, we used the in-house developed, publicly available Python package scanphyslog2bids (v0.1; https://github.com/lukassnoek/scanphyslog2bids). The outputs from the BIDS-conversion pipeline were checked using the bids-validator software package (v1.4.3).\nAnatomical and functional MRI preprocessing were done using Fmriprep (v1.4.1; see the Derivatives section for extensive information about Fmriprep’s preprocessing pipeline)42. For our DWI preprocessing pipeline, we used tools from the MRtrix3 package (www.mrtrix.org; v3.0_RC3)60 and FSL (v6.0.1)95. For the VBM and dual regression pipelines, we used FSL (v6.0.1)71–73. To create the files with Freesurfer-based metrics across all participants, we used Freesurfer version 6.0.096. Physiological nuisance regressors (RETROICOR and HRV/RVT regressors) were estimated using the TAPAS PhysIO Matlab package (v3.2.0)75.\nFirst-level functional MRI analyses for technical validation were implemented using the Python package nistats (v0.0.1b2)59 and nilearn (v0.6.2)59,97. For the inter-subject correlation analysis the Brain Imaging Analysis Kit was used (BrainIAK, http://brainiak.org, v0.10; RRID:SCR_014824)92. Plotting brain images was done using FSLeyes (v0.32)98 and plotting statistical plots was done using the Python packages seaborn99 and Matplotlib100."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33741990"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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"open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[58]/table-wrap[1]#Tab8", "version": null, "note": "Cross-correlations for the subscales of the BIS/BAS for the ID1000 sample. The pattern of correlations is consistent with that reported in literature.", "structure": {"pmcid": "PMC7979787", "section_index": 58, "section_title": "BIS/BAS", "row_count": 5, "column_count": 5}, "table": {"label": "Table 8", "caption": "Cross-correlations for the subscales of the BIS/BAS for the ID1000 sample. The pattern of correlations is consistent with that reported in literature.", "rows": [["BIS/BAS (N = 928)", "BAS drive", "BAS fun", "BAS reward", "BIS"], ["BAS drive", "1", "0.45**", "0.34**", "−0.19**"], ["BAS fun", "", "1", "0.39**", "−0.13**"], ["BAS reward", "", "", "1", "0.19**"], ["BIS", "", "", "", "1"]], "row_count": 5, "column_count": 5, "numeric_cells": 11, "header_rows": 1, "locator": "section[58]/table-wrap[1]#Tab8"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "meta_analysis", "specific": "random_effects", "label": "Meta-analysis / evidence synthesis", "dsl_operations": ["random_effects_pool"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["study_level_effect_estimates"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To do so, using the nistats Python package, we ran mass-univariate first-level GLS models (using an AR1 noise model) based on a design matrix including task-regressors based on the events convolved with a canonical HRF function as well as a discrete cosine basis set functioning as a high-pass filter of 128 seconds and six motion regressors and computed first-level contrast maps for each subject which were subsequently analyzed in a random effects group-level (intercept) model, resulting in whole-brain z-value maps."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["random_effects_pool"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_effects."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["All code used for curating, annotating, and (pre)processing AOMIC are version-controlled using git and can be found in project-specific Github repositories within the NILAB-UvA Github organization: https://github.com/orgs/NILAB-UvA. Many pre and postprocessing steps were identical across datasets, so the code for these procedures is stored in a single repository: https://github.com/NILAB-UvA/AOMIC-common-scripts. Possible parameters are all hard-coded within the scripts, except for a single positional parameter pointing to the directory to be processed. For custom Python-based scripts, we used Python version 3.7. All code was developed on a Linux system with 56 CPUs (Intel Xeon E5-2680 v4, 2.40 GHz) and 126GB RAM running Ubuntu 16.04. All curation, preprocessing, and analyses were run on said Linux system, apart from the Fmriprep, MRIQC, and Freesurfer analyses, which were run in a Docker container provided by those software packages. Custom code was parallelized to run on multiple CPUs concurrently using the Python package joblib (https://joblib.readthedocs.io).\nFor curation, preprocessing, and analysis of the datasets, we used a combination of existing packages and custom scripts (written in Python or bash). To convert the data to the Brain Imaging Data Structure (BIDS), we used the in-house developed, publicly available software package bidsify (v0.3; https://github.com/NILAB-UvA/bidsify), which in turn uses the dcm2niix (v1.0.20181125)94 to convert the Philips PAR/REC files to compressed nifti files. In contrast to the data from PIOP1 and PIOP2 (which were converted to nifti using dcm2niix), r2aGUI (v2.7.0; http://r2agui.sourceforge.net/) was used to convert the data from ID1000. Because r2aGUI does not correct the gradient table of DWI scans for slice angulation, we used the angulation_correction_Achieva Matlab script (version December 29, 2007) from Jonathan Farrell to do so (available for posterity at https://github.com/NILAB-UvA/ID1000/blob/master/code/bidsify/DTI_gradient_table_ID1000.m). To remove facial characteristics from anatomical scans, we used the pydeface package (v.1.1.0)86. Finally, to convert the raw physiology files (i.e., Philips “SCANPHYSLOG” files) to BIDS, we used the in-house developed, publicly available Python package scanphyslog2bids (v0.1; https://github.com/lukassnoek/scanphyslog2bids). The outputs from the BIDS-conversion pipeline were checked using the bids-validator software package (v1.4.3).\nAnatomical and functional MRI preprocessing were done using Fmriprep (v1.4.1; see the Derivatives section for extensive information about Fmriprep’s preprocessing pipeline)42. For our DWI preprocessing pipeline, we used tools from the MRtrix3 package (www.mrtrix.org; v3.0_RC3)60 and FSL (v6.0.1)95. For the VBM and dual regression pipelines, we used FSL (v6.0.1)71–73. To create the files with Freesurfer-based metrics across all participants, we used Freesurfer version 6.0.096. Physiological nuisance regressors (RETROICOR and HRV/RVT regressors) were estimated using the TAPAS PhysIO Matlab package (v3.2.0)75.\nFirst-level functional MRI analyses for technical validation were implemented using the Python package nistats (v0.0.1b2)59 and nilearn (v0.6.2)59,97. For the inter-subject correlation analysis the Brain Imaging Analysis Kit was used (BrainIAK, http://brainiak.org, v0.10; RRID:SCR_014824)92. 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