Running mriqc

Tip

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MRIQC is a BIDS-App [BIDSApps], and therefore it inherently understands the BIDS standard [BIDS] and follows the BIDS-Apps standard command line interface:

mriqc bids-root/ output-folder/ participant

That simple command runs MRIQC on all the T1w and BOLD images found under the BIDS-compliant folder bids-root/. The last participant keyword indicates that the first level analysis is run. (i.e. extracting the IQMs from the images retrieved within bids-root/). The second level (group) is automatically run if no particular subject is provided for analysis.

Note

If the argument --participant-label is not provided, then all subjects will be processed and the group level analysis will automatically be executed without need of running the command in item 3.

To specify one particular subject, the --participant-label argument can be used:

mriqc bids-root/ output-folder/ participant --participant-label S01 S02 S03

That command will run MRIQC only on the subjects indicated: only bids-root/sub-S01, bids-root/sub-S02, and bids-root/sub-S03 will be processed. In this case, the group level will not be triggered automatically. We generate the group level results (the group level report and the features CSV table) with:

mriqc bids-root/ output-folder/ group

Examples of the generated visual reports are found in The MRIQC Reports.

Warning

MRIQC by default attempts to upload anonymized quality metrics to a publicly accessible web server (mriqc.nimh.nih.gov). The uploaded data consists only of calculated quality metrics and scanning parameters. It removes all personal health information and participant identifiers. We try to collect this data to build normal distributions for improved outlier detection, but if you do not wish to participate you can disable the submission with the --no-sub flag.

BIDS data organization

The software automatically finds the data the input folder if it follows the BIDS standard [BIDS]. A fast and easy way to check that your dataset fulfills the BIDS standard is the BIDS validator.

BIDS-App levels

In the participant level, all individual images to be processed are run through the pipeline, and the MRIQC measures are extracted and the individual reports (see The MRIQC Reports) generated. In the group level, the IQMs extracted in first place are combined in a table and the group reports are generated.

Command line interface

MRIQC 22.0.6 Automated Quality Control and visual reports for Quality Assesment of structural (T1w, T2w) and functional MRI of the brain.

IMPORTANT: Anonymized quality metrics (IQMs) will be submitted to MRIQC’s metrics repository. Submission of IQMs can be disabled using the --no-sub argument. Please visit https://mriqc.readthedocs.io/en/latest/dsa.html to revise MRIQC’s Data Sharing Agreement.

usage: mriqc [-h] [--version] [-v] [--species {human,rat}]
             [--participant-label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]] [--session-id [SESSION_ID ...]]
             [--run-id [RUN_ID ...]] [--task-id [TASK_ID ...]] [-m [MODALITIES ...]] [--dsname DSNAME]
             [--bids-database-dir PATH] [--nprocs NPROCS] [--omp-nthreads OMP_NTHREADS] [--mem MEMORY_GB] [--testing]
             [-f] [--pdb] [-w WORK_DIR] [--verbose-reports] [--write-graph] [--dry-run] [--resource-monitor]
             [--use-plugin USE_PLUGIN] [--no-sub] [--email EMAIL] [--webapi-url WEBAPI_URL] [--webapi-port WEBAPI_PORT]
             [--upload-strict] [--ants-float] [--ants-settings ANTS_SETTINGS] [--fft-spikes-detector]
             [--fd_thres FD_THRES] [--deoblique] [--despike] [--start-idx START_IDX] [--stop-idx STOP_IDX]
             bids_dir output_dir {participant,group} [{participant,group} ...]

Positional Arguments

bids_dir

The root folder of a BIDS valid dataset (sub-XXXXX folders should be found at the top level in this folder).

output_dir

The directory where the output files should be stored. If you are running group level analysis this folder should be prepopulated with the results of the participant level analysis.

analysis_level

Possible choices: participant, group

Level of the analysis that will be performed. Multiple participant level analyses can be run independently (in parallel) using the same output_dir.

Named Arguments

--version

show program’s version number and exit

-v, --verbose

Increases log verbosity for each occurrence, debug level is -vvv.

--species

Possible choices: human, rat

Use appropriate template for population

Options for filtering BIDS queries

--participant-label, --participant_label, --participant-labels, --participant_labels

A space delimited list of participant identifiers or a single identifier (the sub- prefix can be removed).

--session-id

Filter input dataset by session ID.

--run-id

Filter input dataset by run ID (only integer run IDs are valid).

--task-id

Filter input dataset by task ID.

-m, --modalities

Filter input dataset by MRI type.

--dsname

A dataset name.

--bids-database-dir

Path to an existing PyBIDS database folder, for faster indexing (especially useful for large datasets).

Options to handle performance

--nprocs, --n_procs, --n_cpus, -n-cpus

Maximum number of simultaneously running parallel processes executed by MRIQC (e.g., several instances of ANTs’ registration). However, when --nprocs is greater or equal to the --omp-nthreads option, it also sets the maximum number of threads that simultaneously running processes may aggregate (meaning, with --nprocs 16 --omp-nthreads 8 a maximum of two 8-CPU-threaded processes will be running at a given time). Under this mode of operation, --nprocs sets the maximum number of processors that can be assigned work within an MRIQC job, which includes all the processors used by currently running single- and multi-threaded processes. If None, the number of CPUs available will be automatically assigned (which may not be what you want in, e.g., shared systems like a HPC cluster.

--omp-nthreads, --ants-nthreads

Maximum number of threads that multi-threaded processes executed by MRIQC (e.g., ANTs’ registration) can use. If None, the number of CPUs available will be automatically assigned (which may not be what you want in, e.g., shared systems like a HPC cluster.

--mem, --mem_gb, --mem-gb

Upper bound memory limit for MRIQC processes.

--testing

Use testing settings for a minimal footprint.

-f, --float32

Cast the input data to float32 if it’s represented in higher precision (saves space and improves perfomance).

--pdb

Open Python debugger (pdb) on exceptions.

Instrumental options

-w, --work-dir

Path where intermediate results should be stored.

--verbose-reports

--write-graph

Write workflow graph.

--dry-run

Do not run the workflow.

--resource-monitor, --profile

Hook up the resource profiler callback to nipype.

--use-plugin

Nipype plugin configuration file.

--no-sub

Turn off submission of anonymized quality metrics to MRIQC’s metrics repository.

--email

Email address to include with quality metric submission.

--webapi-url

IP address where the MRIQC WebAPI is listening.

--webapi-port

Port where the MRIQC WebAPI is listening.

--upload-strict

Upload will fail if upload is strict.

Specific settings for ANTs

--ants-float

Use float number precision on ANTs computations.

--ants-settings

Path to JSON file with settings for ANTs.

Functional MRI workflow configuration

--fft-spikes-detector

Turn on FFT based spike detector (slow).

--fd_thres

Threshold on framewise displacement estimates to detect outliers.

--deoblique

Deoblique the functional scans during head motion correction preprocessing.

--despike

Despike the functional scans during head motion correction preprocessing.

--start-idx

DEPRECATED Initial volume in functional timeseries that should be considered for preprocessing.

--stop-idx

DEPRECATED Final volume in functional timeseries that should be considered for preprocessing.

Running mriqc on HPC clusters

Singularity containers

Requesting resources

We have profiled cores and memory usages with the resource profiler tool of nipype.

An MRIQC run of one subject (from the ABIDE) dataset, containing only one run, one BOLD task (resting-state) yielded the following report:

Using the MultiProc plugin of nipype with nprocs=10, the workflow nodes run across the available processors for 41.68 minutes. A memory peak of 8GB is reached by the end of the runtime, when the plotting nodes are fired up.

We also profiled MRIQC on a dataset with 8 tasks (one run per task), on ds030 of OpenfMRI:

Again, we used n_procs=10. The software run for roughly about the same time (47.11 min). Most of the run time, memory usage keeps around a maximum of 10GB. Since we saw a memory consumption of 1-2GB during the the 1-task example, a rule of thumb may be that each task takes around 1GB of memory.

References

BIDS(1,2)

Brain Imaging Data Structure

BIDSApps

BIDS-Apps: portable neuroimaging pipelines that understand BIDS datasets

About the NiPreps framework licensing

Please check https://www.nipreps.org/community/licensing/ for detailed information on the criteria we use to license MRIQC and other projects of the framework.

License information

Copyright (c) 2021, the NiPreps Developers.

As of the 21.0.x pre-release and release series, MRIQC is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Copyright (c) 2015-2021, the MRIQC developers and the CRN. All rights reserved.

MRIQC 0.16.x series and earlier are licensed under the BSD 3-clause license. You may obtain a copy of the License at https://opensource.org/licenses/BSD-3-Clause

All trademarks referenced herein are property of their respective holders.

Notice

MRIQC Copyright 2021 The NiPreps Developers.

This product includes software developed by the NiPreps Community (https://nipreps.org/).

Portions of this software were developed at the Department of Psychology at Stanford University, Stanford, CA, US.

This software contains code ultimately derived from the PCP Quality Assessment Protocol (QAP; http://preprocessed-connectomes-project.github.io/quality-assessment-protocol) by C. Craddock, S. Giavasis, D. Clark, Z. Shezhad, and J. Pellman.

This software is also distributed as a Docker container image. The bootstraping file for the image (“Dockerfile”) is licensed under the MIT License.