FitLins - Fitting Linear Models to BIDS Datasets

FitLins is a tool for estimating linear models, defined by the BIDS Model specification proposal, to BIDS-formatted datasets.

This software is in alpha stage, and should be considered unstable. Users are welcome to test the software, and open issues.

The CLI follows the BIDS-Apps convention:

Usage:

fitlins <bids_root> <out_dir> <analysis_level> [--model <model_name>]

See the output of fitlins --help for all valid options:

usage: fitlins [-h] [-v]
               [--participant-label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
               [-m MODEL] [-d DERIVATIVES [DERIVATIVES ...]]
               [--derivative-label DERIVATIVE_LABEL]
               [--space {MNI152NLin2009cAsym,}] [--force-index FORCE_INDEX]
               [--ignore IGNORE] [--desc-label DESC_LABEL] [-s TYPE:FWHM]
               [--n-cpus N_CPUS] [--debug] [-w WORK_DIR]
               bids_dir output_dir {run,session,participant,dataset}

FitLins: Workflows for Fitting Linear models to fMRI

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 output path for the outcomes of preprocessing and visual reports
  {run,session,participant,dataset}
                        processing stage to be run (see BIDS-Apps specification).

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit

Options for filtering BIDS queries:
  --participant-label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
                        one or more participant identifiers (the sub- prefix can be removed)
  -m MODEL, --model MODEL
                        location of BIDS model description
  -d DERIVATIVES [DERIVATIVES ...], --derivatives DERIVATIVES [DERIVATIVES ...]
                        location of derivatives (including preprocessed images).
                        If none specified, indexes all derivatives under bids_dir/derivatives.
  --derivative-label DERIVATIVE_LABEL
                        execution label to append to derivative directory name
  --space {MNI152NLin2009cAsym,}
                        registered space of input datasets. Empty value for no explicit space.
  --force-index FORCE_INDEX
                        regex pattern or string to include files
  --ignore IGNORE       regex pattern or string to ignore files
  --desc-label DESC_LABEL
                        use BOLD files with the provided description label

Options for preprocessing BOLD series:
  -s TYPE:FWHM, --smoothing TYPE:FWHM
                        Smooth BOLD series with FWHM mm kernel prior to fitting.
                        Valid types: iso (isotropic);
                        e.g., `--smothing iso:5` will use an isotropic 5mm FWHM kernel

Options to handle performance:
  --n-cpus N_CPUS       maximum number of threads across all processes
  --debug               run debug version of workflow

Other options:
  -w WORK_DIR, --work-dir WORK_DIR
                        path where intermediate results should be stored

At present, FitLins does not operate in subject-native space. It is developed against FMRIPREP-preprocessed datasets, but is intended to work with any dataset following the BIDS Derivatives draft specification.

Models

By default, FitLins will look for a model.json in the root of the BIDS directory. A simple example model for OpenFMRI dataset ds000030 is reproduced below:

{
    "name": "ds000030_bart",
    "description": "model for balloon analog risk task",
    "input": {
        "task": "bart"
    },
    "blocks": [
        {
            "level": "run",
            "transformations": [
                {
                    "name": "factor",
                    "input": [
                        "trial_type",
                        "action"
                    ]
                },
                {
                    "name": "and",
                    "input": [
                        "trial_type.BALOON",
                        "action.ACCEPT"
                    ],
                    "output": [
                        "accept"
                    ]
                },
                {
                    "name": "and",
                    "input": [
                        "trial_type.BALOON",
                        "action.EXPLODE"
                    ],
                    "output": [
                        "explode"
                    ]
                }
            ],
            "model": {
                "HRF_variables":[
                    "accept",
                    "explode"
                ],
                "variables": [
                    "accept",
                    "explode",
                    "FramewiseDisplacement",
                    "X",
                    "Y",
                    "Z",
                    "RotX",
                    "RotY",
                    "RotZ"
                ]
            },
            "contrasts": [
                {
                    "name": "accept_vs_explode",
                    "condition_list": [
                        "accept",
                        "explode"
                    ],
                    "weights": [1, -1],
                    "type": "T"
                }
            ]
        },
        {
            "level": "dataset",
            "model": {
                "variables": [
                    "accept_vs_explode"
                ]
            },
            "contrasts": [
                {
                    "name": "group_accept_vs_explode",
                    "condition_list":[
                        "accept_vs_explode"
                    ],
                    "weights": [1],
                    "type": "T"
                }
            ]
        }
    ]
}

Additional examples can be found in the models branch of the main FitLins repository.

Note

The BIDS Model specification is a draft standard, and some details may change over time.

Warning

FitLins is in Alpha-stage, and is not suitable for use as a library, as the internal organization may change substantially without deprecation periods. Similarly the outputs (or derivatives) are subject to change, as experience and user feedback prompt. The command-line interface outlined above should be fairly stable, however.