docker pull dpys/pynets:latest

[sudo] pip install pynets [--user]

# Install git-lfs
brew install git-lfs (macOS) or [sudo] apt-get install git-lfs (linux)
git lfs install --skip-repo

# Clone the repository and install
git clone https://github.com/dpys/pynets
cd PyNets
[sudo] python setup.py install [--user]

@CONFERENCE{
    title = {PyNets: A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning},
    author = {Pisner, D., Hammonds R.}
    publisher = {Poster session presented at: Annual Meeting of the Organization for Human Brain Mapping}
    url = {https://github.com/dPys/PyNets},
    year = {2020},
    month = {June}
}

usage: pynets_bids [-h]
                   [--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
                   [--session_label SESSION_LABEL [SESSION_LABEL ...]]
                   [--push_location PUSH_LOCATION]
                   [-ua Path to parcellation file in MNI-space [Path to parcellation file in MNI-space ...]]
                   [-cm Cluster mask [Cluster mask ...]]
                   [-roi Path to binarized Region-of-Interest ROI) Nifti1Image [Path to binarized Region-of-Interest (ROI Nifti1Image ...]]
                   [-ref Atlas reference file path]
                   [-way Path to binarized Nifti1Image to constrain tractography [Path to binarized Nifti1Image to constrain tractography ...]]
                   [-config Optional path to a config.json file with runtime settings.]
                   [-pm Cores,memory] [-plug Scheduler type] [-v]
                   [-work Working directory]
                   bids_dir output_dir {participant,group} {dwi,func}
                   [{dwi,func} ...]
pynets_bids: the following arguments are required: bids_dir, output_dir, analysis_level, modality

{
    "dwi": {
            "dg": "['prob', 'det']",  # Indicates the direction-getting method(s) of tractography.
            "ml": "['0', '10', '40']",  # Indicates the minimum streamline length(s) for tractographic filtering.
            "mod": "['csd', 'csa', 'sfm']"  # Indicates the type(s) of diffusion model estimators for fixel reconstruction. At least 1 is required for structural connectometry.
        },
    "func": {
            "ct": "['rena', 'ward', 'kmeans']", # Indicates the type(s) of clustering that will be used to generate a clustering-based parcellation. This should be left as "None" if no clustering will be performed, but can be included simultaneously with the `-a` and `-ua` parcellation options.
            "k": "['200', '400', '600']", # Indicates the number of clusters to generate in a clustering-based parcellation. This should be left as "None" if no clustering will be performed.
            "hp": "['0', '0.028', '0.080']", # Indicates the high-pass frequenc(ies) to apply to signal extraction from nodes.
            "mod": "['partcorr', 'sps']", # Indicates the functional connectivity estimator(s) to use. At least 1 is required for functional connectometry.
            "sm": "['0', '2', '4']", # Indicates the smoothing FWHM value(s) to apply during the nodal time-series signal extraction.
            "es": "['mean', 'median']" # Indicates the method(s) of nodal time-series signal extraction.
        },
    "gen": {
            "a":  "DesikanKlein2012", # Anatomical atlases to define nodes.
            "bin":  "False", # Binarize the resulting connectome graph before analyzing it. Note that undirected weighted graphs are analyzed by default.
            "embed":  "False", # Activate omnibus and single-graph adjacency spectral embedding of connectome estimates sampled.
            "mplx":  0, # If both functional and structural data are provided, this parameter [0-3] indicates the type of multiplex connectome modeling to perform. See `pynets -h` for more details on multiplex modes.
            "n":  "['Cont', 'Default']", # Which, if any, Yeo-7/17 resting-state sub-networks to select from the given parcellation. If multiple are specified, all other options will iterate across each.
            "norm": "['6']", # Level of normalization to apply to graph (e.g. standardize betwee 0-1, Pass-to-Ranks (PTR), log10).
            "spheres":  "False", # Use spheres as nodes (vs. parcel labels, the default).
            "ns":  "None", # If `spheres` is True, this indicates integer radius size(s) of spherical centroid nodes.
            "p":  "['1']", # Apply anti-fragmentation, largest connected-component subgraph selection, or any of a variety of hub-detection methods to graph(s).
            "plt":  "False", # Activate plotting (adjancency matrix and glass-brain included by default).
            "thr":  1.0, # A threshold (0.0-1.0). This can be left as "None" if multi-thresholding is used.
            "max_thr":  0.80, # If performing multi-thresholding, a minimum threshold.
            "min_thr":  0.20, # If performing multi-thresholding, a maximum threshold.
            "step_thr":  0.10, # If performing multi-thresholding, a threshold interval size.
            "dt":  "False", # Global thresholding to achieve a target density. (Only one of `mst`, `dt`, and `df` can be used).
            "mst":  "True", # Local thresholding using the Minimum-Spanning Tree approach. (Only one of `mst`, `dt`, and `df` can be used).
            "df":  "False", # Local thresholding using a disparity filter. (Only one of `mst`, `dt`, and `df` can be used).
            "vox":  "'2mm'" # Voxel size (1mm or 2mm). 2mm is the default.
        }
}