Representational Similarity Analysis

|unit_tests|_ |build_docs|_

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This is a Python package for performing representational similarity analysis (RSA) using MNE-Python <https://martinos.org/mne/stable/index.html>__ data structures. The RSA is computed using a “searchlight” approach.

Read more on RSA in the paper that introduced the technique:

Nikolaus Kriegeskorte, Marieke Mur and Peter Bandettini (2008). Representational similarity analysis - connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2(4). https://doi.org/10.3389/neuro.06.004.2008

.. image:: https://raw.githubusercontent.com/wmvanvliet/mne-rsa/main/doc/rsa.png

Installation

The package can be installed either through PIP:
pip install mne-rsa
or through conda using the conda-forge channel:
conda install -c conda-forge mne-rsa

Use cases

This is what the package can do for you:

• Compute RDMs on arbitrary data

• Compute RDMs in a searchlight across:

  • vertices/voxels and samples (source level)
  • sensors and samples (sensor level)
  • vertices/voxels only (source level)
  • sensors only (sensor level)
  • samples only (source and sensor level)

• Use cross-validated distance metrics when computing RDMs

• And of course: compute RSA between RDMs

Supported metrics for comparing RDMs:

• Spearman correlation (the default)
• Pearson correlation
• Kendall’s Tau-A
• Linear regression (when comparing multiple RDMs at once)
• Partial correlation (when comparing multiple RDMs at once)

Juicy bits of the API

.. code:: python

compute_rdm(data, metric='correlation', **kwargs)

rsa_stcs(stcs, rdm_model, src, spatial_radius=0.04, temporal_radius=0.1, stc_rdm_metric='correlation', stc_rdm_params=dict(), rsa_metric='spearman', y=None, n_folds=1, sel_vertices=None, tmin=None, tmax=None, n_jobs=1, verbose=False)

rsa_evokeds(evokeds, rdm_model, noise_cov=None, spatial_radius=0.04, temporal_radius=0.1, evoked_rdm_metric='correlation', evoked_rdm_params=dict(), rsa_metric='spearman', y=None, n_folds=1, picks=None, tmin=None, tmax=None, n_jobs=1, verbose=False)

rsa_epochs(epochs, rdm_model, noise_cov=None, spatial_radius=0.04, temporal_radius=0.1, epochs_rdm_metric='correlation', epochs_rdm_params=dict(), rsa_metric='spearman', y=None, n_folds=1, picks=None, tmin=None, tmax=None, n_jobs=1, verbose=False)

rsa_nifti(image, rdm_model, spatial_radius=0.01, image_rdm_metric='correlation', image_rdm_params=dict(), rsa_metric='spearman', y=None, n_folds=1, roi_mask=None, brain_mask=None, n_jobs=1, verbose=False)

Example usage

Basic example on the EEG “kiloword” data:

.. code:: python

import mne import rsa data_path = mne.datasets.kiloword.data_path(verbose=True) epochs = mne.read_epochs(data_path + '/kword_metadata-epo.fif')

Compute the model RDM using all word properties

rdm_model = rsa.compute_rdm(epochs.metadata.iloc[:, 1:].values) evoked_rsa = rsa.rsa_epochs(epochs, rdm_model, spatial_radius=0.04, temporal_radius=0.01, verbose=True)

Documentation

For quick guides on how to do specific things, see the examples <https://users.aalto.fi/~vanvlm1/mne-rsa/auto_examples/index.html>__.

Finally, there is the API reference <https://users.aalto.fi/~vanvlm1/mne-rsa/api.html>__ documentation.

Integration with other packages

I mainly wrote this package to perform RSA analysis on MEG data. Hence, integration functions with MNE-Python <https://mne.tools>__ are provided. There is also some integration with nipy <https://nipy.org>__ for fMRI.

Performance

This package aims to be fast and memory efficient. An important design feature is that under the hood, everything operates on generators. The searchlight routines produce a generator of RDMs which are consumed by a generator of RSA values. Parallel processing is also supported, so you can use all of your CPU cores.

Development

Here is how to set up the package as a developer:

.. code:: bash

git clone [email protected]:wmvanvliet/mne-rsa.git cd mne-rsa python setup.py develop --user

.. toctree:: :hidden:

Examples <auto_examples/index> API Reference