NeuroRA
A Python Toolbox for Multimode Neural Data Representation Analysis - A Representational Analysis Toolbox for Neuroscience, including Neural Pattern Similarity (NPS), Representational Similarity Analysis (RSA), Spatiotemporal Pattern Similarity (STPS) & Inter-Subject Correlation (ISC)
NeuroRA
A Python Toolbox of Representational Analysis from Multimodal Neural Data
Overview
Representational Similarity Analysis (RSA) has become a popular and effective method to measure the representation of multivariable neural activity in different modes.
NeuroRA is an easy-to-use toolbox based on Python, which can do some works about RSA among nearly all kinds of neural data, including behavioral, EEG, MEG, fNIRS, sEEG, ECoG, fMRI and some other neuroelectrophysiological data. In addition, users can do Neural Pattern Similarity (NPS), Spatiotemporal Pattern Similarity (STPS), Inter-Subject Correlation (ISC) & Classification-based EEG Decoding on NeuroRA.
Paper
Lu, Z., & Ku, Y. (2020). NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. Frontiers in Neuroinformatics. 14:563669. doi: 10.3389/fninf.2020.563669
Installation
pip install neurora
Documentation
You can read the Documentation here or download the Tutorial here.
Required Dependencies:
- Numpy: a fundamental package for scientific computing
- SciPy: a package that provides many user-friendly and efficient numerical routines
- Scikit-learn: a Python module for machine learning
- Matplotlib: a Python 2D plotting library
- NiBabel: a package prividing read +/- write access to some common medical and neuroimaging file formats
- Nilearn: a Python module for fast and easy statistical learning on NeuroImaging data
- MNE-Python: a Python software for exploring, visualizing, and analyzing human neurophysiological data
Features
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Calculate the Neural Pattern Similarity (NPS)
for each subject / for each time-point / searchlight / for ROI
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Calculate the Spatiotemporal Neural Pattern Similarity (STPS)
for each subject / searchlight / for ROI
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Calculate the Inter-Subject Correlation (ISC)
for each time-point / searchlight / for ROI
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Calculate the Representational Dissimilarity Matrix (RDM)
for each subject / for each channel / for each time-point / searchlight / for ROI / all in
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Calculate the Representational Similarity based on RDMs
for each subject / for each channel / for each time-point / searchlight / for ROI / all in
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One-Step Realize Representational Similarity Analysis (RSA)
for each subject / for each channel / for each time-point / searchlight / for ROI / all in
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Conduct Statistical Analysis
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Save the RSA result as a NIfTI file for fMRI
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Plot the results
Typical schematic diagrams
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Script Demos to Know How to Use
There are two demos in Tutorial to let you know how to use NeuroRA to conduct representational analysis.
| Run the Demo | View the Demo | |
|---|---|---|
| Demo 1 |  |
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| Demo 2 | |
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| Demo 3 | |
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Demo 1 for EEG/MEG, based on visual-92-categories-task MEG dataset, includes 8 sections.
Section 1: Loading example data
Section 2: Preprocessing
Section 3: Calculating the neural pattern similarity
Section 4: Calculating single RDM and Plotting
Section 5: Calculating RDMs and Plotting
Section 6: Calculating the Similarity between two RDMs
Section 7: Calculating the Similarity and Plotting
Section 8: Calculating the RDMs for each channels
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Demo 2 for fMRI, based on Haxby dataset, includes 8 sections.
Section 1: Loading example data
Section 2: Preprocessing
Section 3: Calculating the neural pattern similarity (for ROI)
Section 4: Calculating the neural pattern similarity (Searchlight)
Section 5: Calculating the RDM for ROI and Plotting
Section 6: Calculating the RDM by Searchlight and Plotting
Section 7: Calculating the representational similarities between a coding model and neural activities
Section 8: Saving the RSA result and Plotting
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Demo 3 for comparing classification-based decoding and RSA.
Section 1: Downloading the data
Section 2: Classification-based Decoding
Section 3: Plotting the classification-based decoding results
Section 4: RSA-based Decoding
Section 5: Plotting the RSA-based decoding results
Users can see more details from Demo Codes.
About NeuroRA
Noteworthily, this toolbox is currently only a test version. If you have any question, find some bugs or have some useful suggestions while using, you can email me and I will be happy and thankful to know.
My email address: zitonglu1996@gmail.com
My personal homepage: https://zitonglu1996.github.io