yguo29/neurora

A Python Toolbox for Multimode Neural Data Representation Analysis

A Python Toolbox of Representational Analysis from Multimode 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, ECoG, electrophysiological and fMRI data. In addition, users can do Neural Pattern Similarity (NPS), Spatiotemporal Pattern Similarity (STPS) & Inter-Subject Correlation (ISC) on NeuroRA.

Installation

pip install neurora

Website & How to use

See more details at the NeuroRA website.

You can read or download the Tutorial here to know how to use NeuroRA.

Required Dependencies:

• Numpy: a fundamental package for scientific computing
• 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

• Calculate the Neural Pattern Similarity (NPS)

• Calculate the Spatiotemporal Neural Pattern Similarity (STPS)

• Calculate the Inter-Subject Correlation (ISC)

• Calculate the Representational Dissimilarity Matrix (RDM)

• Calculate the Representational Similarity based on RDMs

• One-Step Realize Representational Similarity Analysis (RSA)

• Statistical Analysis

• Save the RSA result as a NIfTI file for fMRI

• Visualization for RSA results

Paper

Lu, Z., & Ku, Y. NeuroRA: A Python toolbox of representational analysis from multi-modal neural data. (bioRxiv: https://doi.org/10.1101/2020.03.25.008086)

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: [email protected] / [email protected]

My personal homepage: https://zitonglu1996.github.io