NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data - Lu, Zitong; Ku, Yixuan (Frontiers in Neuroinformatics, 2020)
In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities, and even bridge data from different species. However, previous toolboxes have been made to fit for specific datasets. Here, we develop a novel and easy-to-use toolbox based on Python named NeuroRA for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g. EEG, MEG, fNIRS, ECoG, sEEG, neuroelectrophysiology, fMRI), behavioral data, and computer simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. By using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity between different conditions, but also conduct a representational analysis among different RDMs to achieve a cross-modal comparison. In addition, users can calculate neural pattern similarity, spatiotemporal pattern similarity (STPS) and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.