NeuroRA

NeuroRA performs representational analysis across multi-modal neural datasets to quantify and compare brain representations.

Key Features:

• Cross-Modal Data Analysis: Integrates and compares data from EEG, MEG, fNIRS, sEEG, ECoG, neuroelectrophysiology, fMRI, behavioral datasets, and computer-simulated data.
• Representational Similarity Analysis (RSA): Implements RSA within an MVPA framework to assess similarity and dissimilarity among brain representations across conditions.
• Representational Dissimilarity Matrices (RDMs): Calculates RDMs and supports quantitative comparisons among RDMs.
• Pattern Analyses (NPS, STPS, ISC): Provides Neural Pattern Similarity (NPS), Spatiotemporal Pattern Similarity (STPS), and Inter-Subject Correlation (ISC) analyses to explore within- and across-subject patterns.
• Statistical Analysis and Visualization: Includes functions for statistical evaluation of representational measures and for visualizing results.

Scientific Applications:

• Comparative representational studies: Compare neural representations across modalities or species using RSA and RDM comparisons.
• Decoding neural information: Apply MVPA and RSA to decode condition-specific information from EEG, MEG, fMRI, and other recordings.
• Model-to-brain comparisons: Evaluate similarity between computer-simulated model representations and empirical neural or behavioral data via RDMs and RSA.
• Inter- and intra-subject analysis: Use NPS, STPS, and ISC to assess shared and individual patterns of neural representation across subjects and time.

Methodology:

Computational methods explicitly include calculation of representational dissimilarity matrices (RDMs), representational similarity analysis (RSA), Neural Pattern Similarity (NPS), Spatiotemporal Pattern Similarity (STPS), Inter-Subject Correlation (ISC), statistical evaluation, and visualization.