Information Integration in Large Brain Networks: A MATLAB Toolbox

Daniel Toker from the University of California, Berkeley provides a MATLAB toolbox for calculating integrated information in large brain networks from multivariate Gaussian data. The code implements spectral clustering, Queyranne, and Replica Exchange Markov Chain Monte Carlo algorithms. Instructions for use are included in a demo file.

Use Cases

• Calculate integrated information (Phi) for multivariate Gaussian brain data based on the described algorithms.
• Benchmark spectral clustering against Queyranne and REMCMC algorithms for information integration.
• Analyze large-scale brain network connectivity using the provided submodular function optimization toolbox.

Strengths

• Includes multiple established algorithms (spectral clustering, Queyranne, REMCMC) for calculating integrated information.
• Toolbox incorporates the Submodular Function Optimization toolbox for algorithm implementation.
• Provides a demo file with usage instructions for the toolbox.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Daniel Toker, University of California, Berkeley

Collection Method

Toolbox code implementation based on published algorithms.