Pattern Component Modelling (PCM) toolbox

The Pattern Component Modelling (PCM) toolbox is designed to analyze multivariate brain activity patterns in a Bayesian approach. The theory is laid out in Diedrichsen et al. (2017) as well as in this documentation. We provide details for model specification, model estimation, visualisation, and model comparison. The documentation also refers to the empricial examples in demos folder.

The original Matlab version of the toolbox is available at https://github.com/jdiedrichsen/pcm_toolbox. The practical examples in this documentation are available as jupyter notebooks at https://github.com/DiedrichsenLab/PcmPy/tree/master/docs/demos.

Note that the toolbox does not provide functions to extract the required data from the first-level GLM or raw data, or to run search-light or ROI analyses. We have omitted these function as they strongly depend on the analysis package used for the basic imaging analysis. Some useful tools for the extraction of multivariate data from first-level GLMs can be found in the RSA-toolbox (https://github.com/rsagroup/rsatoolbox) and of course nilearn.

Licence and Acknowledgements

The PCMPy toolbox is being developed by members of the Diedrichsenlab including Jörn Diedrichsen, Giacomo Ariani, Spencer Arbuckle, Eva Berlot, and Atsushi Yokoi. It is distributed under MIT License, meaning that it can be freely used and re-used, as long as proper attribution in form of acknowledgments and links (for online use) or citations (in publications) are given. When using, please cite the relevant references:

• Diedrichsen, J., Yokoi, A., & Arbuckle, S. A. (2018). Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns. Neuroimage. 180(Pt A), 119-133.

• Diedrichsen, J., Ridgway, G., Friston, K.J., Wiestler, T., (2011). Comparing the similarity and spatial structure of neural representations: A pattern-component model. Neuroimage.

Documentation

Contents:

• Installation
  • Dependencies
  • Installation using Pip
  • Installation for developers
• Introduction
• Models Specification
  • Statistical Model
  • Encoding- vs. RSA-style models
  • Model types
  • Noise Models
• Model Fitting
  • Fitting to individual data sets
  • Fitting to individual data sets with cross-validation across partitions
  • Fitting to group data sets
  • Fitting to group data sets with cross-validation across participants
  • Likelihood and Optimization
• Visualisation
  • Second Moment matrices
  • Multidimensional scaling
  • Plotting model evidence
• Inference
  • Inference on model parameters
  • Inference on model evidence
  • Group inference
  • Noise ceilings
  • Inference on model components: Model families
• Regularized regression
  • Practical guide
• Application examples
  • Finger representations
  • PCM correlation models
  • Component inference and model families
• Mathematical details
  • Likelihood
  • Restricted likelihood
  • First derivatives of the log-likelihood
  • First derivatives of the restricted log-likelihood
  • Derivates for specific parameters
  • Conjugate Gradient descent
  • Newton-Raphson algorithm
  • Choosing an optimisation algorithm
  • Acceleration of matrix inversion
• API reference
  • Dataset module
  • Model module
  • Inference module
  • Optimization module
  • Regression module
  • Matrix module
  • Util module
  • Simulation module
• References

Indices and tables

• Index

• Module Index

• Search Page