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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Pattern component modeling: A flexible approach for understanding the representational structure of brain activity

Jörn Diedrichsen¹, Atsushi Yokoi², Spencer A Arbuckle³

¹Brain and Mind Institute, Western University, Canada; Department of Statistical and Actuarial Sciences, Western University, Canada; Department of Computer Science, Western University, Canada.

|August 28, 2017

Summary

Pattern Component Modeling (PCM) is a Bayesian approach to evaluate how neural activity patterns represent stimuli or thoughts. PCM offers a flexible way to analyze brain representations and estimate encoded feature spaces from data.

Keywords:

Bayesian models Motor representations Multi-voxel pattern analysis fMRI

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Representational models are crucial for understanding the relationship between neural activity and cognitive functions like perception, action, and thought.
Existing methods like encoding models directly fit voxel activity, which can be limiting for complex representational structures.

Purpose of the Study:

To review and present Pattern Component Modeling (PCM), a practical Bayesian framework for evaluating representational models of neural activity.
To demonstrate the flexibility of PCM in fitting complex representational models and estimating encoded feature spaces.
To provide practical examples and open-source code for applying PCM in neuroscience research.

Main Methods:

PCM evaluates representational models by predicting novel brain activity patterns, similar to encoding models.
Unlike encoding models, PCM integrates over possible voxel activity profiles, computing the marginal likelihood of the data.
Utilizes an analytical expression for marginal likelihood to enable fitting of flexible representational models with estimable feature space parameters.

Main Results:

PCM allows for the estimation of the relative strength and form of encoded feature spaces within representational models.
Demonstrates various ways to specify flexible representational models and compare models of differing complexity.
Presents practical applications in motor control, including fixed and non-linear models of brain representations.

Conclusions:

Pattern Component Modeling (PCM) provides a powerful and flexible Bayesian approach for analyzing neural representations.
The method facilitates the detailed characterization of how the brain encodes information, applicable to diverse cognitive domains.
Open-source software is available for implementing PCM, promoting wider adoption and advancement in the field.