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Related Experiment Video

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Reliability of dissimilarity measures for multi-voxel pattern analysis.

Alexander Walther¹, Hamed Nili², Naveed Ejaz³

¹MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, Cambridge, United Kingdom; Institute of Cognitive Neuroscience, University College London, Alexandra House, 17 Queen Square, London WC1N 3AR, United Kingdom.

|December 29, 2015

Summary

Continuous measures like Mahalanobis distance are more reliable than classification accuracy for analyzing brain activation patterns in representational similarity analysis. Noise normalization further enhances reliability.

Keywords:

Classification Crossvalidation Decoding Linear discriminant Machine learning Multi-voxel pattern analysis Noise normalization Representational similarity analysis fMRI

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Area of Science:

Neuroscience
Cognitive Science
Brain Imaging

Background:

Representational similarity analysis (RSA) is crucial for understanding brain representations.
Common dissimilarity measures include correlation distance and classifier accuracy.
The relative reliability of these measures is not well understood.

Purpose of the Study:

To compare the reliability of different pattern dissimilarity measures in RSA.
To identify the most reliable method for characterizing representational geometries.

Main Methods:

Simulations and analysis of four functional magnetic resonance imaging (fMRI) datasets.
Comparison of classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance.
Evaluation of noise normalization and crossvalidation techniques.

Main Results:

Continuous dissimilarity measures (Euclidean/Mahalanobis, Pearson correlation) are more reliable than classification accuracy.
Classifier discretization and pattern ensemble shifts reduce reliability.
Multivariate noise normalization improves reliability for all measures.
Crossvalidated distances offer unbiased, ratio-scale estimates of dissimilarity.

Conclusions:

Crossvalidated Mahalanobis distance is superior to classification accuracy and correlation distance for RSA.
Reliable dissimilarity measures are essential for accurate characterization of representational geometries.
Findings guide the selection of optimal methods in brain representation research.