%X One fundamental question is what makes two brain states similar. For example, what makes the activity in visual cortex
elicited from viewing a robin similar to a sparrow? One common assumption in fMRI analysis is that neural similarity is
described by Pearson correlation. However, there are a host of other possibilities, including Minkowski and Mahalanobis
measures, with each differing in its mathematical, theoretical, and neural computational assumptions. Moreover, the operable
measures may vary across brain regions and tasks. Here, we evaluated which of several competing similarity measures best
captured neural similarity. Our technique uses a decoding approach to assess the information present in a brain region, and
the similarity measures that best correspond to the classifier’s confusion matrix are preferred. Across two published fMRI
datasets, we found the preferred neural similarity measures were common across brain regions but differed across tasks.
Moreover, Pearson correlation was consistently surpassed by alternatives.
%O This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
%I Springer Science and Business Media LLC
%L discovery10087977
%K Neural similarity · Neural coding · Machine learning · fMRI
%J Computational Brain & Behavior
%D 2019
%A S Bobadilla-Suarez
%A C Ahlheim
%A A Mehrotra
%A A Panos
%A BC Love
%T Measures of Neural Similarity