Measures of Neural Similarity.

Journal: Computational brain & behavior
Published Date: Dec 2, 2019

Abstract

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.

Authors

  • S Bobadilla-Suarez
    Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP UK.
  • C Ahlheim
    Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP UK.
  • A Mehrotra
    Department of Geography, University College London, Gower Street, London, WC1E 6BT UK.
  • A Panos
    Department of Statistical Science, University College London, Gower Street, London, WC1E 6BT UK.
  • B C Love
    Department of Experimental Psychology, University College London, 26 Bedford Way, London, WC1H 0AP UK.

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