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Measures of neural similarity

View ORCID ProfileS. Bobadilla-Suarez, View ORCID ProfileC. Ahlheim, View ORCID ProfileA. Mehrotra, A. Panos, View ORCID ProfileB. C. Love
doi: https://doi.org/10.1101/439893
S. Bobadilla-Suarez
aDepartment of Experimental Psychology, University College London, 26 Bedford Way, London, UK, WC1H 0AP
dThe Alan Turing Institute, 96 Euston Road, London, UK, NW1 2DB
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  • For correspondence: sebastian.suarez.12@ucl.ac.uk
C. Ahlheim
aDepartment of Experimental Psychology, University College London, 26 Bedford Way, London, UK, WC1H 0AP
dThe Alan Turing Institute, 96 Euston Road, London, UK, NW1 2DB
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A. Mehrotra
bDepartment of Geography, University College London, Gower Street, London, WC1E 6BT
dThe Alan Turing Institute, 96 Euston Road, London, UK, NW1 2DB
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A. Panos
cDepartment of Statistical Science, University College London, Gower Street, London, WC1E 6BT
dThe Alan Turing Institute, 96 Euston Road, London, UK, NW1 2DB
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B. C. Love
aDepartment of Experimental Psychology, University College London, 26 Bedford Way, London, UK, WC1H 0AP
dThe Alan Turing Institute, 96 Euston Road, London, UK, NW1 2DB
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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? A common assumption, such as in Representation Similarity Analysis of fMRI data, is that neural similarity is described by Pearson correlation. However, any number of other similarity measures could instead hold, including Minkowski and Mahalanobis measures. The choice of measure is laden with mathematical, theoretical, neural computational assumptions that impact data interpretation. Here, we evaluated which of several competing similarity measures best capture neural similarity. The technique uses a classifier to assess the information present in a brain region and the similarity measure that best corresponds to the classifier’s confusion matrix is 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 12, 2018.
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Measures of neural similarity
S. Bobadilla-Suarez, C. Ahlheim, A. Mehrotra, A. Panos, B. C. Love
bioRxiv 439893; doi: https://doi.org/10.1101/439893
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Measures of neural similarity
S. Bobadilla-Suarez, C. Ahlheim, A. Mehrotra, A. Panos, B. C. Love
bioRxiv 439893; doi: https://doi.org/10.1101/439893

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