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Selection entropy: The information hidden within neuronal patterns

Fagerholm, Erik D; Dezhina, Zalina; Moran, Rosalyn J; Friston, Karl J; Turkheimer, Federico; Leech, Robert; (2023) Selection entropy: The information hidden within neuronal patterns. Physical Review Research , 5 (2) , Article 023197. 10.1103/PhysRevResearch.5.023197. Green open access

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Abstract

Boltzmann entropy is a measure of the hidden information contained within a system. In the context of neuroimaging, information can be hidden within the multiple brain states that cannot be distinguished within a single image. Here, we show that information can also be hidden within multiple indistinguishable selections of neuronal patterns between brain regions, as quantified by a novel metric that we term “selection entropy.” We show the ways in which selection entropy behaves in comparison with the Kullback-Leibler (KL) divergence (relative entropy). First, we use synthetic data sets to demonstrate that selection entropy is more sensitive to small changes in probability distributions compared with the KL divergence. Second, we show that selection entropy identifies a principal gradient between sensorimotor and transmodal brain regions more definitively than the KL divergence within resting-state functional magnetic resonance imaging time series. As such, we introduce selection entropy as an additional asset in the analysis of neuronal functional selectivity.

Type: Article
Title: Selection entropy: The information hidden within neuronal patterns
Open access status: An open access version is available from UCL Discovery
DOI: 10.1103/PhysRevResearch.5.023197
Publisher version: https://doi.org/10.1103/PhysRevResearch.5.023197
Language: English
Additional information: Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. (https://creativecommons.org/licenses/by/4.0) Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10173954
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