Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals

Combrisson, E., Nest, T., Brovelli, A., Ince, R. A.A. , Soto, J. L.P., Guillot, A. and Jerbi, K. (2020) Tensorpac: An open-source Python toolbox for tensor-based phase-amplitude coupling measurement in electrophysiological brain signals. PLoS Computational Biology, 16(10), e1008302. (doi: 10.1371/journal.pcbi.1008302)

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Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes. Current research in the field is not only hampered by the absence of a gold standard for PAC analysis, but also by the computational costs of running exhaustive computations on large and high-dimensional electrophysiological brain signals. In addition, various signal properties and analyses parameters can lead to spurious PAC. Here, we present Tensorpac, an open-source Python toolbox dedicated to PAC analysis of neurophysiological data. The advantages of Tensorpac include (1) higher computational efficiency thanks to software design that combines tensor computations and parallel computing, (2) the implementation of all most widely used PAC methods in one package, (3) the statistical analysis of PAC measures, and (4) extended PAC visualization capabilities. Tensorpac is distributed under a BSD-3-Clause license and can be launched on any operating system (Linux, OSX and Windows). It can be installed directly via pip or downloaded from Github (https://github.com/EtienneCmb/tensorpac). By making Tensorpac available, we aim to enhance the reproducibility and quality of PAC research, and provide open tools that will accelerate future method development in neuroscience.

Item Type:Articles
Additional Information:Funding: EC and AB were supported by the French National Agency (ANR-18-CE28-0016-01) (http://anr.fr). EC was also supported by funding via a Natural Sciences and Engineering Research Council of Canada (NSERC) (https://www.nserc-crsng.gc.ca). JLPS acknowledge support from the Brazilian Ministry of Education (CAPES grant 1719-04-1) and the Fulbright Commission to JLP Soto (https://www.iie.org/programs/capes). KJ was supported by funding from the Canada Research Chairs program and a Discovery Grant (RGPIN-2015-04854) from NSERC (Canada), a New Investigators Award from FQNT (2018-NC-206005) and an IVADO-Apogée fundamental research project grant (http://www.frqnt.gouv.qc.ca/). This research is also supported in part by the FRQNT Strategic Clusters Program (2020-RS4-265502 - Centre UNIQUE - Union Neurosciences and Artificial Intelligence - Quebec) (https://ivado.ca/). TC acknowledges support through the Centre de Recherches Mathématiques (CRM).
Glasgow Author(s) Enlighten ID:Ince, Dr Robin
Authors: Combrisson, E., Nest, T., Brovelli, A., Ince, R. A.A., Soto, J. L.P., Guillot, A., and Jerbi, K.
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
Journal Name:PLoS Computational Biology
ISSN (Online):1553-7358
Copyright Holders:Copyright © 2020 Combrisson et al.
First Published:First published in PLoS Computational Biology
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304240Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysisRobin InceWellcome Trust (WELLCOTR)214120/Z/18/ZNP - Centre for Cognitive Neuroimaging (CCNi)