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Feasibility of topological data analysis for event-related fMRI

Recent fMRI research shows that perceptual and cognitive representations are instantiated in high-dimensional multivoxel patterns in the brain. However, the methods for detecting these representations are limited. Topological data analysis (TDA) is a new approach, based on the mathematical field of...

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Autores principales: Ellis, Cameron T., Lesnick, Michael, Henselman-Petrusek, Gregory, Keller, Bryn, Cohen, Jonathan D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6663178/
https://www.ncbi.nlm.nih.gov/pubmed/31410374
http://dx.doi.org/10.1162/netn_a_00095
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author Ellis, Cameron T.
Lesnick, Michael
Henselman-Petrusek, Gregory
Keller, Bryn
Cohen, Jonathan D.
author_facet Ellis, Cameron T.
Lesnick, Michael
Henselman-Petrusek, Gregory
Keller, Bryn
Cohen, Jonathan D.
author_sort Ellis, Cameron T.
collection PubMed
description Recent fMRI research shows that perceptual and cognitive representations are instantiated in high-dimensional multivoxel patterns in the brain. However, the methods for detecting these representations are limited. Topological data analysis (TDA) is a new approach, based on the mathematical field of topology, that can detect unique types of geometric features in patterns of data. Several recent studies have successfully applied TDA to study various forms of neural data; however, to our knowledge, TDA has not been successfully applied to data from event-related fMRI designs. Event-related fMRI is very common but limited in terms of the number of events that can be run within a practical time frame and the effect size that can be expected. Here, we investigate whether persistent homology—a popular TDA tool that identifies topological features in data and quantifies their robustness—can identify known signals given these constraints. We use fmrisim, a Python-based simulator of realistic fMRI data, to assess the plausibility of recovering a simple topological representation under a variety of conditions. Our results suggest that persistent homology can be used under certain circumstances to recover topological structure embedded in realistic fMRI data simulations.
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spelling pubmed-66631782019-08-13 Feasibility of topological data analysis for event-related fMRI Ellis, Cameron T. Lesnick, Michael Henselman-Petrusek, Gregory Keller, Bryn Cohen, Jonathan D. Netw Neurosci Research Articles Recent fMRI research shows that perceptual and cognitive representations are instantiated in high-dimensional multivoxel patterns in the brain. However, the methods for detecting these representations are limited. Topological data analysis (TDA) is a new approach, based on the mathematical field of topology, that can detect unique types of geometric features in patterns of data. Several recent studies have successfully applied TDA to study various forms of neural data; however, to our knowledge, TDA has not been successfully applied to data from event-related fMRI designs. Event-related fMRI is very common but limited in terms of the number of events that can be run within a practical time frame and the effect size that can be expected. Here, we investigate whether persistent homology—a popular TDA tool that identifies topological features in data and quantifies their robustness—can identify known signals given these constraints. We use fmrisim, a Python-based simulator of realistic fMRI data, to assess the plausibility of recovering a simple topological representation under a variety of conditions. Our results suggest that persistent homology can be used under certain circumstances to recover topological structure embedded in realistic fMRI data simulations. MIT Press 2019-07-01 /pmc/articles/PMC6663178/ /pubmed/31410374 http://dx.doi.org/10.1162/netn_a_00095 Text en © 2019 Massachusetts Institute of Technology This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.
spellingShingle Research Articles
Ellis, Cameron T.
Lesnick, Michael
Henselman-Petrusek, Gregory
Keller, Bryn
Cohen, Jonathan D.
Feasibility of topological data analysis for event-related fMRI
title Feasibility of topological data analysis for event-related fMRI
title_full Feasibility of topological data analysis for event-related fMRI
title_fullStr Feasibility of topological data analysis for event-related fMRI
title_full_unstemmed Feasibility of topological data analysis for event-related fMRI
title_short Feasibility of topological data analysis for event-related fMRI
title_sort feasibility of topological data analysis for event-related fmri
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6663178/
https://www.ncbi.nlm.nih.gov/pubmed/31410374
http://dx.doi.org/10.1162/netn_a_00095
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