Topological View of Flows Inside the BOLD Spontaneous Activity of the Human Brain.

Betti Numbers brain activity fMRI video persistence bar code topological data analysis

Journal

Frontiers in computational neuroscience
ISSN: 1662-5188
Titre abrégé: Front Comput Neurosci
Pays: Switzerland
ID NLM: 101477956

Informations de publication

Date de publication:
2020
Historique:
received: 30 10 2019
accepted: 30 03 2020
entrez: 12 5 2020
pubmed: 12 5 2020
medline: 12 5 2020
Statut: epublish

Résumé

Spatio-temporal brain activities with variable delay detectable in resting-state functional magnetic resonance imaging (rs-fMRI) give rise to highly reproducible structures, termed cortical lag threads, that propagate from one brain region to another. Using a computational topology of data approach, we found that persistent, recurring blood oxygen level dependent (BOLD) signals in triangulated rs-fMRI videoframes display previously undetected topological findings, i.e., vortex structures that cover brain activated regions. Measure of persistence of vortex shapes in BOLD signal propagation is carried out in terms of Betti numbers that rise and fall over time during spontaneous activity of the brain. Importantly, a topology of data given in terms of geometric shapes of BOLD signal propagation offers a practical approach in coping with and sidestepping massive noise in neurodata, such as unwanted dark (low intensity) regions in the neighborhood of non-zero BOLD signals. Our findings have been codified and visualized in plots able to track the non-trivial BOLD signals that appear intermittently in a sequence of rs-fMRI videoframes. The end result of this tracking of changing lag structures is a so-called persistent barcode, which is a pictograph that offers a convenient visual means of exhibiting, comparing, and classifying brain activation patterns.

Identifiants

pubmed: 32390820
doi: 10.3389/fncom.2020.00034
pmc: PMC7189216
doi:

Types de publication

Journal Article

Langues

eng

Pagination

34

Informations de copyright

Copyright © 2020 Don, Peters, Ramanna and Tozzi.

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Auteurs

Arjuna P H Don (APH)

Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB, Canada.

James F Peters (JF)

Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB, Canada.

Sheela Ramanna (S)

Applied Computer Science, University of Winnipeg, Winnipeg, MB, Canada.

Arturo Tozzi (A)

Computational Intelligence Laboratory, University of Manitoba, Winnipeg, MB, Canada.
Department of Physics, University of North Texas, Denton, TX, United States.

Classifications MeSH