Simplicial and Topological Descriptions of Human Brain Dynamics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00544003" target="_blank" >RIV/67985807:_____/21:00544003 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1162/netn_a_00190" target="_blank" >http://dx.doi.org/10.1162/netn_a_00190</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1162/netn_a_00190" target="_blank" >10.1162/netn_a_00190</a>
Alternative languages
Result language
angličtina
Original language name
Simplicial and Topological Descriptions of Human Brain Dynamics
Original language description
While brain imaging tools like functional magnetic resonance imaging (fMRI) afford measurements of whole-brain activity, it remains unclear how best to interpret patterns found amid the data's apparent self-organization. To clarify how patterns of brain activity support brain function, one might identify metric spaces that optimally distinguish brain states across experimentally defined conditions. Therefore, the present study considers the relative capacities of several metric spaces to disambiguate experimentally defined brain states. One fundamental metric space interprets fMRI data topographically, that is, as the vector of amplitudes of a multivariate signal, changing with time. Another perspective compares the brain's functional connectivity, that is, the similarity matrix computed between signals from different brain regions. More recently, metric spaces that consider the data's topology have become available. Such methods treat data as a sample drawn from an abstract geometric object. To recover the structure of that object, topological data analysis detects features that are invariant under continuous deformations (such as coordinate rotation and nodal misalignment). Moreover, the methods explicitly consider features that persist across multiple geometric scales. While, certainly, there are strengths and weaknesses of each brain dynamics metric space, we find that those that track topological features optimally distinguish experimentally defined brain states.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Network Neuroscience
ISSN
2472-1751
e-ISSN
2472-1751
Volume of the periodical
5
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
549-568
UT code for WoS article
000663433500012
EID of the result in the Scopus database
2-s2.0-85108277345