Simplicial and Topological Descriptions of Human Brain Dynamics

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Simplicial and Topological Descriptions of Human Brain Dynamics

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Simplicial and Topological Descriptions of Human Brain Dynamics

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Network Neuroscience

ISSN

2472-1751

e-ISSN

2472-1751

Svazek periodika

5

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

549-568

Kód UT WoS článku

000663433500012

EID výsledku v databázi Scopus

2-s2.0-85108277345