Promises and Pitfalls of Topological Data Analysis for Brain Connectivity Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00543460" target="_blank" >RIV/67985807:_____/21:00543460 - isvavai.cz</a>
Alternative codes found
RIV/00023752:_____/21:43920630 RIV/68407700:21230/21:00357134
Result on the web
<a href="http://dx.doi.org/10.1016/j.neuroimage.2021.118245" target="_blank" >http://dx.doi.org/10.1016/j.neuroimage.2021.118245</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neuroimage.2021.118245" target="_blank" >10.1016/j.neuroimage.2021.118245</a>
Alternative languages
Result language
angličtina
Original language name
Promises and Pitfalls of Topological Data Analysis for Brain Connectivity Analysis
Original language description
Developing sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two distinctive examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure. We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better, potentially due to topographical, rather than topological, specificity of the differences. In the easier task of seizure discrimination from scalp electroencephalography data, classification based on persistent homology features generally reached comparable performance to using raw connectivity, albeit with typically smaller accuracies obtained for the directed (effective) connectivity compared to the undirected (functional) connectivity. Specific applications for topological data analysis may open when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.
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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
Neuroimage
ISSN
1053-8119
e-ISSN
1095-9572
Volume of the periodical
238
Issue of the periodical within the volume
September 2021
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
118245
UT code for WoS article
000679378500004
EID of the result in the Scopus database
2-s2.0-85109174654