Causality from phases of high-dimensional nonlinear systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00604108" target="_blank" >RIV/67985807:_____/25:00604108 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.ins.2024.121761" target="_blank" >https://doi.org/10.1016/j.ins.2024.121761</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ins.2024.121761" target="_blank" >10.1016/j.ins.2024.121761</a>
Alternative languages
Result language
angličtina
Original language name
Causality from phases of high-dimensional nonlinear systems
Original language description
Detecting causal relations in large dynamical systems is a difficult endeavor. As system dimension increases only linear approaches can be used, under the not always reasonable assumption of linear dynamics, since applications of nonlinear approaches are computationally intractable. Herein we test our recently developed information theory-based approach for causality detection from phases, appropriate for time series that exhibit well-behaved oscillatory patterns, and we expand our previous analysis to large systems with intricate connection patterns. Other than periodic-like behavior, this approach does not require any assumptions and works well for large-dimensional systems. We assess its performance on artificial data from networks of 3 or 10 coupled Rössler oscillators and networks of 3 coupled Mackey-Glass equations. We then employ it to study the dynamics of the human brain in two test-cases, one of emotional state change in healthy subjects and one of pathological system change in epilepsy. In the course of our study, we identify some very interesting phenomena related to synchronization, which can lead any causality measure to failure. We finally discuss the steps needed to properly investigate causal relations, so that even if some real connections cannot be detected, at least false connections would not be inferred.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GF21-14727K" target="_blank" >GF21-14727K: Learning Synchronization Patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Information Sciences
ISSN
0020-0255
e-ISSN
1872-6291
Volume of the periodical
697
Issue of the periodical within the volume
April 2025
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
19
Pages from-to
121761
UT code for WoS article
001409475600001
EID of the result in the Scopus database
2-s2.0-85212337184