Further results on why a point process is effective for estimating correlation between brain regions
| Autor: | S. G. Horovitz, Sergio A. Cannas, Dante R. Chialvo, Mahdi Zarepour Nasir Abadi, Ignacio Cifre |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
DYNAMICS
Computer science POINT PROCESSES Materials Science (miscellaneous) Functional connectivity functional connectivity General Physics and Astronomy FUNCTIONAL CONNECTIVITY RESTING STATES purl.org/becyt/ford/1.3 [https] dynamics Point process lcsh:QC1-999 Correlation purl.org/becyt/ford/1 [https] TIME SERIES resting states lcsh:Q Statistical physics Physical and Theoretical Chemistry time series lcsh:Science point processes lcsh:Physics |
| Zdroj: | Papers in Physics, Vol 12 (2020) CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET |
| ISSN: | 1852-4249 |
| Popis: | Signals from brain functional magnetic resonance imaging (fMRI) can be efficiently represented by a sparse spatiotemporal point process, according to a recently introduced heuristic signal processing scheme. This approach has already been validated for relevant conditions, demonstrating that it preserves and compresses a surprisingly large fraction of the signal information. Here we investigated the conditions necessary for such an approach to succeed, as well as the underlying reasons, using real fMRI data and a simulated dataset. The results show that the key lies in the temporal correlation properties of the time series under consideration. It was found that signals with slowly decaying autocorrelations are particularly suitable for this type of compression, where inflection points contain most of the information. Fil: Cifre, I.. Universitat Ramon Llull; España Fil: Zarepour Nasir Abadi, Mahdi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Horovitz, S. G.. National Institutes of Health; Estados Unidos Fil: Cannas, Sergio Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina Fil: Chialvo, Dante Renato. Universidad Nacional de San Martín. Escuela de Ciencia y Tecnología; Argentina |
| Databáze: | OpenAIRE |
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