A New Mutual Information Measure to Estimate Functional Connectivity: Preliminary Study
| Autor: | Marcelo A. Colominas, Nisrine Jrad, Mohamad El Sayed Hussein Jomaa, Patrick Van Bogaert, Anne Humeau-Heurtier |
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| Přispěvatelé: | Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers (UA), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET) |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Rest 0206 medical engineering 02 engineering and technology Electroencephalography Brain mapping 03 medical and health sciences 0302 clinical medicine medicine Humans Entropy (information theory) Child ComputingMilieux_MISCELLANEOUS Brain Mapping Quantitative Biology::Neurons and Cognition medicine.diagnostic_test business.industry Functional connectivity Brain Estimator Pattern recognition Mutual information Magnetic Resonance Imaging 020601 biomedical engineering Temporal resolution Artificial intelligence business Functional magnetic resonance imaging [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing 030217 neurology & neurosurgery |
| Zdroj: | EMBC 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Jul 2019, Berlin, Germany. pp.640-643, ⟨10.1109/EMBC.2019.8856659⟩ |
| DOI: | 10.1109/embc.2019.8856659 |
| Popis: | Functional Connectivity (FC) is a powerful tool to investigate brain networks both in rest and while performing tasks. Functional magnetic resonance imaging (fMRI) gave good spatial estimation of FC but lacked the temporal resolution. Electroencephalography (EEG) allows estimating FC with good temporal resolution. In this study we introduce a new method based on Mutual Information and Multivariate Improved Weighted Multi-scale Permutation Entropy to estimate FC of brain using EEG. We applied this method on resting-state EEG signals from healthy children. Using network measures of nodes and Wilcoxon signed-rank test, we identified the most important nodes in the estimated networks. Comparing the localization of those outstanding nodes with the regions involved in resting-state networks (RSNs) estimated from fMRI showed that our proposal is efficient in the identification of nodes belonging to RSNs and could be used as a general estimator for FC without having to band-pass the signals into frequency bands. |
| Databáze: | OpenAIRE |
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