Monitoring Effective Connectivity in the Preterm Brain: A Graph Approach to Study Maturation
| Autor: | Mario Lavanga, Anneleen Dereymaeker, Katrien Jansen, Alexander Caicedo, Gunnar Naulaers, S. Van Huffel, O De Wel |
|---|---|
| Přispěvatelé: | Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
General Computer Science Article Subject Age prediction Electroencephalography Biology Audiology Preterm brain lcsh:QA75.5-76.95 050105 experimental psychology Functional connectivity 03 medical and health sciences 0302 clinical medicine medicine 0501 psychology and cognitive sciences Clustering coefficient Pregnancy Increasing attention Multidisciplinary Anatomical connectivity SISTA medicine.diagnostic_test [SCCO.NEUR]Cognitive science/Neuroscience 05 social sciences Postmenstrual Age medicine.disease Graph (abstract data type) lcsh:Electronic computers. Computer science 030217 neurology & neurosurgery |
| Zdroj: | Complexity Complexity, Wiley, 2017, 2017, pp.1-13. ⟨10.1155/2017/9078541⟩ Repositorio EdocUR-U. Rosario Universidad del Rosario instacron:Universidad del Rosario Complexity, Vol 2017 (2017) |
| ISSN: | 1076-2787 1099-0526 |
| Popis: | In recent years, functional connectivity in the developmental science received increasing attention. Although it has been reported that the anatomical connectivity in the preterm brain develops dramatically during the last months of pregnancy, little is known about how functional and effective connectivity change with maturation. The present study investigated how effective connectivity in premature infants evolves. To assess it, we use EEG measurements and graph-theory methodologies. We recorded data from 25 preterm babies, who underwent long-EEG monitoring at least twice during their stay in the NICU. The recordings took place from 27 weeks postmenstrual age (PMA) until 42 weeks PMA. Results showed that the EEG-connectivity, assessed using graph-theory indices, moved from a small-world network to a random one, since the clustering coefficient increases and the path length decreases. This shift can be due to the development of the thalamocortical connections and long-range cortical connections. Based on the network indices, we developed different age-prediction models. The best result showed that it is possible to predict the age of the infant with a root mean-squared error (MSE) equal to 2.11 weeks. These results are similar to the ones reported in the literature for age prediction in preterm babies. |
| Databáze: | OpenAIRE |
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