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D'Annunzio Chieti Pescara","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005874","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Topogr"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome.<\/jats:p><jats:p>The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics.<\/jats:p><jats:p>Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age.<\/jats:p><jats:p>The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes.<\/jats:p>","DOI":"10.1007\/s10548-023-01008-0","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T09:02:10Z","timestamp":1697101330000},"page":"461-474","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Microstate Analysis Reflects Maturation of the Preterm Brain"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0159-3381","authenticated-orcid":false,"given":"Tim","family":"Hermans","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1151-7526","authenticated-orcid":false,"given":"Mohammad","family":"Khazaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0850-2924","authenticated-orcid":false,"given":"Khadijeh","family":"Raeisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7279-3052","authenticated-orcid":false,"given":"Pierpaolo","family":"Croce","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriella","family":"Tamburro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anneleen","family":"Dereymaeker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3482-5145","authenticated-orcid":false,"given":"Maarten","family":"De Vos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4768-4687","authenticated-orcid":false,"given":"Filippo","family":"Zappasodi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8100-457X","authenticated-orcid":false,"given":"Silvia","family":"Comani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"1008_CR1","doi-asserted-by":"publisher","first-page":"016028","DOI":"10.1088\/1741-2552\/ab5469","volume":"17","author":"AH Ansari","year":"2020","unstructured":"Ansari AH, De Wel O, Pillay K, Dereymaeker A, Jansen K, Van Huffel S, Naulaers G, De Vos M (2020) A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants. 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