Abnormal spectral and scale-free properties of resting-state EEG in girls with Rett syndrome.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
09 08 2023
Historique:
received: 14 11 2022
accepted: 25 07 2023
medline: 11 8 2023
pubmed: 10 8 2023
entrez: 9 8 2023
Statut: epublish

Résumé

Spontaneous EEG contains important information about neuronal network properties that is valuable for understanding different neurological and psychiatric conditions. Rett syndrome (RTT) is a rare neurodevelopmental disorder, caused by mutation in the MECP2 gene. RTT is characterized by severe motor impairments that prevent adequate assessment of cognitive functions. Here we probe EEG parameters obtained in no visual input condition from a 28-channels system in 23 patients with Rett Syndrome and 38 their typically developing peers aged 3-17 years old. Confirming previous results, RTT showed a fronto-central theta power (4-6.25 Hz) increase that correlates with a progression of the disease. Alpha power (6.75-11.75 Hz) across multiple regions was, on the contrary, decreased in RTT, also corresponding to general background slowing reported previously. Among novel results we found an increase in gamma power (31-39.5 Hz) across frontal, central and temporal electrodes, suggesting elevated excitation/inhibition ratio. Long-range temporal correlation measured by detrended fluctuation analysis within 6-13 Hz was also increased, pointing to a more predictable oscillation pattern in RTT. Overall measured EEG parameters allow to differentiate groups with high accuracy, ROC AUC value of 0.92 ± 0.08, indicating clinical relevance.

Identifiants

pubmed: 37558701
doi: 10.1038/s41598-023-39398-7
pii: 10.1038/s41598-023-39398-7
pmc: PMC10412611
doi:

Substances chimiques

Methyl-CpG-Binding Protein 2 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

12932

Informations de copyright

© 2023. Springer Nature Limited.

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Auteurs

Olga Sysoeva (O)

Center for Cognitive Sciences, Sirius University of Science and Technology, Sochi, Russia, 354340. olga.v.sysoeva@gmail.com.
Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova St. 5a, Moscow, Russia, 117485. olga.v.sysoeva@gmail.com.

Vladimir Maximenko (V)

Artificial Intelligence and Neurotechnology Lab, Privolzhsky Research Medical University, Nizhny Novgorod, Russia, 603950.
Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016.

Alexander Kuc (A)

Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016.

Victoria Voinova (V)

Veltischev Research and Clinical Institute for Pediatrics of the Pirogov, Russian National Research Medical University, Ministry of Health of Russian Federation, Moscow, Russia, 125412.
Mental Health Research Center, Moscow, Russia, 117152.

Olga Martynova (O)

Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova St. 5a, Moscow, Russia, 117485.

Alexander Hramov (A)

Artificial Intelligence and Neurotechnology Lab, Privolzhsky Research Medical University, Nizhny Novgorod, Russia, 603950.
Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, A. Nevskogo Str., Kaliningrad, Russia, 236016.

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Classifications MeSH