Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia

Romain Aubonnet; Mahmoud Hassan; Paolo Gargiulo; Stefano Seri; Giorgio Di Lorenzo

doi:10.1109/MetroXRAINE66377.2025.11340067

Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia

Romain Aubonnet
, Mahmoud Hassan
, Paolo Gargiulo
, Stefano Seri
, Giorgio Di Lorenzo

College of Health and Life Sciences, Institute of Health and Neurodevelopment Aston University,Birmingham,United Kingdom
Laboratory of Psychophysiology and Cognitive Neuroscience, Department of Systems Medicine, University of Rome Tor Vergata, 00133 Rome, Italy; IRCCS—Fondazione Santa Lucia, 00179 Rome, Italy
Institute of Biomedical and Neural Engineering Reykjavik University Reykjavik,Rennes,France
Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

Schizophrenia is a complex and severe mental illness that remains challenging to characterize. This study investigates dynamic functional source connectivity in the alpha band from resting-state EEG in individuals experiencing the first episode of schizophrenia and matched controls. Cortical sources were estimated from EEG data, and static and dynamic functional connectivity were computed in the alpha band. The dynamic connectivity matrices were clustered to identify brain network states, from which temporal, power, and graph theory features were obtained for each subject. Statistical analysis showed no differences between patients and controls but identified significant correlations between metrics and cognitive and pathopsychological scores. These findings highlight the potential of dynamic approaches in providing a complementary set of features in characterizing schizophrenia.

Original language	English
Title of host publication	2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
Publisher	IEEE
Pages	1170-1175
Number of pages	6
ISBN (Electronic)	9798331502799
DOIs	https://doi.org/10.1109/MetroXRAINE66377.2025.11340067
Publication status	Published - 23 Jan 2026
Event	2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) - Ancona, Italy Duration: 22 Oct 2025 → 24 Oct 2025

Conference

Conference	2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
Period	22/10/25 → 24/10/25

Keywords

resting-state EEG
dynamic source connectivity
brain network states
alpha band
first episode of schizophrenia

Access to Document

10.1109/MetroXRAINE66377.2025.11340067

Cite this

Aubonnet, R., Hassan, M., Gargiulo, P., Seri, S., & Di Lorenzo, G. (2026). Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia. In 2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 1170-1175). IEEE. https://doi.org/10.1109/MetroXRAINE66377.2025.11340067

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title = "Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia",

abstract = "Schizophrenia is a complex and severe mental illness that remains challenging to characterize. This study investigates dynamic functional source connectivity in the alpha band from resting-state EEG in individuals experiencing the first episode of schizophrenia and matched controls. Cortical sources were estimated from EEG data, and static and dynamic functional connectivity were computed in the alpha band. The dynamic connectivity matrices were clustered to identify brain network states, from which temporal, power, and graph theory features were obtained for each subject. Statistical analysis showed no differences between patients and controls but identified significant correlations between metrics and cognitive and pathopsychological scores. These findings highlight the potential of dynamic approaches in providing a complementary set of features in characterizing schizophrenia.",

keywords = "resting-state EEG, dynamic source connectivity, brain network states, alpha band, first episode of schizophrenia",

author = "Romain Aubonnet and Mahmoud Hassan and Paolo Gargiulo and Stefano Seri and \{Di Lorenzo\}, Giorgio",

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doi = "10.1109/MetroXRAINE66377.2025.11340067",

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publisher = "IEEE",

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note = "2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) ; Conference date: 22-10-2025 Through 24-10-2025",

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Aubonnet, R, Hassan, M, Gargiulo, P, Seri, S & Di Lorenzo, G 2026, Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia. in 2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE, pp. 1170-1175, 2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 22/10/25. https://doi.org/10.1109/MetroXRAINE66377.2025.11340067

Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia. / Aubonnet, Romain; Hassan, Mahmoud; Gargiulo, Paolo et al.
2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE, 2026. p. 1170-1175.

Research output: Chapter in Book/Published conference output › Conference publication

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T1 - Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia

AU - Aubonnet, Romain

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AU - Seri, Stefano

AU - Di Lorenzo, Giorgio

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N2 - Schizophrenia is a complex and severe mental illness that remains challenging to characterize. This study investigates dynamic functional source connectivity in the alpha band from resting-state EEG in individuals experiencing the first episode of schizophrenia and matched controls. Cortical sources were estimated from EEG data, and static and dynamic functional connectivity were computed in the alpha band. The dynamic connectivity matrices were clustered to identify brain network states, from which temporal, power, and graph theory features were obtained for each subject. Statistical analysis showed no differences between patients and controls but identified significant correlations between metrics and cognitive and pathopsychological scores. These findings highlight the potential of dynamic approaches in providing a complementary set of features in characterizing schizophrenia.

AB - Schizophrenia is a complex and severe mental illness that remains challenging to characterize. This study investigates dynamic functional source connectivity in the alpha band from resting-state EEG in individuals experiencing the first episode of schizophrenia and matched controls. Cortical sources were estimated from EEG data, and static and dynamic functional connectivity were computed in the alpha band. The dynamic connectivity matrices were clustered to identify brain network states, from which temporal, power, and graph theory features were obtained for each subject. Statistical analysis showed no differences between patients and controls but identified significant correlations between metrics and cognitive and pathopsychological scores. These findings highlight the potential of dynamic approaches in providing a complementary set of features in characterizing schizophrenia.

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Aubonnet R, Hassan M, Gargiulo P, Seri S, Di Lorenzo G. Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia. In 2025 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE. 2026. p. 1170-1175 Epub 2025 Oct 22. doi: 10.1109/MetroXRAINE66377.2025.11340067

Alpha band EEG resting-state dynamic functional source connectivity in first-episode schizophrenia

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