The noise in our brain: A systematic review and meta-analysis of neuroimaging and signal-detection studies on source monitoring in psychosis

Damiani, Stefano; Cavicchioli, Marco; Guiot, Cecilia; Donadeo, Alberto; Scalabrini, Andrea; Grecuzzo, Valentina; Bergamaschini, Irma; Provenzani, Umberto; Politi, Pierluigi; Fusar-Poli, Paolo

doi:10.1016/j.jpsychires.2023.11.036

Journal of Psychiatric Research

2024

DOI: 10.1016/j.jpsychires.2023.11.036

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The noise in our brain: A systematic review and meta-analysis of neuroimaging and signal-detection studies on source monitoring in psychosis

Stefano Damiani,

Marco Cavicchioli,

Cecilia Guiot

et al.

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2024

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Cited by 2 publications

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Unraveling the brain mechanisms of source monitoring with non-invasive brain stimulation: A systematic review

Perret,

Neige,

Brunelin

et al. 2024

International Journal of Clinical and Health Psychology

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Unraveling the brain mechanisms of source monitoring with non-invasive brain stimulation: A systematic review

Perret,

Neige,

Brunelin

et al. 2024

International Journal of Clinical and Health Psychology

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Resting State EEG Analysis for Schizophrenia: from Alpha-Rhythm Reduction to Microstates Assessment

Fedotov,

Shustov

2024

Psychiatry

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Background: due to the emergence of new technologies for analyzing of EEG signal, many new researches in this field have appeared in recent years, including those investigating EEG parameters of schizophrenia. The aim: this publication provides an overview of actual studies on the possibilities of using the assessment of resting state EEG recordings in the diagnostics and prognosis of schizophrenia course. Material and methods: publications were selected in eLibrary, PubMed, Google Scholar and CNKI databases using the keywords: “psychosis”, “schizophrenia”, “EEG”, “resting state”. Methodologically, the atricle is a narrative literature review. Thirty-three sources were selected for analysis. Discussion and conclusion: according to the data available to present date qualitive and quantitative assessment of resting EEGs cannot be used for the instrumental diagnosis of schizophrenia because the most commonly detected increase in the proportion of slow-wave activity is seen in a several disorders. However, some quantitative spectral estimates of resting state EEG could be used to identify poor prognosis response to antipsychotic therapy, as well as for objective assessment of the dynamics of the mental state. Estimation of the power of slow resting EEG rhythms and other methods of assessing the connectivity of different neural networks could be considered as potential markers of the presence of a specific endophenotype. Modern digital technologies, including machine learning and artificial intelligence algorithms, make it possible to identify resting EEG of the schizophrenic patients from healthy controls with accuracy, sensitivity and specificity more than 95%. EEG microstates assessment, which can be used to assess the functioning of large neuronal ensembles, are one of the methods for detecting the endophenotype of schizophrenia.

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The noise in our brain: A systematic review and meta-analysis of neuroimaging and signal-detection studies on source monitoring in psychosis

Cited by 2 publications

References 47 publications

Unraveling the brain mechanisms of source monitoring with non-invasive brain stimulation: A systematic review

Unraveling the brain mechanisms of source monitoring with non-invasive brain stimulation: A systematic review

Resting State EEG Analysis for Schizophrenia: from Alpha-Rhythm Reduction to Microstates Assessment

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