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2021 | OriginalPaper | Buchkapitel

Analysis of the Middle and Long Latency ERP Components in Schizophrenia

verfasst von : Miguel Rocha e Costa, Felipe Teixeira, João Paulo Teixeira

Erschienen in: Optimization, Learning Algorithms and Applications

Verlag: Springer International Publishing

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Abstract

Schizophrenia is a complex and disabling mental disorder estimated to affect 21 million people worldwide. Electroencephalography (EEG) has proven to be an excellent tool to improve and aid the current diagnosis of mental disorders such as schizophrenia. The illness is comprised of various disabilities associated with sensory processing and perception. In this work, the first 10−200 ms of brain activity after the self-generation via button presses (condition 1) and passive presentation (condition 2) of auditory stimuli was addressed. A time-domain analysis of the event-related potentials (ERPs), specifically the MLAEP, N1, and P2 components, was conducted on 49 schizophrenic patients (SZ) and 32 healthy controls (HC), provided by a public dataset. The amplitudes, latencies, and scalp distribution of the peaks were used to compare groups. Suppression, measured as the difference between both conditions’ neural activity, was also evaluated. With the exception of the N1 peak during condition (1), patients exhibited significantly reduced amplitudes in all waveforms analyzed in both conditions. The SZ group also demonstrated a peak delay in the MLAEP during condition (2) and a modestly earlier P2 peak during condition (1). Furthermore, patients exhibited less and more N1 and P2 suppression, respectively. Finally, the spatial distribution of activity in the scalp during the MLAEP peak in both conditions, N1 peak in condition (1) and N1 suppression differed considerably between groups. These findings and measurements will be used with the finality of developing an intelligent system capable of accurately diagnosing schizophrenia.

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Titel: Analysis of the Middle and Long Latency ERP Components in Schizophrenia
verfasst von: Miguel Rocha e Costa
Felipe Teixeira
João Paulo Teixeira
Verlag: Springer International Publishing
Buch: Optimization, Learning Algorithms and Applications
Print ISBN: 978-3-030-91884-2

Electronic ISBN: 978-3-030-91885-9

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-91885-9_35

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