Reduction of auditory event-related P300 amplitude in subjects with at-risk mental state for schizophrenia

2008 ◽  
Vol 105 (1-3) ◽  
pp. 272-278 ◽  
Author(s):  
Seza Özgürdal ◽  
Yehonala Gudlowski ◽  
Henning Witthaus ◽  
Wolfram Kawohl ◽  
Idun Uhl ◽  
...  
2021 ◽  
Vol 12 ◽  
Author(s):  
Andrea Perrottelli ◽  
Giulia Maria Giordano ◽  
Francesco Brando ◽  
Luigi Giuliani ◽  
Armida Mucci

Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia.Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis.Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands.Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.


NeuroImage ◽  
2011 ◽  
Vol 56 (3) ◽  
pp. 1531-1539 ◽  
Author(s):  
Louis-David Lord ◽  
Paul Allen ◽  
Paul Expert ◽  
Oliver Howes ◽  
Renaud Lambiotte ◽  
...  

2013 ◽  
Vol 8 (1) ◽  
pp. 82-86 ◽  
Author(s):  
Patrick Welsh ◽  
Sam Cartwright-Hatton ◽  
Adrian Wells ◽  
Libby Snow ◽  
Paul A. Tiffin

2007 ◽  
Vol 90 (1-3) ◽  
pp. 238-244 ◽  
Author(s):  
J LAPPIN ◽  
K MORGAN ◽  
L VALMAGGIA ◽  
M BROOME ◽  
J WOOLLEY ◽  
...  

2016 ◽  
Vol 26 ◽  
pp. S501 ◽  
Author(s):  
R.M. Gabernet ◽  
M. Tost ◽  
A. Gutiérrez-Zotes ◽  
V. Sánchez-Gistau ◽  
M. Solé ◽  
...  

2018 ◽  
Vol 192 ◽  
pp. 281-286 ◽  
Author(s):  
Noriyuki Ohmuro ◽  
Masahiro Katsura ◽  
Chika Obara ◽  
Tatsuo Kikuchi ◽  
Yumiko Hamaie ◽  
...  

2017 ◽  
Vol 43 (suppl_1) ◽  
pp. S164-S164
Author(s):  
Jessica Hartmann ◽  
Barnaby Nelson

2016 ◽  
Vol 174 (1-3) ◽  
pp. 24-28 ◽  
Author(s):  
Dorien H. Nieman ◽  
Sara Dragt ◽  
Esther D.A. van Duin ◽  
Nadine Denneman ◽  
Jozefien M. Overbeek ◽  
...  

2013 ◽  
Vol 43 (11) ◽  
pp. 2311-2325 ◽  
Author(s):  
L. R. Valmaggia ◽  
D. Stahl ◽  
A. R. Yung ◽  
B. Nelson ◽  
P. Fusar-Poli ◽  
...  

BackgroundMany research groups have attempted to predict which individuals with an at-risk mental state (ARMS) for psychosis will later develop a psychotic disorder. However, it is difficult to predict the course and outcome based on individual symptoms scores.MethodData from 318 ARMS individuals from two specialized services for ARMS subjects were analysed using latent class cluster analysis (LCCA). The score on the Comprehensive Assessment of At-Risk Mental States (CAARMS) was used to explore the number, size and symptom profiles of latent classes.ResultsLCCA produced four high-risk classes, censored after 2 years of follow-up: class 1 (mild) had the lowest transition risk (4.9%). Subjects in this group had the lowest scores on all the CAARMS items, they were younger, more likely to be students and had the highest Global Assessment of Functioning (GAF) score. Subjects in class 2 (moderate) had a transition risk of 10.9%, scored moderately on all CAARMS items and were more likely to be in employment. Those in class 3 (moderate–severe) had a transition risk of 11.4% and scored moderately severe on the CAARMS. Subjects in class 4 (severe) had the highest transition risk (41.2%), they scored highest on the CAARMS, had the lowest GAF score and were more likely to be unemployed. Overall, class 4 was best distinguished from the other classes on the alogia, avolition/apathy, anhedonia, social isolation and impaired role functioning.ConclusionsThe different classes of symptoms were associated with significant differences in the risk of transition at 2 years of follow-up. Symptomatic clustering predicts prognosis better than individual symptoms.


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