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Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs
Schizophrenia subjects often suffer from a failure to properly predict incoming inputs; most notably, some patients exhibit impaired prediction of the sensory consequences of their own actions. The mechanisms underlying this deficit remain unclear, though. One possible mechanism could consist in abe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931057/ https://www.ncbi.nlm.nih.gov/pubmed/35301329 http://dx.doi.org/10.1038/s41537-022-00239-7 |
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author | Karanikolaou, Maria Limanowski, Jakub Northoff, Georg |
author_facet | Karanikolaou, Maria Limanowski, Jakub Northoff, Georg |
author_sort | Karanikolaou, Maria |
collection | PubMed |
description | Schizophrenia subjects often suffer from a failure to properly predict incoming inputs; most notably, some patients exhibit impaired prediction of the sensory consequences of their own actions. The mechanisms underlying this deficit remain unclear, though. One possible mechanism could consist in aberrant predictive processing, as schizophrenic patients show relatively less attenuated neuronal activity to self-produced tones, than healthy controls. Here, we tested the hypothesis that this aberrant predictive mechanism would manifest itself in the temporal irregularity of neuronal signals. For that purpose, we here introduce an event-related potential (ERP) study model analysis that consists of an EEG real-time model equation, eeg(t) and a frequency Laplace transformed Transfer Function (TF) equation, eeg(s). Combining circuit analysis with control and cable theory, we estimate the temporal model representations of auditory ERPs to reveal neural mechanisms that make predictions about self-generated sensations. We use data from 49 schizophrenic patients (SZ) and 32 healthy control (HC) subjects in an auditory ‘prediction’ paradigm; i.e., who either pressed a button to deliver a sound tone (epoch a), or just heard the tone without button press (epoch b). Our results show significantly higher degrees of temporal irregularity or imprecision between different trials of the ERP from the Cz electrode (N100, P200) in SZ compared to HC (Levene’s test, p < 0.0001) as indexed by altered latency, lower similarity/correlation of single trial time courses (using dynamic time warping), and longer settling times to reach steady state in the intertrial interval. Using machine learning, SZ vs HC could be highly accurately classified (92%) based on the temporal parameters of their ERPs’ TF models, using as features the poles of the TF rational functions. Together, our findings show temporal irregularity or imprecision between single trials to be abnormally increased in SZ. This may indicate a general impairment of SZ, related to precisely predicting the sensory consequences of one’s actions. |
format | Online Article Text |
id | pubmed-8931057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89310572022-04-01 Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs Karanikolaou, Maria Limanowski, Jakub Northoff, Georg Schizophrenia (Heidelb) Article Schizophrenia subjects often suffer from a failure to properly predict incoming inputs; most notably, some patients exhibit impaired prediction of the sensory consequences of their own actions. The mechanisms underlying this deficit remain unclear, though. One possible mechanism could consist in aberrant predictive processing, as schizophrenic patients show relatively less attenuated neuronal activity to self-produced tones, than healthy controls. Here, we tested the hypothesis that this aberrant predictive mechanism would manifest itself in the temporal irregularity of neuronal signals. For that purpose, we here introduce an event-related potential (ERP) study model analysis that consists of an EEG real-time model equation, eeg(t) and a frequency Laplace transformed Transfer Function (TF) equation, eeg(s). Combining circuit analysis with control and cable theory, we estimate the temporal model representations of auditory ERPs to reveal neural mechanisms that make predictions about self-generated sensations. We use data from 49 schizophrenic patients (SZ) and 32 healthy control (HC) subjects in an auditory ‘prediction’ paradigm; i.e., who either pressed a button to deliver a sound tone (epoch a), or just heard the tone without button press (epoch b). Our results show significantly higher degrees of temporal irregularity or imprecision between different trials of the ERP from the Cz electrode (N100, P200) in SZ compared to HC (Levene’s test, p < 0.0001) as indexed by altered latency, lower similarity/correlation of single trial time courses (using dynamic time warping), and longer settling times to reach steady state in the intertrial interval. Using machine learning, SZ vs HC could be highly accurately classified (92%) based on the temporal parameters of their ERPs’ TF models, using as features the poles of the TF rational functions. Together, our findings show temporal irregularity or imprecision between single trials to be abnormally increased in SZ. This may indicate a general impairment of SZ, related to precisely predicting the sensory consequences of one’s actions. Nature Publishing Group UK 2022-03-17 /pmc/articles/PMC8931057/ /pubmed/35301329 http://dx.doi.org/10.1038/s41537-022-00239-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Karanikolaou, Maria Limanowski, Jakub Northoff, Georg Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs |
title | Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs |
title_full | Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs |
title_fullStr | Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs |
title_full_unstemmed | Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs |
title_short | Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs |
title_sort | does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of erps |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931057/ https://www.ncbi.nlm.nih.gov/pubmed/35301329 http://dx.doi.org/10.1038/s41537-022-00239-7 |
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