2018
DOI: 10.1177/1550059418761459
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Auditory Mismatch Negativity and P300a Elicited by the “Optimal” Multi-feature Paradigm in Early Schizophrenia

Abstract: The mismatch negativity (MMN) is an EEG-derived event-related potential (ERP) elicited by any violation of a predicted auditory "rule," regardless of whether one is attending to the stimuli and is thought to reflect updating of the stimulus context. Redirection of attention toward a rare, distracting stimulus event, however, can be measured by the subsequent P3a component of the P300. Chronic schizophrenia patients exhibit robust MMN deficits, as well as reductions in P3a amplitude. While, the substantial lite… Show more

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Cited by 13 publications
(7 citation statements)
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References 81 publications
(175 reference statements)
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“…A more recent study, however, found no significant MMN deficit to 800 µs ITD deviants in Sz when these were intermixed with other deviants in an optimal, multi-feature paradigm. However, the study also did not find deficits in other MMN types (38).…”
Section: Location Mmncontrasting
confidence: 59%
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“…A more recent study, however, found no significant MMN deficit to 800 µs ITD deviants in Sz when these were intermixed with other deviants in an optimal, multi-feature paradigm. However, the study also did not find deficits in other MMN types (38).…”
Section: Location Mmncontrasting
confidence: 59%
“…In Sz and CHR, the vast majority of studies have been performed using pitch and duration deviants, which are easiest to generate and manipulate parametrically, and which show subtle differences in terms of sensitivity to patient type (6,45). Here, we additionally evaluated integrity of location MMN generation, which has been studied in only a few prior Sz studies (37,38,40), and has not previously been investigated in CHR. As observed here (Table 2), location MMN has shorter onset latency than other deviant types, suggesting deviance detection even within subcortical components of the central auditory pathway.…”
Section: Discussionmentioning
confidence: 99%
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“…The computational feature behind deviance detection is currently thought to be foundation and trigger of higher order cognitive functions (Näätänen, Astikainen, Ruusuvirta, & Huotilainen, 2010) such as attention (Fritz, Elhilali, David, & Shamma, 2007; Sussman, Winkler, & Wang, 2003) and memory (Bartha-Doering, Deuster, Giordano, am Zehnhoff-Dinnesen, & Dobel, 2015; Ranganath & Rainer, 2003). Consequently, it is not surprising that MMN is not only disrupted in patients suffering from neurodevelopmental and psychiatric conditions, with a characteristically prominent reduction in schizophrenia (Baldeweg, Klugman, Gruzelier, & Hirsch, 2004; Damaso, Michie, & Todd, 2015; Ells et al., 2018; Fisher et al., 2018; Haigh et al., 2017; Javitt & Sweet, 2015; Joshi et al., 2018; Kantrowitz, Swerdlow, Dunn, & Vinogradov, 2018; Koshiyama et al., 2018; Näätänen & Kähkönen, 2009; Todd, Harms, Schall, & Michie, 2013), but also altered in other pathologies such as Parkinson’s disease (Brønnick, Nordby, Larsen, & Aarsland, 2010; Heldmann et al., 2017; Minks et al., 2014; Pekkonen, Jousmäki, Reinikainen, & Partanen, 1995; Seer, Lange, Georgiev, Jahanshahi, & Kopp, 2016; Solís-Vivanco et al., 2011), Alzheimer’s disease (Idrizbegovic, Hederstierna, & Rosenhall, 2016; Jiang et al., 2017; Papadaniil et al., 2016; Pekkonen, 2000; Pekkonen, Hirvonen, Jääskeläinen, Kaakkola, & Huttunen, 2001; Tsolaki et al., 2017), autism spectrum disorders (Goris et al., 2018; Hudac et al., 2018; Schwartz, Shinn-Cunningham, & Tager-Flusberg, 2018; Vlaskamp et al., 2017), and language impairments (Davids et al., 2011; Kujala & Leminen, 2017). Because of this, MMN has become a central tool in cognitive and clinical neuroscience (Bartha-Doering et al., 2015; Kujala, Tervaniemi, & Schröger, 2007; Näätänen, Paavilainen, Rinne, & Alho, 2007; Näätän...…”
Section: Introduction: Ssa and Mmn Two Faces Of Deviance Detectionmentioning
confidence: 99%
“…Additional factors such as symptomotology [17,22,26], social and occupational function (as discussed above) [11,12], gray matter deficits [13], or duration of illness [28,29] should be taken into account in addition to a reduced MMN waveform. P3a also holds promise as a biomarker for schizophrenia; several studies reported reduced amplitudes compared to controls [25,[57][58][59][60]. As with MMN, the type of deviant used appears to impact the reliability of finding a reduced P3a amplitude, and additional factors (such as symptomology and functionability) have influence and should be considered alongside waveform reduction in establishing a biomarker.…”
Section: Discussion/conclusionmentioning
confidence: 99%