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Parsing components of auditory predictive coding in schizophrenia using a roving standard mismatch negativity
Amanda McCleery1, Daniel H Mathalon2, Jonathan K Wynn1
1Semel Institute for Neuroscience and Human Behavior, UCLA,Los Angeles, CA,USA.
Background:
Mismatch negativity (MMN) is an event-related potential (ERP) component reflecting auditory predictive coding. Repeated standard tones evoke increasing positivity ('repetition positivity'; RP), reflecting strengthening of the standard's memory trace and the prediction it will recur. Likewise, deviant tones preceded by more standard repetitions evoke greater negativity ('deviant negativity'; DN), reflecting stronger prediction error signaling. These memory trace effects are also evident in MMN difference wave. Here, we assess group differences and test-retest reliability of these indices in schizophrenia patients (SZ) and healthy controls (HC).
Methods:
Electroencephalography was recorded twice, 2 weeks apart, from 43 SZ and 30 HC, during a roving standard paradigm. We examined ERPs to the third, eighth, and 33rd standards (RP), immediately subsequent deviants (DN), and the corresponding MMN. Memory trace effects were assessed by comparing amplitudes associated with the three standard repetition trains.
Results:
Compared with controls, SZ showed reduced MMNs and DNs, but normal RPs. Both groups showed memory trace effects for RP, MMN, and DN, with a trend for attenuated DNs in SZ. Intraclass correlations obtained via this paradigm indicated good-to-moderate reliabilities for overall MMN, DN and RP, but moderate to poor reliabilities for components associated with short, intermediate, and long standard trains, and poor reliability of their memory trace effects.
Conclusion:
MMN deficits in SZ reflected attenuated prediction error signaling (DN), with relatively intact predictive code formation (RP) and memory trace effects. This roving standard MMN paradigm requires additional development/validation to obtain suitable levels of reliability for use in clinical trials.
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