Automatic Auditory Processing Deficits in Schizophrenia and Clinical High-Risk Patients: Forecasting Psychosis Risk with Mismatch Negativity

Abstract

Introduction Only about one third of patients at high risk for psychosis based on current clinical criteria convert to a psychotic disorder within a 2.5-year follow-up period. Targeting clinical high-risk (CHR) individuals for preventive interventions could expose many to unnecessary treatments, underscoring the need to enhance predictive accuracy with non-clinical measures. Candidate measures include event-related potential (ERP) components with established sensitivity to schizophrenia. Here we examined the mismatch negativity (MMN) component of the ERP elicited automatically by auditory deviance in CHR and early illness schizophrenia (ESZ) patients. We also examined whether MMN predicted subsequent conversion to psychosis in CHR patients. Method MMN to auditory deviants (duration, frequency, and duration+frequency “double deviant”) were assessed in 44 healthy controls (HC), 19 ESZ, and 38 CHR patients. Within CHR patients, 15 converters to psychosis were compared to 16 non-converters with at least 12 months of clinical follow-up. Hierarchical Cox regression examined the ability of MMN to predict time to psychosis onset in CHR patients. Results Irrespective of deviant type, MMN was significantly reduced in ESZ and CHR patients relative to HC, and in CHR converters relative to non-converters. MMN did not significantly differentiate ESZ and CHR patients. The duration+frequency double deviant MMN, but not the single deviant MMNs, significantly predicted the time to psychosis onset in CHR patients. Conclusions Neurophysiological mechanisms underlying automatic processing of auditory deviance, as reflected by the duration+frequency double deviant MMN, are compromised prior to psychosis onset, and can enhance the prediction of psychosis risk among CHR patients.

Mismatch negativity is a breakthrough biomarker for understanding and treating psychotic disorders

“…At baseline, converters had significantly smaller MMN amplitude, one comparable to that in earlyillness patients, whereas MMN in nonconverters was comparable to that of healthy age-matched controls. Perez et al (21) extended these findings to show that MMN amplitude also "forecasts" the time lag to psychosis onset in CHR individuals; those with more severe MMN abnormalities had shorter times to psychosis. These CHR and related studies (21,(23)(24)(25)(26) draw attention to the importance of identifying early biologic markers of disease vulnerability for predicting the development of psychosis and enhancing individualized risk-estimation/ prevention strategies (20).…”

“…Identifying biological markers in high-risk populations is a critical step toward informing the pathology of the disorder, predicting psychosis onset, and potentially devising early interventions to alter the course of the illness (20). As noted by Perez et al (21), however, only about one third of patients at high risk for psychosis, based on clinical criteria alone, develop a psychotic disorder within a 2.5-y follow-up period. Targeting CHR individuals for preventive interventions could expose many to unnecessary treatments (with their accompanying side effects), underscoring the need to enhance predictive accuracy with nonclinical measures.…”

Can new paradigms bring new perspectives for mismatch negativity studies in schizophrenia?

“…MMN is also a promising biomarker for conversion to psychosis in high-risk patients (Bodatsch et al 2011;Perez et al 2014;Higuchi et al 2014; for review see Nagai et al 2013b;Näätänen et al 2015). Although dysfunction in auditory deviance prediction has been reported in number of clinical populations (Näätänen et al 2012), there is also some evidence that MMN reduction is specific to schizophrenia (Baldeweg and Hirsch 2014;Umbricht et al 2003).…”

Neural mechanisms of mismatch negativity (MMN) dysfunction in schizophrenia

“…in [3][4][5][6][7]. Moreover, MMN deficits correlate highly with level of function in established 5,8 , first-episode 9 and prodromal 10,11 schizophrenia, suggesting that it indexes core pathophysiological mechanisms.…”

“…In ERSP analysis, electrophysiological activity is divided conventionally into discrete delta (.5-4 Hz), theta (4-7 Hz), alpha (7)(8)(9)(10)(11)(12), beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) and gamma (>24 Hz) bands, which reflect differential underlying local-circuit processes 20,[27][28][29] . Within these bands, stimulus-related activity is further differentiated into those that reflect alterations in phase reset mechanisms as reflected in intertrial coherence (ITC) vs. those that reflect alterations in single-trial power (e.g.…”