Mismatch Negativity in Response to Auditory Deviance and Risk for Future Psychosis in Youth at Clinical High Risk for Psychosis

Holly K Hamilton; Brian J Roach; Peter M Bachman; Aysenil Belger; Ricardo E Carrión; Erica Duncan; Jason K Johannesen; Gregory A Light; Margaret A Niznikiewicz; Jean Addington; Carrie E Bearden; Kristin S Cadenhead; Barbara A Cornblatt; Thomas H McGlashan; Diana O Perkins; Ming T Tsuang; Elaine F Walker; Scott W Woods; Tyrone D Cannon; Daniel H Mathalon

doi:10.1001/jamapsychiatry.2022.1417

. 2022 Jun 8;79(8):780–789. doi: 10.1001/jamapsychiatry.2022.1417

Mismatch Negativity in Response to Auditory Deviance and Risk for Future Psychosis in Youth at Clinical High Risk for Psychosis

Holly K Hamilton ^1,², Brian J Roach ¹, Peter M Bachman ³, Aysenil Belger ⁴, Ricardo E Carrión ^5,^6,⁷, Erica Duncan ^8,⁹, Jason K Johannesen ¹⁰, Gregory A Light ^11,¹², Margaret A Niznikiewicz ^13,¹⁴, Jean Addington ¹⁵, Carrie E Bearden ^16,¹⁷, Kristin S Cadenhead ¹¹, Barbara A Cornblatt ^5,^6,^7,¹⁸, Thomas H McGlashan ¹⁰, Diana O Perkins ⁴, Ming T Tsuang ¹¹, Elaine F Walker ^9,¹⁹, Scott W Woods ¹⁰, Tyrone D Cannon ^10,²⁰, Daniel H Mathalon ^1,^2,^✉

¹San Francisco Veterans Affairs Health Care System, San Francisco, California

²Department of Psychiatry & Behavioral Sciences, University of California, San Francisco

³Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania

⁴Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill

⁵Division of Psychiatry Research, The Zucker Hillside Hospital, North Shore-Long Island Jewish Health System, Glen Oaks, New York

⁶Institute of Behavioral Science, Feinstein Institutes for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York

⁷Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York

⁸Atlanta Veterans Affairs Health Care System, Decatur, Georgia

⁹Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia

¹⁰Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut

¹¹Department of Psychiatry, University of California, San Diego, La Jolla

¹²Veterans Affairs San Diego Healthcare System, La Jolla, California

¹³Department of Psychiatry, Harvard Medical School at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Boston

¹⁴Veterans Affairs Boston Healthcare System, Brockton, Massachusetts

¹⁵Hotchkiss Brain Institute Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada

¹⁶Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles

¹⁷Department of Psychology, University of California, Los Angeles, Los Angeles

¹⁸Department of Molecular Medicine, Hofstra North Shore-LIJ School of Medicine, Hempstead, New York

¹⁹Department of Psychology, Emory University, Atlanta, Georgia

²⁰Department of Psychology, Yale University, School of Medicine, New Haven, Connecticut

Accepted for Publication: April 19, 2022.

Published Online: June 8, 2022. doi:10.1001/jamapsychiatry.2022.1417

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Corresponding Author: Daniel H. Mathalon, PhD, MD, University of California, San Francisco & San Francisco VA Health Care System, 4150 Clement St, 116D, San Francisco, CA 94121 (daniel.mathalon@ucsf.edu).

Correction: This article was corrected on August 1, 2022, to fix an error in Figure 3.

Author Contributions: Drs Hamilton and Mathalon had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Roach, Belger, Light, Addington, Bearden, Cadenhead, Perkins, Woods, Cannon, Mathalon.

Acquisition, analysis, or interpretation of data: Hamilton, Roach, Bachman, Belger, Carrion, Duncan, Johannesen, Light, Niznikiewicz, Addington, Bearden, Cadenhead, Perkins, Tsuang, Walker, Woods, Cannon, Mathalon.

Drafting of the manuscript: Hamilton, Roach, Belger, Mathalon.

Critical revision of the manuscript for important intellectual content: Hamilton, Roach, Bachman, Carrion, Duncan, Johannesen, Light, Niznikiewicz, Addington, Bearden, Cadenhead, Perkins, Tsuang, Walker, Woods, Cannon, Mathalon.

Statistical analysis: Hamilton, Roach, Carrion, Mathalon.

Obtained funding: Belger, Addington, Bearden, Cadenhead, Perkins, Walker, Cannon, Mathalon.

Administrative, technical, or material support: Hamilton, Roach, Bachman, Carrion, Light, Cadenhead, Mathalon.

Supervision: Roach, Bachman, Belger, Duncan, Niznikiewicz, Bearden, Cadenhead, Cannon, Mathalon.

Conflict of Interest Disclosures: Dr Duncan has received research support for work unrelated to this project from Posit Science Inc, Auspex Pharmaceuticals, and Teva Pharmaceuticals. Dr Johannesen reports grants from the National Institute of Mental Health and received salary support during the conduct of the study and also receives compensation by salary and stock shares from Sage Therapeutics outside the submitted work. Dr Light reports consulting for Astellas Pharma, NeuroSig, Neurocrine, Boehringer Ingelheim Pharmaceuticals, and Heptares Therapeutics outside the submitted work. Dr Addington reports grants from the University of Calgary during the conduct of the study. Dr Bearden reports grants from the National Institute of Mental Health during the conduct of the study and outside the submitted work and served on the scientific advisory board for One Mind outside the submitted work. Drs Cornblatt and McGlashan report grants from the National Institute of Mental Health during the conduct of the study. Dr Perkins is a consultant to Sunovion and Alkermes. Dr Woods reports grants from the National Institute of Mental Health during the conduct of the study and outside the submitted work; grants from Substance Abuse and Mental Health Services Administration, Boehringer Ingelheim, Amarex, Teva, and One Mind outside the submitted work; personal fees from Boehringer Ingelheim, Takeda, and New England Research Institute outside the submitted work; and has a patent for US 8492418 B2 for a method of treating the schizophrenia prodrome issued. Dr Cannon is a consultant to Boehringer Ingelheim Pharmaceuticals and Lundbeck A/S. Dr Mathalon reports grants from National Institute of Mental Health during the conduct of the study; consulting fees from Boehringer Ingelheim, Cadent Therapeutics, Gilgamesh Pharmaceuticals Inc, Recognify, Syndesi Therapeutics, Neurocrine, Greenwich Bioscience, and Aptinyx outside the submitted work. No other disclosures were reported.

Funding/Support: This work was supported by the National Institute of Mental Health (grants U01MH081902 to Dr Cannon, P50 MH066286 to Dr Bearden, U01MH081988 to Dr Walker, U01MH076989 to Dr Mathalon, U01MH081944 to Dr Cadenhead, U01MH081984 to Dr Addington, U01MH082004 to Dr Perkins, U01MH081857 to Dr Cornblatt, U01MH081928 to Dr Seidman, and U01MH082022 to Dr Woods) and a Career Development Award (grant 1K2CX001878) to Dr Hamilton from the US Department of Veterans Affairs Clinical Science Research and Development Service.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: Drs Hamilton, Mathalon, Duncan, Light, and Niznikiewicz are employees of the US government. The content is solely the responsibility of the authors and does not necessarily reflect the position or policy of the National Institutes of Health, the Department of Veterans Affairs, or the US government.

Additional Contributions: We thank Larry J. Seidman, PhD, who passed away before this manuscript was submitted, for his contributions to this work. We would also like to thank Cassandra Marzke, BA (University of California, San Francisco), and Claire Bertrand, BS (San Francisco Veterans Affairs Health Care System and University of California, San Francisco), for their assistance preparing the manuscript figures; compensation was not received outside of standard salary.

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Corresponding author.

PMCID: PMC9178501 PMID: 35675082

This case-control study examines whether mismatch negativity event-related potential amplitude, which is deficient in schizophrenia, is reduced in young people at clinical high risk for psychosis and associated with outcomes, accounting for effects of antipsychotic medication use.

Key Points

Question

Is mismatch negativity event–related potential amplitude associated with future clinical outcomes among young people at clinical high risk for psychosis?

Findings

In this multisite case-control study of 580 individuals at clinical high risk for psychosis and 241 healthy controls, greater deficits in baseline mismatch negativity amplitude were associated with psychosis conversion as well as the imminence of psychosis onset but only in at-risk individuals not taking antipsychotic medication at baseline.

Meaning

While the prognostic accuracy achieved by mismatch negativity was insufficiently large to support its use as a clinical test on its own, it may make a contribution to future multivariate multimodal prediction algorithms and provide pathophysiological insights for a subgroup of at-risk individuals.

Abstract

Importance

Although clinical criteria for identifying youth at risk for psychosis have been validated, they are not sufficiently accurate for predicting outcomes to inform major treatment decisions. The identification of biomarkers may improve outcome prediction among individuals at clinical high risk for psychosis (CHR-P).

Objective

To examine whether mismatch negativity (MMN) event–related potential amplitude, which is deficient in schizophrenia, is reduced in young people with the CHR-P syndrome and associated with outcomes, accounting for effects of antipsychotic medication use.

Design, Setting, and Participants

MMN data were collected as part of the multisite case-control North American Prodrome Longitudinal Study (NAPLS-2) from 8 university-based outpatient research programs. Baseline MMN data were collected from June 2009 through April 2013. Clinical outcomes were assessed throughout 24 months. Participants were individuals with the CHR-P syndrome and healthy controls with MMN data. Participants with the CHR-P syndrome who developed psychosis (ie, converters) were compared with those who did not develop psychosis (ie, nonconverters) who were followed up for 24 months. Analysis took place between December 2019 and December 2021.

Main Outcomes and Measures

Electroencephalography was recorded during a passive auditory oddball paradigm. MMN elicited by duration-, pitch-, and duration + pitch double-deviant tones was measured.

Results

The CHR-P group (n = 580; mean [SD] age, 19.24 [4.39] years) included 247 female individuals (42.6%) and the healthy control group (n = 241; mean age, 20.33 [4.74] years) included 114 female individuals (47.3%). In the CHR-P group, 450 (77.6%) were not taking antipsychotic medication at baseline. Baseline MMN amplitudes, irrespective of deviant type, were deficient in future CHR-P converters to psychosis (n = 77, unmedicated n = 54) compared with nonconverters (n = 238, unmedicated n = 190) in both the full sample (d = 0.27) and the unmedicated subsample (d = 0.33). In the full sample, baseline medication status interacted with group and deviant type indicating that double-deviant MMN, compared with single deviants, was reduced in unmedicated converters compared with nonconverters (d = 0.43). Further, within the unmedicated subsample, deficits in double-deviant MMN were most strongly associated with earlier conversion to psychosis (hazard ratio, 1.40 [95% CI, 1.03-1.90]; P = .03], which persisted over and above positive symptom severity.

Conclusions and Relevance

This study found that MMN amplitude deficits were sensitive to future psychosis conversion among individuals at risk of CHR-P, particularly those not taking antipsychotic medication at baseline, although associations were modest. While MMN shows limited promise as a biomarker of psychosis onset on its own, it may contribute novel risk information to multivariate prediction algorithms and serve as a translational neurophysiological target for novel treatment development in a subgroup of at-risk individuals.

Introduction

Psychotic disorders, including schizophrenia, are commonly preceded by a prodromal period involving attenuated psychoticlike symptoms and a decline in functioning. Efforts to prospectively identify individuals during this period have led to the development of clinical criteria for the clinical high risk for psychosis (CHR-P) syndrome. These criteria have been validated in studies showing that approximately 15% to 25% of individuals with the CHR-P syndrome will transition to a psychotic disorder within 1 to 3 years,¹ whereas 30% to 46% remit from the CHR-P syndrome.^2,3 While algorithms integrating cognitive and clinical data have been developed to predict the likelihood of future psychosis transition among individuals with the CHR-P syndrome,^4,5,6,7 they are not accurate enough to support treatment decision-making. Accordingly, many CHR-P studies have focused on identifying prognostic biomarkers that may improve clinical outcome prediction and clarify pathogenic mechanisms associated with psychosis onset to guide the development of mechanistically informed treatments.

One candidate biomarker is the electroencephalography-based mismatch negativity (MMN), a negative voltage event-related potential component elicited automatically 100 to 250 milliseconds following infrequent deviant sounds randomly embedded within a series of frequent standard sounds.^8,9 Any deviant sound features can elicit MMN, with pitch, duration, and intensity deviants as the most commonly studied.^10,11 MMN amplitude reduction is a highly replicated abnormality in schizophrenia^12,13 linked to N-methyl-d-aspartate receptor (NMDAR) hypofunction,¹⁴ a pathophysiological mechanism theorized to contribute to symptoms and cognitive dysfunction.^{15,16,17,18,19}

Evidence of reduced MMN among individuals with the CHR-P syndrome^20,21 suggests it may be compromised prior to psychosis onset.^{22,23,24,25,26,27,28,29,30,31,32,33,34} Several studies have shown MMN to be reduced in individuals with the CHR-P syndrome who subsequently convert to psychosis compared with individuals with the CHR-P syndrome followed up throughout 1 to 2 years without converting (Cohen d range, 0.20-0.75).^22,26,35,36 Moreover, 2 recent small studies suggest that relatively intact MMN may predict CHR-P remission and functional recovery.^37,38

The majority of CHR-P studies to date and to our knowledge have examined duration-deviant MMN,^{22,23,24,25,26,27,28,29,31,32,33,34} although some have included pitch-deviant MMN.^{22,23,29,31,35} Some have reported more sensitivity of duration-deviant than pitch-deviant MMN to the CHR-P syndrome^29,31,35 and/or future conversion.³⁵ Based on the possibility that some individuals with the CHR-P syndrome may have greater duration-deviant MMN deficits while others have greater pitch-deviant deficits, a prior study included both as well as a pitch + duration double-deviant MMN with the hypothesis that double deviance would yield a more sensitive measure than single-deviant MMNs.²² While mean MMN deficits in individuals with the CHR-P syndrome who developed psychosis were similar across deviant types, only the double-deviant predicted time to psychosis onset.²² Accordingly, the role of deviant type in predicting CHR-P outcomes remains an area of active investigation.

Theoretical models of MMN have adopted a predictive coding framework that emphasizes short-term plasticity,^39,40,41 positing that repetition of standard sounds in the recent auditory stream builds a memory trace that serves as a prediction that the standard will recur. When this prediction is violated by the occurrence of a deviant sound, an MMN prediction error signal is evoked and the predictive code is updated. These models have led to the identification of the repetition positivity⁴² elicited by standard stimuli that increases with successive standard repetitions,^40,42,43,44 consistent with strengthening of the standard’s memory trace and associated prediction that it will recur. The strength of the prediction error signal is known to increase as the predictive code strengthens,^40,43 suggesting that a greater repetition positivity memory trace effect should be associated with larger MMN. Using data from the current MMN study, we previously showed individuals with the CHR-P syndrome, particularly those who transitioned to psychosis, to have repetition positivity amplitude deficits to both early- and late-appearing standards.⁴⁵ However, how these repetition positivity deficits relate to MMN deficits in individuals with the CHR-P syndrome has yet to be examined.

This study examined MMN in individuals with the CHR-P syndrome and healthy control (HC) participants using data collected as part of the multisite North American Prodrome Longitudinal Study (NAPLS2). In the largest study of MMN in individuals with the CHR-P syndrome to date and to our knowledge, we evaluated whether baseline MMN amplitude was associated with future clinical outcomes, as well as with time to psychosis onset. To overcome limitations associated with single-deviant MMN paradigms, we used a multideviant paradigm consisting of duration and pitch deviants as well as pitch + duration double deviants.²² We hypothesized that baseline MMN amplitudes would be reduced in individuals with the CHR-P syndrome who subsequently converted to psychosis compared with those did not convert during follow-up and compared with HCs. Based on prior reports,^37,38 we further hypothesized that baseline MMN amplitudes would be normal in individuals with the CHR-P syndrome whose symptoms remitted during follow-up and would be larger than MMN of individuals with the CHR-P syndrome whose symptoms persisted or converted to psychosis. We also hypothesized that double-deviant MMN would be more strongly associated with CHR-P outcomes compared with single-deviant MMNs, both in case-control and time-to-conversion analyses. Further, we hypothesized that (1) the previously reported⁴⁵ repetition positivity memory trace deficits in these individuals with the CHR-P syndrome would be correlated with MMN deficits using traditional deviant-standard difference waves and (2) MMN and repetition positivity would be independently associated with time to psychosis onset. Finally, given the possible role of antipsychotic medication at baseline in altering clinical symptom severity, clinical outcomes, and possibly MMN amplitude, we conducted all analyses in the full CHR-P sample and in the subsample not prescribed antipsychotic medications, hypothesizing that the unmedicated subsample would yield stronger associations.

Methods

Participants

Participants with the CHR-P syndrome and HC individuals were aged 12 to 35 years in the 8-site NAPLS2 project who had electroencephalography data recorded during an MMN paradigm (eFigure 1 in the Supplement). Baseline MMN data were collected from June 2009 through April 2013. Participants with the CHR-P syndrome met the criteria of psychosis-risk syndromes based on the Structured Interview for Psychosis-Risk Syndromes.⁴⁶ Symptoms were assessed using the Scale of Psychosis-Risk Syndromes.⁴⁶ Exclusion criteria included lifetime psychotic disorder diagnosis, IQ of 70 or less, or significant central nervous system disorder, and HCs could not be taking antipsychotics or have a first-degree relative with psychosis.^47,48 The study was approved by the institutional review board at each site. Adult participants provided written informed consent, and minors provided written assent with parents providing written consent.

Participants completed baseline electroencephalography recording and were followed up for 24 months or until they converted to psychosis, completing clinical assessments every 6 months (eTable 6 in the Supplement provides characteristics of study completers vs noncompleters). Data on race and ethnicity were self-reported. While some participants with the CHR-P syndrome converted (CHR-P converters), others were followed up for 24 months and did not convert (CHR-P nonconverters). CHR-P nonconverters were further classified by 24-month clinical outcome as CHR-P remitters (ie, no longer met CHR-P criteria) or CHR-P persistent (ie, syndrome progression/symptom persistence). Baseline clinical assessment was completed on a separate day from electroencephalography recording (median [SD] time between assessments, 18.7 [23.4] days). Participants with the CHR-P syndrome, including CHR-P converters and CHR-P nonconverters, were taking antipsychotic medications at baseline but were included because the clinical history indicated that medication was initiated when symptoms were in the CHR-P range.

MMN Oddball Paradigm

Three infrequent deviant tones were pseudorandomly presented among 633-Hz, 50-millisecond frequent standard tones (85%) in the same order for all participants, including (1) a 633-Hz, 100-millisecond duration-deviant tone (5%), (2) a 1000-Hz, 50-millisecond pitch-deviant tone (5%), and (3) a 1000-Hz, 100-millisecond combined pitch + duration double-deviant tone (5%). All tones had a 5-millisecond rise and fall time. A total of 1794 trials with a 500-millisecond stimulus onset asynchrony were presented in 3 blocks, allowing a pause every 5 minutes. Participants were instructed to ignore auditory stimuli while simultaneously performing a visual oddball task. Visual stimulus presentation was jittered to avoid co-occurring visual oddball and MMN stimuli and to minimize correlation between MMN and visual event-related potential components.

Electroencephalographic Data Acquisition/Preprocessing

Participants wore ER1-A earphones (Etymotic) during the MMN paradigm viewed on a computer monitor. Electroencephalography recordings were digitized at 1024 Hz using a 32-channel (4 sites) or 64-channel (4 sites) electrode cap and a ActiveTwo system with mastoid reference electrodes (BioSemi). Vertical/horizontal electro-oculogram electrodes were used for eye movement/blink correction. Continuous electroencephalography data were rereferenced to a mean mastoid and high-pass filtered at 1 Hz before segmentation into 1-second epochs. Blink/saccade correction was completed using a regression method⁴⁹ and epochs were baseline corrected (−50 to 0 milliseconds). Data were subjected to spherical spline interpolation⁵⁰ and epochs with amplitudes greater than ±100 μV were rejected. Event-related potential averages were determined using a sorted averaging method.⁵¹ Additional preprocessing details are provided in the eMethods in the Supplement and have been described previously.^22,52 Following calculating averaged stimulus-locked electroencephalography epochs by deviant type, event-related potential waveforms were low-pass filtered at 30 Hz and standard waveforms were subtracted from deviant waveforms to obtain difference waves for each deviant type for each participant. MMN was quantified at 6 frontocentral electrodes (ie, F3, Fz, F4, C3, Cz, C4)⁵³ as the mean amplitude over a fixed window defined based on grand average difference waves (90-170 milliseconds for pitch-deviant and double-deviant tones and 150-230 milliseconds for duration-deviant tones).

Statistical Analysis

Analyses were run on age- and study site–adjusted MMN amplitude z scores, which reflect the degree to which a participant’s MMN amplitude deviates, in standard deviation units, from the normal value expected for their age and study site based on observed HC group data (eMethods in the Supplement). This procedure results in an HC MMN mean (SD) z score of 0 (1), with more positive z scores reflecting more deficient (ie, less negative) MMN amplitudes. This approach, which we have used previously,^{22,45,54,55,56} only removes normal aging and site-specific associations while retaining any pathological aging associations in the CHR-P data.

Group differences in MMN were assessed using mixed-effects models, with 2 groups (CHR-P and HC), 3 groups (CHR-P converter, CHR-P nonconverter, and HC), or 4 groups (CHR-P converter, CHR-P persistent, CHR-P remitter, and HC) as the between-participants factor, deviant type (duration, pitch, and double), frontocentral lead (frontal and central), and lateral lead (left, midline, and right) as within-participants factors and participant as a random factor. Unstructured and compound symmetry covariance matrices were evaluated using Schwarz bayesian information criterion to assess model fit. Models were rerun excluding participants taking antipsychotics at baseline. Further, a 2-group (CHR-P converter and CHR-P nonconverter) model was run including antipsychotic medication status (medicated vs unmedicated) as another between-participants factor. Additional models covaried for sex (eResults in the Supplement).

Hierarchical Cox regressions were performed to model the association between MMN and time to psychosis onset in the full sample of participants with the CHR-P syndrome, with censoring of data when participants withdrew or were lost to follow-up. Additional Cox regressions tested the association between MMN and time to conversion over and above positive symptoms by first entering modified Scale of Psychosis-Risk Syndromes P1 (unusual thought content/delusional ideas) + P2 (suspiciousness/persecutory ideas) combination scores implemented in the NAPL2 risk calculator⁴ and previously shown to be the strongest clinical predictor of psychosis.^57,58 Finally, Cox regressions determined the extent to which MMN and repetition positivity are associated with time to conversion, using repetition positivity composite z scores (eMethods in the Supplement).⁴⁵

The mean MMN z score across 6 frontocentral electrodes for each deviant type was correlated with Scale of Psychosis-Risk Syndromes composite symptom scores and Scale of Psychosis-Risk Syndromes P1 + P2 scores and with repetition positivity z scores. False discovery rate (FDR) multiple comparison adjustment⁵⁹ (α = .05; 2-tailed) was applied. Analysis took place between December 2019 and December 2021.

Results

Group Differences

Sample characteristics are shown in the Table, and mean MMN amplitudes by group are provided in eTable 1 in the Supplement. Overall, 15 individuals (1.8%) identified as American Indian/Alaska Native, 60 (7.3%) as Asian, 126 (15.3%) as Black/African American, 145 (17.7%) as Hispanic/Latino, 2 (0.2%) as Native Hawaiian/Pacific Islander, and 490 (59.7%) as White. Overall, 130 of 580 CHR-P participants (22.4%), including 23 of 77 CHR-P converters (29.9%) and 48 of 238 CHR-P nonconverters (20.2%), were taking antipsychotic medications at baseline. Mixed models used unstructured covariance matrices, which had the lowest bayesian information criterion. There was a trend-level reduction in MMN in individuals with the CHR-P syndrome (n = 580; mean [SD] age, 19.24 [4.39] years; 247 female individuals [42.6%]) compared with HC (n = 241; mean [SD] age, 20.33 [4.74] years; 114 female individuals [47.3%]) across deviant types (F_1,819 = 3.51; P = .06; Cohen d = 0.15; Figure 1; eTable 2 in the Supplement) although the statistical significance of this difference was reduced in the unmedicated subsample (F_1,689 = 2.27; P = .13; Cohen d = 0.12; eTable 3 in the Supplement).

Table. Demographic and Clinical Characteristics in CHR-P and HC Groups.

Characteristic	CHR-P (n = 580)	HC (n = 241)	χ² or t	P value	CHR-P converters (n = 77)	CHR-P nonconverters (n = 238)	χ² or t	P value
Sex, No. (%)
Female	247 (42.6)	114 (47.3)	χ² = 1.54	.22	29 (37.7)	107 (45.0)	χ² = 1.26	.26
Male	333 (57.4)	127 (52.7)	χ² = 1.54	.22	48 (62.3)	131 (55.0)	χ² = 1.26	.26
CHR-P syndrome, No. (%)^a
APSS	549 (94.7)	NA	NA	NA	73 (94.8)	224 (94.1)	χ² = 0.05	.82
BIPS	18 (3.1)	NA	NA	NA	8 (10.9)	3 (1.3)	χ² = 14.39	<.001
GRDS	71 (12.2)	NA	NA	NA	14 (18.2)	27 (11.3)	χ² = 2.40	.12
Current antipsychotic medication, No. (%)	130 (22.4)	0 (0.0)	χ² = 64.18	<.001	23 (29.9)	48 (20.2)	χ² = 3.14	.08
Age, mean (SD), y^b	19.24 (4.39)	20.33 (4.74)	t = 10.05	.002	18.64 (3.63)	19.45 (4.58)	t = 1.42	.16
Baseline SOPS ratings, mean (SD)^c
Positive	11.38 (4.17)	0.92 (1.50)	t = 52.77	<.001	13.05 (3.97)	10.79 (4.45)	t = 3.97	<.001
Negative	11.48 (6.13)	1.60 (2.50)	t = 32.75	<.001	12.29 (6.31)	11.08 (6.25)	t = 1.46	.15
Disorganization	4.97 (3.03)	0.66 (1.18)	t = 29.31	<.001	6.10 (3.89)	4.64 (3.03)	t = 3.02	.003
General	8.74 (4.34)	1.35 (2.19)	t = 32.64	<.001	9.49 (4.29)	7.96 (4.33)	t = 2.71	.007

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Abbreviations: APSS, Attenuated Positive Symptoms Syndrome; BIPS, Brief Intermittent Psychotic Syndrome; CHR-P, clinical high risk for psychosis; GRDS, Genetic Risk and Deterioration Syndrome; HC, healthy control; NA, not applicable; SOPS, Scale of Psychosis-Risk Syndromes.

^{^a}

CHR-P criteria for APSS, BIPS, and GRDS are not mutually exclusive.

^{^b}

Age range: CHR-P, 12.09-36.33 years; HC, 12.09-34.50 years; CHR-P converters, 12.83-28.51 years; CHR-P nonconverters, 12.09-36.33 years.

^{^c}

SOPS negative, disorganization, and general symptom ratings missing for 3 participants with the CHR-P syndrome (1 CHR-P nonconverter).

Figure 1. — A, Scalp topography maps, depicting mean mismatch negativity amplitudes in a fixed measurement window (indicated by gray bars in waveforms), are shown for duration-, pitch-, and double-deviant stimuli for healthy control (HC; n = 241) and clinical high risk for psychosis (CHR-P; n = 580) groups. Mean difference waveforms across 6 frontocentral electrodes are shown for HC and CHR-P groups for each deviant type. B, Group means for mismatch negativity age- and site-corrected z scores are shown. Error bars denote standard errors within groups.

The 3-group (CHR-P converter, CHR-P nonconverter, and HC) analysis of MMN yielded a significant group association (F_2,553 = 3.58; P = .03; Figure 2; eTable 2 in the Supplement), with follow-up tests showing reduced baseline MMN amplitudes in CHR-P converters (n = 77) compared with CHR-P nonconverters (n = 238; t₅₅₃ = 2.05; P = .04; Cohen d = 0.27) and HCs (n = 241; t₅₅₃ = 2.68; P = .008; Cohen d = 0.39). This group association did not interact with deviant type. A nearly identical pattern of associations was observed when the analysis was restricted to the unmedicated subsample, as MMN of CHR-P converters was reduced compared with CHR-P nonconverters (Cohen d = 0.33; eTable 3 in the Supplement).

In the mixed model including medication status (eTable 4 in the Supplement), a 3-way conversion group × deviant type × medication status interaction association was observed (F_2,622 = 7.8; P < .001), driven by a conversion group × medication status association for double-deviant MMN only. Follow-up tests showed double-deviant MMN to be reduced in CHR-P converters compared with CHR-P nonconverters in the unmedicated CHR-P subsample (t₆₂₂ = 2.78; Tukey-Kramer–adjusted P = .03; Cohen d = 0.43) but not the medicated CHR-P subsample (t₆₂₂ = 0.43; adjusted P = .67) (Figure 3). There were no medication differences within CHR-P converters (t₆₂₂ = 1.32; adjusted P = .55) or CHR-P nonconverters (t₆₂₂ = 0.05; adjusted P = .99).

Figure 3. — A, Scalp topography maps, depicting mean MMN amplitudes in a fixed measurement window (indicated by gray bars in waveforms), are shown for duration-, pitch-, and double-deviant stimuli for unmedicated clinical high risk for psychosis (CHR-P) nonconverters (n = 190), unmedicated CHR-P converters (n = 54), medicated CHR-P converters (n = 23), and medicated CHR-P nonconverters (n = 48). Mean difference waveforms across 6 frontocentral electrodes are shown for each group for each deviant type. B, Group means for MMN age- and site-corrected z scores by medication status are shown for double-deviant MMN. Error bars denote standard errors within groups.

Further parsing the CHR-P nonconverters in the 4-group analysis (HC, CHR-P converter, CHR-P persistent, and CHR-P remission) revealed no additional information beyond the 3-group model (eResults, eTable 5, and eFigure 2 in the Supplement).

Cox Regressions

More deficient double-deviant MMN was associated with a shorter time to conversion at a trend level (χ² = 2.84; Wald[1] = 2.85; P = .09; exp[B] = 1.24; 95% CI, 0.97-1.60) in the full CHR-P sample, but this association became significant when tested in the unmedicated subsample (χ² = 4.63; Wald[1] = 4.672; P = .03; exp[B] = 1.40; 95% CI, 1.03-1.90) (Figure 4). The addition of pitch- and duration-deviant MMN in the second step provided no significant improvement in prediction accuracy (full sample model: χ² = 3.30, P = .35; unmedicated sample model χ² = 4.67, P = .20) and attenuated the double-deviant association in the full (Wald[1] = 1.24; P = .27; exp[B] = 1.25; 95% CI, 0.85-1.80) and unmedicated (Wald[1] = 2.33; P = .13; exp[B] = 1.41; 95% CI, 0.91-2.19) samples. There was no association between pitch- or duration-deviant MMN and time to conversion when entered at the first step in the full sample (pitch-deviant MMN: χ² = 1.16; Wald[1] = 1.16; P = .28; exp[B] = 1.14; 95% CI, 0.90-1.45; duration-deviant MMN: χ² = 2.00; Wald[1] = 2.00; P = .16; exp[B] = 1.19; 95% CI, 0.94-1.52) and in the unmedicated subsample (pitch-deviant MMN:χ² = 2.25; Wald[1] = 2.25; P = .13; exp[B] = 1.25; 95% CI, 0.93-1.66; duration-deviant MMN: χ² = 1.53; Wald[1] = 1.54; P = .22; exp[B] = 1.19; 95% CI, 0.90-1.58).

Figure 4. — Greater double-deviant MMN amplitude deficits in participants at clinical high risk for psychosis are associated with an earlier transition to psychosis. Estimated cumulative survival functions are plotted for the 25th, 50th, and 75th percentiles of duration-deviant MMN age- and site- corrected z scores.

Controlling for P1 + P2 symptoms, double-deviant MMN remained a significant predictor of time to conversion among unmedicated individuals with the CHR-P syndrome (overall model: χ² = 29.81; P < .001; χ²change = 4.43; P = .04; double-deviant MMN: Wald[1] = 4.23; P = .04; exp[B] = 1.39; 95% CI, 1.02-1.91) but not in the full sample (overall model: χ² = 24.79, P < .001; χ² change = 2.43; P = .12; double-deviant MMN: Wald[1] = 2.35; P = .13; exp[B] = 1.23; 95% CI, 0.95-1.59).

When repetition positivity and double-deviant MMN were included together in the model, both MMN and repetition positivity were independently associated with time to conversion in the unmedicated subsample (overall model: χ² = 8.72; P = .01; double-deviant MMN: Wald[1] = 3.77; P = .05; exp[B] = 1.36; 95% CI, 1.00-1.85; repetition positivity: Wald[1] = 4.23; P = .04; exp[B] = 0.69; 95% CI, 0.48-0.98) but not in the full sample (overall model: χ² = 4.71; P = .10; double-deviant MMN: Wald[1] = 2.37; P = .12; exp[B] = 1.22; 95% CI, 0.95-1.58; repetition positivity: Wald[1] = 1.92; P = .17; exp[B] = 0.80; 95% CI, 0.59-1.10).

Correlations

There were no associations between MMN and symptoms (FDR-corrected P’s > .06). Double-deviant MMN was weakly negatively correlated with repetition positivity in individuals with the CHR-P syndrome (r = –0.12, FDR-corrected P = .02) and HCs (r = –0.16, FDR-corrected P = .03). Pitch-deviant MMN was correlated with repetition positivity in individuals with the CHR-P syndrome (r = –0.10, FDR-corrected P = .03) but not HCs (FDR-corrected P = .31). Duration-deviant MMN was not associated with repetition positivity in individuals with the CHR-P syndrome (FDR-corrected P = .41) or HCs (FDR-corrected P = .10).

Discussion

In the largest study of individuals with the CHR-P syndrome followed up longitudinally to date and to our knowledge, we examined whether auditory MMN is associated with future clinical outcomes. Individuals with the CHR-P syndrome when analyzed as a group, irrespective of clinical outcomes, demonstrated only a trend-level reduction in MMN amplitudes compared with HCs across MMN double, duration, and pitch deviants. However, when subgrouped according to clinical outcomes, MMN amplitude deficits, irrespective of deviant type, were evident among CHR-P converters compared with CHR-P nonconverters and HCs. Moreover, when only the unmedicated CHR-P subsample was considered, amplitude deficits for double-deviant MMN, compared with single-deviant MMNs, showed greater sensitivity to conversion and was associated with earlier conversion to psychosis. In contrast, no association was observed between baseline MMN and future CHR-P remission. Together, these results suggest that MMN is sensitive to conversion, and the imminence of conversion, in individuals with the CHR-P syndrome, particularly those who were not taking antipsychotic medications at baseline. Importantly, double-deviant MMN improved prediction of time to conversion over and above the predictive accuracy provided by P1 + P2 symptoms among unmedicated individuals with the CHR-P syndrome, suggesting that prognostic information provided by MMN is not redundant with the strongest symptom predictor of psychosis.

Our results replicate and extend findings from previous smaller studies demonstrating that deficient MMN amplitude is associated with future conversion,^22,26,35,36 as well as earlier psychosis onset,^22,35 further corroborating deficient MMN as a biomarker of psychosis risk. Given prior research showing MMN to be mediated by glutamatergic NMDAR neurotransmission,^14,17 these studies support NMDAR hypofunction models of schizophrenia^15,16,18,19 and suggest that this pathophysiological mechanism predates, and indicates increased risk for, future psychosis. While our results corroborate prior small single-site studies,^22,26,35,36 our effect size was relatively small and insufficient to support MMN’s clinical utility as prognostic indicator on its own. Given the heterogeneity among individuals with the CHR-P syndrome, it is possible that the modest predictive accuracy of MMN in unselected individuals with the CHR-P syndrome obscures a stronger prognostic effect in an unidentified subtype of individuals with the CHR-P syndrome. Moreover, the ability of MMN to contribute small effects to a stronger multimodal multivariate prediction algorithm has yet to be fully explored.

MMNs elicited by pitch- or duration-deviant stimuli are subserved by distinct neural generators.^9,11,60 The MMN schizophrenia literature suggests that duration-deviant MMN is more impaired in schizophrenia than pitch-deviant MMN,^12,13,61,62 although there is variability across studies and clear evidence that pitch-deviant MMN is also compromised. We previously showed double-deviant MMN to be superior to single-deviant MMNs in predicting time to psychosis onset,²² which we replicated in the current study, although these associations only reached statistical significance in the CHR-P subsample that were not taking antipsychotics. This suggests that the double-deviant MMN increases sensitivity to future psychosis risk, relative to single-deviant MMNs, but also highlights that treatment with antipsychotic medication at the time of MMN assessment obscures this predictive association, an important caveat for future studies.

Based on the predictive coding framework increasingly adopted in MMN research,^39,40,41 MMN amplitude reduction observed in future CHR-P converters is indicative of a faulty prediction error signal when an improbable deviant stimulus occurs. Our prior analysis of the repetition positivity to standard stimuli⁴⁵ further implicated deficient predictive code/memory trace formation and strengthening with standard repetitions in CHR-P converters. Thus, deficient predictive code formation and maintenance, as well as prediction error signaling, are evident prior to, and foretell more imminent onset of, full psychosis. Interestingly, the MMN and repetition positivity deficits in the current CHR-P sample were, at best, only weakly negatively correlated, and each independently predicted time to conversion, suggesting they reflect distinct pathophysiological mechanisms within the auditory predictive coding machinery.

Interestingly, our results contrast with prior reports showing relatively intact MMN to be associated with future CHR-P remission.^37,38 However, both studies included CHR-P converters among the CHR-P nonremitters, potentially confounding MMN predictions of remission with predictions of conversion.⁶³

Limitations

Nearly half of the NAPLS2 participants failed to complete the study,⁴⁸ preventing their inclusion in the clinical outcome analysis. Also, because the traditional multideviant paradigm we implemented requires separate means of each deviant type to be calculated, there were not enough deviant trials to generate MMN submeans and examine associations with the number of preceding standards on the magnitude of the MMN. Accordingly, we were unable to complete an analysis of the memory trace association with MMN elicited by deviant stimuli. Future studies using a single-deviant or a roving standard paradigm are needed to address whether the expected memory trace association with the magnitude of the MMN prediction error signal is also compromised in CHR-P converters. Indeed, such work may result in an MMN paradigm optimized for CHR-P clinical prediction.

Conclusions

Deficient MMN amplitude was associated with future psychosis conversion and the imminence of this conversion over and above positive symptom severity, particularly in antipsychotic medication–free individuals with the CHR-P syndrome, although associations were modest. Results indicate that while MMN shows limited promise as a biomarker of future psychosis on its own, it may contribute new prognostic information about psychosis risk compared with the most predictive CHR-P positive symptom domains, and the contributions of MMN to multivariate prediction algorithms remain to be fully investigated. These results also support the potential role of MMN as a translational neurophysiological target for novel treatment development in a subgroup of individuals with the CHR-P syndrome, including possible efforts to target NMDAR glutamate transmission based the role that NMDARs play in mediating the MMN.

Supplement.

eMethods.

eFigure 1. Enrollment and inclusion flow chart

eFigure 2. MMN amplitudes by clinical outcome group

eResults.

eTable 1. Mean MMN amplitudes by group

eTable 2. CHR-P and conversion group effects on MMN

eTable 3. Analysis of MMN by group and conversion group in unmedicated subsample

eTable 4. Analysis of MMN by conversion group with medication status

eTable 5. Analysis of MMN clinical outcome group in full and unmedicated subsamples

eTable 6. Characteristics of CHR-Nonconverter completers and study noncompleters

eReferences

Click here for additional data file.^{(546.4KB, pdf)}

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Supplement.

eMethods.

eFigure 1. Enrollment and inclusion flow chart

eFigure 2. MMN amplitudes by clinical outcome group

eResults.

eTable 1. Mean MMN amplitudes by group

eTable 2. CHR-P and conversion group effects on MMN

eTable 3. Analysis of MMN by group and conversion group in unmedicated subsample

eTable 4. Analysis of MMN by conversion group with medication status

eTable 5. Analysis of MMN clinical outcome group in full and unmedicated subsamples

eTable 6. Characteristics of CHR-Nonconverter completers and study noncompleters

eReferences

Click here for additional data file.^{(546.4KB, pdf)}

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PERMALINK

Mismatch Negativity in Response to Auditory Deviance and Risk for Future Psychosis in Youth at Clinical High Risk for Psychosis

Holly K Hamilton, PhD

Brian J Roach, MS

Peter M Bachman, PhD

Aysenil Belger, PhD

Ricardo E Carrión, PhD

Erica Duncan, MD

Jason K Johannesen, PhD

Gregory A Light, PhD

Margaret A Niznikiewicz, PhD

Jean Addington, PhD

Carrie E Bearden, PhD

Kristin S Cadenhead, MD

Barbara A Cornblatt, PhD

Thomas H McGlashan, MD

Diana O Perkins, MD

Ming T Tsuang, MD, PhD

Elaine F Walker, PhD

Scott W Woods, MD

Tyrone D Cannon, PhD

Daniel H Mathalon, PhD, MD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Participants

MMN Oddball Paradigm

Electroencephalographic Data Acquisition/Preprocessing

Statistical Analysis

Results

Group Differences

Table. Demographic and Clinical Characteristics in CHR-P and HC Groups.

Figure 1. Mismatch Negativity Amplitudes by Group.

Figure 2. Mismatch Negativity Amplitudes by Conversion Group.

Figure 3. Mismatch Negativity (MMN) Amplitudes by Conversion Group and Medication Status.

Cox Regressions

Figure 4. Estimated Survival Functions for Quartiles of Double-Deviant Mismatch Negativity (MMN) Amplitude Among Unmedicated Participants at Clinical High Risk for Psychosis .

Correlations

Discussion

Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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