Skip to main content Accessibility help
×
Home
Hostname: page-component-99c86f546-7mfl8 Total loading time: 0.819 Render date: 2021-12-07T23:53:28.562Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

Forecasting prognostic trajectories with mismatch negativity in early psychosis

Published online by Cambridge University Press:  02 September 2021

Minah Kim
Affiliation:
Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
Taekwan Kim
Affiliation:
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
Wu Jeong Hwang
Affiliation:
Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
Silvia Kyungjin Lho
Affiliation:
Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
Sun-Young Moon
Affiliation:
Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
Tae Young Lee
Affiliation:
Department of Neuropsychiatry, Pusan National University Yangsan Hospital, Yangsan, Republic of Korea Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
Jun Soo Kwon*
Affiliation:
Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea Institute of Human Behavioral Medicine, SNU-MRC, Seoul, Republic of Korea
*
Author for correspondence: Jun Soo Kwon, E-mail: kwonjs@snu.ac.kr
Rights & Permissions[Opens in a new window]

Abstract

Background

Prognostic heterogeneity in early psychosis patients yields significant difficulties in determining the degree and duration of early intervention; this heterogeneity highlights the need for prognostic biomarkers. Although mismatch negativity (MMN) has been widely studied across early phases of psychotic disorders, its potential as a common prognostic biomarker in early periods, such as clinical high risk (CHR) for psychosis and first-episode psychosis (FEP), has not been fully studied.

Methods

A total of 104 FEP patients, 102 CHR individuals, and 107 healthy controls (HCs) participated in baseline MMN recording. Clinical outcomes were assessed; 17 FEP patients were treatment resistant, 73 FEP patients were nonresistant, 56 CHR individuals were nonremitters (15 transitioned to a psychotic disorder), and 22 CHR subjects were remitters. Baseline MMN amplitudes were compared across clinical outcome groups and tested for utility prognostic biomarkers using binary logistic regression.

Results

MMN amplitudes were greatest in HCs, intermediate in CHR subjects, and smallest in FEP patients. In the clinical outcome groups, MMN amplitudes were reduced from the baseline in both FEP and CHR patients with poor prognostic trajectories. Reduced baseline MMN amplitudes were a significant predictor of later treatment resistance in FEP patients [Exp(β) = 2.100, 95% confidence interval (CI) 1.104–3.993, p = 0.024] and nonremission in CHR individuals [Exp(β) = 1.898, 95% CI 1.065–3.374, p = 0.030].

Conclusions

These findings suggest that MMN could be used as a common prognostic biomarker across early psychosis periods, which will aid clinical decisions for early intervention.

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Introduction

Early stages of psychotic disorder have been regarded as critical periods for early intervention to improve the clinical outcome of the disorder (Correll et al., Reference Correll, Galling, Pawar, Krivko, Bonetto, Ruggeri and Kane2018; Fusar-Poli, McGorry, & Kane, Reference Fusar-Poli, McGorry and Kane2017; Lieberman et al., Reference Lieberman, Perkins, Belger, Chakos, Jarskog, Boteva and Gilmore2001). It has been well studied that a shorter duration of untreated psychosis (DUP) is related to a better prognosis of first-episode psychosis (FEP) patients (Marshall et al., Reference Marshall, Lewis, Lockwood, Drake, Jones and Croudace2005; Perkins, Gu, Boteva, & Lieberman, Reference Perkins, Gu, Boteva and Lieberman2005). In addition, the concept of clinical high risk (CHR) for psychosis was established not only for early detection of FEP to shorten the DUP but also for delaying or preventing the onset of psychotic disorder (Miller et al., Reference Miller, McGlashan, Rosen, Somjee, Markovich, Stein and Woods2002; Yung et al., Reference Yung, Yuen, McGorry, Phillips, Kelly, Dell'Olio and Buckby2005). However, those patients in the early stages of psychotic disorder were proven to be heterogeneous in their prognostic trajectories. While approximately 40% of FEP patients show favorable outcomes, such as full remission or a partial response, the remaining patients display chronic, relapsing disease courses and are even treatment resistant (Birchwood, Todd, & Jackson, Reference Birchwood, Todd and Jackson1998; Lieberman, Reference Lieberman1993). Similarly, three prognostic trajectories of positive, moderately impaired, and severely impaired outcomes were found among CHR individuals through group-based multitrajectory modeling (Allswede et al., Reference Allswede, Addington, Bearden, Cadenhead, Cornblatt, Mathalon and Cannon2020). Meta-analytic studies reported that 22% of CHR subjects transitioned to a psychotic disorder within 3 years and 65% of CHR individuals were nonremitters within 1.94 years (Fusar-Poli et al., Reference Fusar-Poli, Bechdolf, Taylor, Bonoldi, Carpenter, Yung and McGuire2013, Reference Fusar-Poli, de Pablo, Correll, Meyer-Lindenberg, Millan, Borgwardt and Arango2020; Simon et al., Reference Simon, Borgwardt, Riecher-Rossler, Velthorst, de Haan and Fusar-Poli2013), suggesting that improving the general clinical outcome is as important as preventing the onset of psychotic disorder (Carrion et al., Reference Carrion, McLaughlin, Goldberg, Auther, Olsen, Olvet and Cornblatt2013; Lin et al., Reference Lin, Wood, Nelson, Beavan, McGorry and Yung2015; Schlosser et al., Reference Schlosser, Jacobson, Chen, Sugar, Niendam, Li and Cannon2012).

Prognostic heterogeneity produces significant difficulties in clinical decision making, such as decisions concerning the early use of clozapine in FEP patients who are resistant to usual antipsychotic treatment. Intervention for CHR individuals also involves challenges, such as deciding who should receive rigorous or less intensive interventions, including the justification of the use of antipsychotic medication. By using biological predictors that will aid in forecasting prognostic trajectories of early psychosis patients, patients’ suffering during trial and error regarding treatment will be resolved, and a significant amount of time and resources will be saved. Previous studies reported that cortical gyrification (Palaniyappan et al., Reference Palaniyappan, Marques, Taylor, Handley, Mondelli, Bonaccorso and Dazzan2013), bilateral hippocampal increase (Lappin et al., Reference Lappin, Morgan, Chalavi, Morgan, Reinders, Fearon and Dazzan2014), corticostriatal functional connectivity (Oh, Kim, Kim, Lee, & Kwon, Reference Oh, Kim, Kim, Lee and Kwon2020; Sarpal et al., Reference Sarpal, Argyelan, Robinson, Szeszko, Karlsgodt, John and Malhotra2016), glutathione and glutamate levels (Dempster et al., Reference Dempster, Jeon, MacKinley, Williamson, Theberge and Palaniyappan2020), and electrophysiological markers (Lho, Kim, Lee, Kwak, & Kwon, Reference Lho, Kim, Lee, Kwak and Kwon2019; Mi et al., Reference Mi, Wang, Li, She, Li, Huang and Zheng2021; Renaldi et al., Reference Renaldi, Kim, Lee, Kwak, Tanra and Kwon2019) were associated with the treatment response of FEP patients. In CHR subjects, structural brain imaging (Cannon et al., Reference Cannon, Chung, He, Sun, Jacobson and van Erp2015; de Wit et al., Reference de Wit, Ziermans, Nieuwenhuis, Schothorst, van Engeland, Kahn and Schnack2017; Ho et al., Reference Ho, Holt, Cheung, Iglesias, Goh, Wang and Zhou2017; Koutsouleris et al., Reference Koutsouleris, Riecher-Rossler, Meisenzahl, Smieskova, Studerus, Kambeitz-Ilankovic and Borgwardt2015; Koutsouleris, Upthegrove, & Wood, Reference Koutsouleris, Upthegrove and Wood2019; Reniers et al., Reference Reniers, Lin, Yung, Koutsouleris, Nelson, Cropley and Wood2017), neurochemical markers (Allen et al., Reference Allen, Chaddock, Egerton, Howes, Barker, Bonoldi and McGuire2015; Bossong et al., Reference Bossong, Antoniades, Azis, Samson, Quinn, Bonoldi and McGuire2019; Egerton et al., Reference Egerton, Stone, Chaddock, Barker, Bonoldi, Howard and McGuire2014), and event-related potential (ERP) markers (Bodatsch et al., Reference Bodatsch, Ruhrmann, Wagner, Muller, Schultze-Lutter, Frommann and Brockhaus-Dumke2011; Hamilton et al., Reference Hamilton, Roach, Bachman, Belger, Carrion, Duncan and Mathalon2019; Kim, Lee, Lee, Kim, & Kwon, Reference Kim, Lee, Lee, Kim and Kwon2015; Kim, Lee, Yoon, Lee, & Kwon, Reference Kim, Lee, Yoon, Lee and Kwon2018; Perez et al., Reference Perez, Woods, Roach, Ford, McGlashan, Srihari and Mathalon2014) were suggested as biological predictors of symptomatic and functional outcomes. However, clinically efficient biomarkers that forecast prognostic trajectories across the course of psychotic disorders, from CHR to FEP, have not yet been studied.

Mismatch negativity (MMN) is an ERP component that is elicited when repetitive standard stimuli are interrupted by infrequent deviant stimuli and is thus thought to be reflective of the automatic auditory change detection process (Naatanen & Escera, Reference Naatanen and Escera2000). Because MMN generation is associated with neurotransmission at the N-methyl-D-aspartate (NMDA) receptor, MMN has been widely studied across the course of psychotic disorders to elucidate the pathophysiological mechanism of schizophrenia (Javitt & Freedman, Reference Javitt and Freedman2015; Javitt, Steinschneider, Schroeder, & Arezzo, Reference Javitt, Steinschneider, Schroeder and Arezzo1996; Uno & Coyle, Reference Uno and Coyle2019). Reduced duration deviant MMN (dMMN) amplitude has been consistently reported in schizophrenia and early psychosis patients, including FEP patients and CHR individuals, although the degree of dMMN impairment is less significant than in chronic schizophrenia patients (Erickson, Ruffle, & Gold, Reference Erickson, Ruffle and Gold2016; Haigh, Coffman, & Salisbury, Reference Haigh, Coffman and Salisbury2017; Hamilton, Boos, & Mathalon, Reference Hamilton, Boos and Mathalon2020; Kim, Cho, Yoon, Lee, & Kwon, Reference Kim, Cho, Yoon, Lee and Kwon2017; Nagai et al., Reference Nagai, Tada, Kirihara, Yahata, Hashimoto, Araki and Kasai2013; Tateno et al., Reference Tateno, Higuchi, Nakajima, Sasabayashi, Nakamura, Ueno and Suzuki2021). Although little is known about dMMN as a prognostic predictor of FEP patients (Higgins, Lewandowski, Liukasemsarn, & Hall, Reference Higgins, Lewandowski, Liukasemsarn and Hall2021; Lho et al., Reference Lho, Kim, Park, Hwang, Moon, Oh and Kwon2020), previous studies, including our own, showed that dMMN was predictive of a transition to psychotic disorder, remission, and symptomatic and functional improvement in CHR individuals (Bodatsch et al., Reference Bodatsch, Ruhrmann, Wagner, Muller, Schultze-Lutter, Frommann and Brockhaus-Dumke2011; Fujioka et al., Reference Fujioka, Kirihara, Koshiyama, Tada, Nagai, Usui and Kasai2020; Kim et al., Reference Kim, Lee, Yoon, Lee and Kwon2018; Perez et al., Reference Perez, Woods, Roach, Ford, McGlashan, Srihari and Mathalon2014). Therefore, dMMN has the potential to be a clinically efficient biomarker that forecasts prognostic trajectories across the course of psychotic disorder, which would aid in clinical decision making regarding interventions for patients with FEP and subjects at CHR for psychosis.

In the current study, we aimed to investigate whether dMMN amplitude could be a potential biomarker to predict poor prognostic outcomes in FEP and CHR individuals. We hypothesized that (1) dMMN is impaired in FEP and CHR participants compared to healthy controls (HCs); thus, dMMN may be a potential biomarker for prognosis prediction in early psychosis patients; (2) baseline dMMN is smaller in FEP patients who are treatment resistant than in patients who are not treatment resistant and is predictive of later treatment resistance; and (3) dMMN at baseline is reduced in CHR individuals who are not remitted or who transition to a psychotic disorder compared to individuals who are remitted or who have not transitioned after a minimum of 1 year from the baseline dMMN assessment, and dMMN is predictive of future nonremission from CHR status or transition to a psychotic disorder.

Methods

Participants

A total of 104 FEP patients, 102 individuals at CHR for psychosis, and 107 HCs participated in the baseline assessment, including dMMN recording, between August 2009 and June 2020. Among them, 25 FEP patients, 48 CHR subjects, and 47 HCs participated in our previous dMMN studies (Kim et al., Reference Kim, Cho, Yoon, Lee and Kwon2017, Reference Kim, Lee, Yoon, Lee and Kwon2018; Lho et al., Reference Lho, Kim, Lee, Kwak and Kwon2019). FEP patients and CHR individuals were recruited from the Seoul Youth Clinic (SYC; www.youthclinic.org), a center for early detection and intervention of psychosis (Kwon, Byun, Lee, & An, Reference Kwon, Byun, Lee and An2012) and from an inpatient and outpatient clinic of the Department of Neuropsychiatry at the Seoul National University Hospital (SNUH). The definition of FEP was an individual aged 16–40 years who satisfied the diagnosis of schizophreniform disorder, schizophrenia or schizoaffective disorder when assessed using the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Axis I Disorders (SCID-I) and a duration of psychotic illness less than 2 years. Psychotic symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS). To confirm the CHR status of the participants, the Structured Interview for Prodromal Symptoms (SIPS) (Miller et al., Reference Miller, McGlashan, Rosen, Somjee, Markovich, Stein and Woods2002) was used. Prodromal symptoms were assessed using the validated Korean version of the Scale of Prodromal Symptoms (SOPS) (Jung et al., Reference Jung, Jang, Kang, Choi, Shin, Kim and Kwon2010; Miller et al., Reference Miller, McGlashan, Rosen, Cadenhead, Cannon, Ventura and Woods2003). In both the FEP and CHR groups, general functional status was defined using the modified Global Assessment of Functioning (mGAF) (Hall, Reference Hall1995). HCs were recruited via internet advertisement and were screened using the SCID-I Nonpatient Edition (SCID-NP). Potential HC participants were excluded if they had any first- to third-degree biological relatives with a psychotic disorder. Common exclusion criteria included substance abuse or dependence (except nicotine), neurological disease or significant head trauma, medical illness that could be accompanied by psychiatric symptoms, sensory impairments, and intellectual disability [intelligence quotient (IQ) < 70].

Among the 104 FEP patients, 90 patients with FEP received usual treatment, including antipsychotic medication, for at least 18 months until June 2020. Treatment resistance was defined when a patient showed at least moderately severe psychotic symptoms despite taking a sufficient dose (⩾20 mg olanzapine equivalent dose per day) of more than two antipsychotics for at least 12 months or taking clozapine according to the minimum requirement criteria of the Treatment Response and Resistance in Psychosis (TRRIP) working group consensus guidelines (Conley & Buchanan, Reference Conley and Buchanan1997; Gardner, Murphy, O'Donnell, Centorrino, & Baldessarini, Reference Gardner, Murphy, O'Donnell, Centorrino and Baldessarini2010; Howes et al., Reference Howes, McCutcheon, Agid, de Bartolomeis, van Beveren, Birnbaum and Correll2017). Certified psychiatrists who were blinded to the dMMN amplitudes thoroughly reviewed medical records to assess the symptom severity, medication status, and treatment adherence information provided by patients themselves and their caregivers at each visit to clinic. As a result, FEP patients were divided into treatment-resistant (FEP-TR, n = 17) and nonresistant (FEP-nonTR, n = 73) groups. The means and standard deviations of follow-up duration were 39.4 ± 32.7 months in the FEP-TR group and 46.1 ± 34.2 months in the FEP-nonTR group. Among the 102 CHR subjects, 78 CHR individuals participated in annual follow-up clinical assessment at least once until June 2020. A remitter was defined as a CHR individual who scored <3 on the SOPS positive symptoms subscale and 65⩾ on the mGAF measured at the last clinical assessment to reflect both symptomatic and functional recovery (Bossong et al., Reference Bossong, Antoniades, Azis, Samson, Quinn, Bonoldi and McGuire2019; Fujioka et al., Reference Fujioka, Kirihara, Koshiyama, Tada, Nagai, Usui and Kasai2020; Hamilton et al., Reference Hamilton, Roach, Bachman, Belger, Carrion, Duncan and Mathalon2019; Kim et al., Reference Kim, Lee, Yoon, Lee and Kwon2018). According to the remission criteria, certified psychiatrists who were blinded to dMMN amplitude determined 22 CHR remitters and 56 nonremitters, including 15 subjects who ended the follow-up assessment with a transition to a psychotic disorder (12 schizophrenia and three schizoaffective disorder) according to SIPS criteria. The means and standard deviations of follow-up duration were 46.0 ± 36.5 months for CHR remitters and 30.1 ± 27.9 months for CHR nonremitters. Sixty-three CHR individuals did not transition to psychotic disorder (online Fig. S1 in the Supplementary Material).

Written informed consent was obtained from all participants after they were given a thorough explanation of the study procedure (IRB no. H-1110-009-380). For minors, informed consent was obtained from both the participants themselves and their parents. This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of SNUH (IRB no. H-2008-195-1154).

MMN acquisition

Continuous electroencephalographic (EEG) recording was conducted using a Neuroscan 128 Channel SynAmps system equipped with a 128-channel Quick-Cap based on the modified 10–20 international system (Compumedics, Charlotte, NC, USA) while participants were performing a passive auditory oddball task. The EEG data were digitized at a 1000 Hz sampling rate, and an online bandpass filter between 0.05 and 100 Hz was used. The reference electrodes were placed on both mastoids. The vertical and horizontal electrooculograms were recorded using electrodes below and on the outer canthus of the left eye to monitor eye movement artifacts. The resistance of all electrode sites was less than 5 kΩ. During the passive auditory oddball task performance, participants were instructed to ignore the auditory sound and concentrate on a ‘Where's Waldo?’ picture book. A pseudorandom series of 1000 Hz (80 dB, 10 ms rise/fall) auditory stimuli were binaurally presented using a STIM2 sound generator (Compumedics). With an intertrial interval of 600 ms, the duration of deviant stimuli was 100 ms (18.2%, 218/1200), and the duration of standard stimuli was 50 ms (81.8%, 982/1200).

Data preprocessing

Curry version 7 software (Compumedics) was used to preprocess the ERP data. After replacing bad channels using the linear interpolation of the adjacent channels (up to 7%), eye movement artifact reduction was performed according to the validated ocular artifact reduction algorithm (Semlitsch, Anderer, Schuster, & Presslich, Reference Semlitsch, Anderer, Schuster and Presslich1986). EEG recordings were rereferenced to common average reference data, and a 0.1–30 Hz bandpass filter was applied. Continuous EEG data were epoched to a 100 ms prestimulus interval and a 300 ms poststimulus interval, and the averaged prestimulus interval voltage was used in baseline correction. Automatic artifact rejection was performed by removing the epochs containing EEG amplitudes that exceeded ± 75 μV. The means and standard deviations of the numbers of remaining epochs for deviant stimuli were not significantly different across all groups (Table 1). dMMN was calculated by subtracting the ERPs elicited by the standard stimuli from those elicited by deviant stimuli. The most negative deflection between 130 and 250 ms poststimulus onset at the FCz electrode site, where dMMN had the maximal amplitude (Garrido, Kilner, Stephan, & Friston, Reference Garrido, Kilner, Stephan and Friston2009), was detected as the peak dMMN amplitudes and latencies.

Table 1. Demographic, clinical, and duration deviant mismatch negativity (dMMN) characteristics of the participants at baseline

FEP, first-episode psychosis; CHR, clinical high risk; HC, healthy control; FEP-TR, FEP patients who were treatment resistant; FEP-nonTR, FEP patients who were not treatment resistant; IQ, intelligence quotient; DOI, duration of illness; DUP, duration of untreated psychosis; DUPP, duration of untreated prodromal psychosis; PANSS, Positive and Negative Syndrome Scale; SOPS, Scale of Prodromal Symptoms; mGAF, modified Global Assessment of Functioning.

Data are indicated as the mean ± standard deviation.

*Statistical significance at p < 0.05.

**Statistical significance at p < 0.005.

1 Analysis of variance, independent t test or Welch's t test if the variances were not equal; χ2 analysis or Fisher's exact test for categorical data.

2 Mann–Whitney U test, χ2 analysis or Fisher's exact test for categorical data.

3 Independent t test or Welch's t test if the variances were not equal; χ2 analysis or Fisher's exact test for categorical data.

4 Number (percentage) of participants who were prescribed each medication at the time of dMMN measurement.

Statistical analysis

The demographic, clinical, and dMMN characteristics were compared using analysis of variance (ANOVA) or independent t test across the groups for continuous variables. A Bonferroni test was used for post hoc analysis. Welch's t test was performed if the variances were not equal, and Mann–Whitney U tests were used if the normality assumption was not satisfied. Chi-square tests were used for categorical variables. Group comparisons of MMN amplitudes and latencies across the FEP patients, CHR subjects, and HCs were performed using univariate analysis of covariance (ANCOVA) with age as a covariate. A post hoc simple contrast test was used to reveal specific group differences. Binary logistic regression analyses with the backward selection method were used to investigate whether baseline dMMN amplitudes were predictive of treatment resistance in FEP patients and predictive of nonremission or transition to psychotic disorder in CHR individuals. Common independent variables were the dMMN amplitude at FCz electrode site, sex, handedness, age, IQ, and education years in both FEP and CHR groups. Variables with significant group differences at baseline were selected as additional independent variables, which included the duration of illness (DOI) for FEP treatment resistance prediction and the SOPS positive subscale score at baseline for CHR transition prediction. Statistical analyses were performed using SPSS v.25.0 (IBM, Armonk, NY), and statistical significance was set at p < 0.05.

Results

Participant characteristics

Table 1 summarizes the demographic and clinical characteristics of the participants at baseline. Detailed information including follow-up characteristics of the clinical outcome groups is presented in online Supplementary Table S1 (FEP-TR v. FEP-nonTR) and online Supplementary Table S2 (CHR remitters v. CHR nonremitters and transitioned CHR v. nontransitioned CHR) in the Supplementary Material. There were more females than males in the FEP group than in the CHR and HC groups (χ2 = 29.984, p < 0.001). Individuals at CHR for psychosis were significantly younger and less educated than FEP patients (age, p < 0.001; education years, p < 0.001) and HCs (age, p < 0.001; education years, p < 0.001). IQ was highest in HCs (HC v. FEP, p < 0.001; HC v. CHR, p < 0.001) and lowest in FEP patients (FEP v. CHR, p = 0.014). In the clinical outcome group comparison, FEP-TR patients had a longer DOI than FEP-nonTR patients (Z = −2.377, p = 0.017), and transitioned CHR subjects had higher scores on the SOPS positive symptoms subscale (Z = −2.018, p = 0.044) than nontransitioned CHR individuals at baseline. Other baseline demographic and clinical characteristics, as well as follow-up duration, were not significantly different across the clinical outcome groups. Online Tables S3 and S4 in the Supplementary Material show no significant difference in baseline participant characteristics between the FEP patients who were assessed as treatment resistant and those who were not, as well as between CHR subjects who participated in the follow-up assessment and those who did not.

MMN characteristics

Table 1 presents group comparison results of baseline dMMN peak amplitudes and latencies at the FCz electrode site. ANCOVA with age as a covariate revealed a significant group difference in dMMN amplitude at the FCz electrode site (F = 19.915, p < 0.001) across the FEP, CHR, and HC groups. A post hoc simple contrast test showed that dMMN amplitudes were greatest in HCs, intermediate in CHR subjects, and smallest in FEP patients (FEP v. HC, p < 0.001; CHR v. HC, p = 0.002; FEP v. CHR, p = 0.004; Fig. 1). There was no significant group effect of dMMN peak latency. According to the clinical outcome groups, the FEP-TR group showed smaller dMMN amplitudes than the FEP-nonTR group (Z = −2.093, p = 0.036, Cohen's d = 0.634; Fig. 2), and dMMN amplitudes of CHR remitters were greater than those of CHR nonremitters (t = −2.136, p = 0.036, Cohen's d = 0.544; Fig. 3). In addition, ANCOVA with age as a covariate showed that dMMN amplitudes were significantly different across FEP patients, CHR nonremitters, CHR remitters, and HCs (F = 14.435, p < 0.001). A post hoc simple contrast test revealed that dMMN amplitudes were similarly impaired in FEP patients and CHR nonremitters compared to CHR remitters and HCs (FEP v. CHR nonremitters, p = 0.164; FEP v. CHR remitters, p = 0.004; CHR remitters v. HC, p = 0.441; online Fig. S2 in the Supplementary Material). Group comparison results across the FEP-TR group, FEP-nonTR group, CHR nonremitters, CHR remitters, and HCs are provided in the online Supplementary Material (Fig. S3). No significant group difference in dMMN amplitude was found between CHR subjects who transitioned to a psychotic disorder and those who did not (online Table S5 in the Supplementary Material). Online Tables S6 and S7 in the Supplementary Material show that there was no significant difference in dMMN characteristics between FEP patients who were assessed as treatment resistant and those who were not or between CHR subjects who participated in the follow-up assessment and those who did not.

Fig. 1. (a) Grand-averaged duration deviant mismatch negativity (dMMN) waveforms across the patients with first-episode psychosis (FEP), subjects at clinical high risk (CHR) for psychosis, and healthy controls (HCs) at the FCz electrode site. (b) dMMN amplitude across the groups at the FCz electrode site. Horizontal lines in groups indicate means, and vertical lines indicate 95% confidence intervals. *Indicates statistical significance at p < 0.05. **Indicates statistical significance at p < 0.005. (c) Two-dimensional topographic maps of dMMN in FEP patients, CHR individuals, and HCs. The colored bar with numbers indicates the dMMN amplitude (μV).

Fig. 2. (a) Grand-averaged duration deviant mismatch negativity (dMMN) waveforms across the patients with first-episode psychosis (FEP) who were treatment resistant (FEP-TR) and those who were not (FEP-nonTR) at the FCz electrode site. (b) dMMN amplitude across the clinical outcome groups at the FCz electrode site. Horizontal lines in groups indicate means, and vertical lines indicate 95% confidence intervals. *Indicates statistical significance at p < 0.05. (c) Two-dimensional topographic maps of dMMN in FEP-TR and FEP-nonTR patients. The colored bar with numbers indicates the amplitude of the dMMN (μV).

Fig. 3. (a) Grand-averaged duration deviant mismatch negativity (dMMN) waveforms across the individuals at clinical high risk (CHR) for psychosis who were remitted and those who were not at the FCz electrode site. (b) dMMN amplitude across the clinical outcome groups at the FCz electrode site. Horizontal lines in groups indicate means, and vertical lines indicate 95% confidence intervals. *Indicates statistical significance at p < 0.05. (c) Two-dimensional topographic maps of dMMN in CHR remitters and CHR nonremitters. The colored bar with numbers indicates the amplitude of the dMMN (μV).

Predicting prognostic trajectories using MMN

Table 2 presents the results of binary logistic regression analysis with the backward selection method. Treatment resistance in FEP was predicted by the dMMN amplitude [Exp(β) = 2.100, 95% confidence interval (95% CI) 1.104–3.993, p = 0.024] and DOI [Exp(β) = 1.138, 95% CI 1.031–1.256, p = 0.010]. Nonremission in CHR patients was predicted by the dMMN amplitude [Exp(β) = 1.895, 95% CI 1.065–3.374, p = 0.030]. The binary logistic regression model for predicting the transition to a psychotic disorder in CHR subjects did not include the dMMN amplitude but only included the SOPS positive subscale score at baseline [Exp(β) = 1.178, 95% CI 1.005–1.380, p = 0.043].

Table 2. Significant predictors of treatment resistance in first-episode psychosis (FEP) patients and nonremission or transition to psychotic disorder in subjects at clinical high risk (CHR) for psychosis in binary logistic regression analysis with the backward selection method

CI, confidence interval; dMMN, duration deviant mismatch negativity; SOPS, Scale of Prodromal Symptoms.

*Statistical significance, p < 0.05.

Discussion

Diagnosis based on the symptomatic phenotype produces significant prognostic heterogeneity that interferes with the goal of early intervention in early psychosis; thus, investigation of biomarkers that are predictive of prognostic trajectories of early psychosis patients is warranted (Allswede et al., Reference Allswede, Addington, Bearden, Cadenhead, Cornblatt, Mathalon and Cannon2020; Birchwood et al., Reference Birchwood, Todd and Jackson1998; Clementz et al., Reference Clementz, Sweeney, Hamm, Ivleva, Ethridge, Pearlson and Tamminga2016). In this longitudinal study, we aimed to confirm the usefulness of dMMN as a common prognostic biomarker across the early psychosis periods, from CHR to FEP. In line with many previous studies (Erickson et al., Reference Erickson, Ruffle and Gold2016; Higgins et al., Reference Higgins, Lewandowski, Liukasemsarn and Hall2021; Tateno et al., Reference Tateno, Higuchi, Nakajima, Sasabayashi, Nakamura, Ueno and Suzuki2021), dMMN amplitudes at the FCz electrode site were impaired in both FEP and CHR patients; thus, these amplitudes were used for further analysis for prognosis prediction. From the baseline assessment, dMMN amplitude was smaller in FEP-TR patients than in FEP-nonTR patients and was predictive of later treatment resistance. In addition, the dMMN amplitude was reduced in CHR nonremitters compared to CHR remitters at baseline and was associated with nonremission from a CHR status. These findings not only support the need for biomarkers predictive of prognostic trajectories but also highlight the usefulness of dMMN as a common prognostic biomarker across the early psychosis periods.

In the current study, impaired dMMN amplitude was observed from the baseline in FEP patients with poor prognosis (i.e. FEP-TR) and was a significant predictor of later treatment resistance. Considering that MMN generation is closely related to NMDA receptor-mediated glutamatergic activity (Javitt et al., Reference Javitt, Steinschneider, Schroeder and Arezzo1996; Uno & Coyle, Reference Uno and Coyle2019), the current study result is in line with previous studies that reported an abnormal NMDA-glutamate system and its association with poor treatment response in FEP patients (Dempster et al., Reference Dempster, Jeon, MacKinley, Williamson, Theberge and Palaniyappan2020; Mouchlianitis et al., Reference Mouchlianitis, Bloomfield, Law, Beck, Selvaraj, Rasquinha and Howes2016). The present study suggests that FEP patients with reduced dMMN amplitude may benefit from the early use of clozapine or adjuvant use of drugs targeting the NMDA-glutamate system (Rapado-Castro et al., Reference Rapado-Castro, Dodd, Bush, Malhi, Skvarc, On and Dean2017; Swerdlow et al., Reference Swerdlow, Bhakta, Chou, Talledo, Balvaneda and Light2016; Zheng et al., Reference Zheng, Zhang, Cai, Yang, Qiu, Ungvari and Xiang2018). In addition, recent studies have shown that treatment resistance to antipsychotics is evident upon illness onset in most FEP-TR patients (Demjaha et al., Reference Demjaha, Lappin, Stahl, Patel, MacCabe, Howes and Murray2017; Lally et al., Reference Lally, Ajnakina, Di Forti, Trotta, Demjaha, Kolliakou and Murray2016), suggesting that FEP treatment resistance can be forecasted by biomarkers showing abnormalities from the early psychosis period, such as MMN.

We found that CHR nonremitters presented a reduced dMMN amplitude, which was comparable to that of FEP patients and was smaller than that of HCs, whereas CHR remitters showed a similar magnitude of dMMN amplitude as that of HCs, which was larger than that of FEP patients. This finding supports the biological heterogeneity of CHR individuals, which may be a cause of the smaller effect size of MMN impairment found in the entire CHR group compared to FEP and schizophrenia patients (Erickson et al., Reference Erickson, Ruffle and Gold2016; Kim et al., Reference Kim, Cho, Yoon, Lee and Kwon2017). Furthermore, we replicated our previous report with extended cohort data and showed that a relatively preserved dMMN amplitude to a level of HCs at baseline was associated with a better clinical outcome, such as remission (Kim et al., Reference Kim, Lee, Yoon, Lee and Kwon2018); a similar result was provided by a recent study by Fujioka et al. (Reference Fujioka, Kirihara, Koshiyama, Tada, Nagai, Usui and Kasai2020). However, our analysis did not replicate previous study results that reported that impaired MMN was predictive of later transition to psychotic disorder (Bodatsch et al., Reference Bodatsch, Ruhrmann, Wagner, Muller, Schultze-Lutter, Frommann and Brockhaus-Dumke2011; Perez et al., Reference Perez, Woods, Roach, Ford, McGlashan, Srihari and Mathalon2014). The relatively small number of transitioned CHR subjects and the prescription of adequate medication, which aims to reduce the transition rate, may be the cause of the insignificant results of the current study. Therefore, future large-scale multisite longitudinal studies controlling for medication are warranted to test whether MMN could predict the prognosis of CHR individuals, including remission, nonremission, and transition to psychotic disorder, as in the recent study by Hamilton et al. (Reference Hamilton, Roach, Bachman, Belger, Carrion, Duncan and Mathalon2019) of auditory P300.

Although treatment guidelines for schizophrenia recommend the use of clozapine in patients who are resistant to usual antipsychotic treatment (Addington, Addington, Abidi, Raedler, & Remington, Reference Addington, Addington, Abidi, Raedler and Remington2017), it is difficult to know which FEP patients will be treatment resistant without spending a significant amount of time and resources and without extending patients’ suffering during trial and error. Moreover, in subjects at CHR for psychosis, although deciding the timing and intensity of intervention is more critical due to the nonspecific, heterogeneous, and changing nature of at-risk states, it is not easy to determine when to provide an intervention, which patients should receive an intervention, and the most suitable type of intervention to use due to the uncertain, diverse clinical outcomes of subjects at CHR for psychosis (Fusar-Poli et al., Reference Fusar-Poli, de Pablo, Correll, Meyer-Lindenberg, Millan, Borgwardt and Arango2020). In addition, existing studies proposing CHR risk calculators based on clinical and neurocognitive features suggest the importance of the incorporation of biomarkers, such as ERP components reflective of early psychosis states, to enhance model performance (Cannon et al., Reference Cannon, Yu, Addington, Bearden, Cadenhead, Cornblatt and Kattan2016; Oribe et al., Reference Oribe, Hirano, Del Re, Mesholam-Gately, Woodberry, Ueno and Niznikiewicz2020; Park et al., Reference Park, Lho, Hwang, Moon, Oh, Kim and Kwon2021; Schmidt et al., Reference Schmidt, Cappucciati, Radua, Rutigliano, Rocchetti, Dell'Osso and Fusar-Poli2017). Given that early psychosis periods exhibit a continuum of prodrome to FEP states, which share the common aspects of improved clinical outcomes by early interventions (Correll et al., Reference Correll, Galling, Pawar, Krivko, Bonetto, Ruggeri and Kane2018; Fusar-Poli et al., Reference Fusar-Poli, McGorry and Kane2017; Lieberman et al., Reference Lieberman, Perkins, Belger, Chakos, Jarskog, Boteva and Gilmore2001), common prognostic biomarkers that forecast prognosis across the early psychosis periods would provide valuable information for early intervention. In this regard, this study first suggested that MMN may serve as a common prognostic biomarker that should be included in future prediction model development to forecast clinical outcomes of early psychosis patients from CHR to FEP states.

This study has several limitations. First, medication use at the time of dMMN recording and the time to clinical outcome varied among FEP patients and CHR individuals; however, medication use at baseline and follow-up duration between clinical outcome groups were not different. Second, treatment resistance and nonremission were determined at a single last assessment that could not reflect changes outside the assessment points; thus, the findings should be interpreted with caution and potential biases should be considered. For example, a CHR individual who was classified as a remitter at the last follow-up assessment could subsequently become a nonremitter or transition to a psychotic disorder. Third, we used dMMN exclusively, unlike previous studies that used frequency-deviant MMN or double-deviant MMN (Bodatsch et al., Reference Bodatsch, Ruhrmann, Wagner, Muller, Schultze-Lutter, Frommann and Brockhaus-Dumke2011; Perez et al., Reference Perez, Woods, Roach, Ford, McGlashan, Srihari and Mathalon2014), which may be one of the causes of the negative results in the exploratory analysis for predicting the transition to psychotic disorder. Fourth, our sample size and the number of FEP-TR and transitioned CHR patients were small compared to other multisite studies, which may have diluted the effect of MMN on prognosis prediction. Considering that our sample was obtained from a single center (i.e. SYC), the sample size was relatively large, and our results are free from the issue of multicenter data stability. Nevertheless, large-scale multisite longitudinal studies with various MMN paradigms should be conducted to confirm the results of the current study.

To our knowledge, the present study is the first to investigate whether dMMN could serve as a common prognostic biomarker in early psychosis patients across the CHR to FEP. We observed that a reduced dMMN amplitude was already present at the baseline in poor clinical outcome groups and was predictive of treatment resistance in FEP patients and nonremission in subjects at CHR for psychosis. The present study suggests that reduced dMMN amplitude may support the clinical decision of early clozapine use for expected FEP-TR and the provision of rigorous treatment for potential CHR nonremission cases. In conclusion, the current study results provide information regarding dMMN as a common prognostic biomarker that could be added to existing prediction models based on clinical and neurocognitive features across early psychosis periods, which will aid in clinical decision-making for early interventions.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291721003068

Financial support

This research was supported by the Brain Research Program and the Basic Science Research Program through the National Research Foundation of Korea (NRF) and by the KBRI Basic Research Program through Korea Brain Research Institute, funded by the Ministry of Science, ICT & Future Planning (grant nos. 2017M3C7A1029610, 2019R1C1C1002457, 2020M3E5D9079910, and 21-BR-03-01).

Conflict of interest

None.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

References

Addington, J., Addington, D., Abidi, S., Raedler, T., & Remington, G. (2017). Canadian treatment guidelines for individuals at clinical high risk of psychosis. The Canadian Journal of Psychiatry, 62(9), 656661. doi:10.1177/0706743717719895.CrossRefGoogle ScholarPubMed
Allen, P., Chaddock, C. A., Egerton, A., Howes, O. D., Barker, G., Bonoldi, I., … McGuire, P. (2015). Functional outcome in people at high risk for psychosis predicted by thalamic glutamate levels and prefronto-striatal activation. Schizophrenia Bulletin, 41(2), 429439. doi:10.1093/schbul/sbu115.CrossRefGoogle ScholarPubMed
Allswede, D. M., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., … Cannon, T. D. (2020). Characterizing covariant trajectories of individuals at clinical high risk for psychosis across symptomatic and functional domains. American Journal of Psychiatry, 177(2), 164171. doi:10.1176/appi.ajp.2019.18111290.CrossRefGoogle ScholarPubMed
Birchwood, M., Todd, P., & Jackson, C. (1998). Early intervention in psychosis. The critical period hypothesis. The British Journal of Psychiatry, 172(33), 5359. doi:10.1192/S0007125000297663.CrossRefGoogle ScholarPubMed
Bodatsch, M., Ruhrmann, S., Wagner, M., Muller, R., Schultze-Lutter, F., Frommann, I., … Brockhaus-Dumke, A. (2011). Prediction of psychosis by mismatch negativity. Biological Psychiatry, 69(10), 959966. doi:10.1016/j.biopsych.2010.09.057.CrossRefGoogle ScholarPubMed
Bossong, M. G., Antoniades, M., Azis, M., Samson, C., Quinn, B., Bonoldi, I., … McGuire, P. (2019). Association of hippocampal glutamate levels with adverse outcomes in individuals at clinical high risk for psychosis. JAMA Psychiatry, 76(2), 199207. doi:10.1001/jamapsychiatry.2018.3252.CrossRefGoogle ScholarPubMed
Cannon, T. D., Chung, Y., He, G., Sun, D., Jacobson, A., van Erp, T. G., … North American Prodrome Longitudinal Study Consortium. (2015). Progressive reduction in cortical thickness as psychosis develops: A multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biological Psychiatry, 77(2), 147157. doi:10.1016/j.biopsych.2014.05.023.CrossRefGoogle ScholarPubMed
Cannon, T. D., Yu, C., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., … Kattan, M. W. (2016). An individualized risk calculator for research in prodromal psychosis. American Journal of Psychiatry, 173(10), 980988. doi:10.1176/appi.ajp.2016.15070890.CrossRefGoogle ScholarPubMed
Carrion, R. E., McLaughlin, D., Goldberg, T. E., Auther, A. M., Olsen, R. H., Olvet, D. M., … Cornblatt, B. A. (2013). Prediction of functional outcome in individuals at clinical high risk for psychosis. JAMA Psychiatry, 70(11), 11331142. doi:10.1001/jamapsychiatry.2013.1909.CrossRefGoogle ScholarPubMed
Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., … Tamminga, C. A. (2016). Identification of distinct psychosis biotypes using brain-based biomarkers. American Journal of Psychiatry, 173(4), 373384. doi:10.1176/appi.ajp.2015.14091200.CrossRefGoogle ScholarPubMed
Conley, R. R., & Buchanan, R. W. (1997). Evaluation of treatment-resistant schizophrenia. Schizophrenia Bulletin, 23(4), 663674. doi:10.1093/schbul/23.4.663.CrossRefGoogle ScholarPubMed
Correll, C. U., Galling, B., Pawar, A., Krivko, A., Bonetto, C., Ruggeri, M., … Kane, J. M. (2018). Comparison of early intervention services vs treatment as usual for early-phase psychosis: A systematic review, meta-analysis, and meta-regression. JAMA Psychiatry, 75(6), 555565. doi:10.1001/jamapsychiatry.2018.0623.CrossRefGoogle ScholarPubMed
Demjaha, A., Lappin, J. M., Stahl, D., Patel, M. X., MacCabe, J. H., Howes, O. D., … Murray, R. M. (2017). Antipsychotic treatment resistance in first-episode psychosis: Prevalence, subtypes and predictors. Psychological Medicine, 47(11), 19811989. doi:10.1017/S0033291717000435.CrossRefGoogle ScholarPubMed
Dempster, K., Jeon, P., MacKinley, M., Williamson, P., Theberge, J., & Palaniyappan, L. (2020). Early treatment response in first episode psychosis: A 7-T magnetic resonance spectroscopic study of glutathione and glutamate. Molecular Psychiatry, 25(8), 16401650. doi:10.1038/s41380-020-0704-x.CrossRefGoogle ScholarPubMed
de Wit, S., Ziermans, T. B., Nieuwenhuis, M., Schothorst, P. F., van Engeland, H., Kahn, R. S., … Schnack, H. G. (2017). Individual prediction of long-term outcome in adolescents at ultra-high risk for psychosis: Applying machine learning techniques to brain imaging data. Human Brain Mapping, 38(2), 704714. doi:10.1002/hbm.23410.CrossRefGoogle ScholarPubMed
Egerton, A., Stone, J. M., Chaddock, C. A., Barker, G. J., Bonoldi, I., Howard, R. M., … McGuire, P. K. (2014). Relationship between brain glutamate levels and clinical outcome in individuals at ultra high risk of psychosis. Neuropsychopharmacology, 39(12), 28912899. doi:10.1038/npp.2014.143.CrossRefGoogle ScholarPubMed
Erickson, M. A., Ruffle, A., & Gold, J. M. (2016). A meta-analysis of mismatch negativity in schizophrenia: From clinical risk to disease specificity and progression. Biological Psychiatry, 79(12), 980987. doi:10.1016/j.biopsych.2015.08.025.CrossRefGoogle ScholarPubMed
Fujioka, M., Kirihara, K., Koshiyama, D., Tada, M., Nagai, T., Usui, K., … Kasai, K. (2020). Mismatch negativity predicts remission and neurocognitive function in individuals at ultra-high risk for psychosis. Frontiers in Psychiatry, 11, 770. doi:10.3389/fpsyt.2020.00770.CrossRefGoogle ScholarPubMed
Fusar-Poli, P., Bechdolf, A., Taylor, M. J., Bonoldi, I., Carpenter, W. T., Yung, A. R., & McGuire, P. (2013). At risk for schizophrenic or affective psychoses? A meta-analysis of DSM/ICD diagnostic outcomes in individuals at high clinical risk. Schizophrenia Bulletin, 39(4), 923932. doi:10.1093/schbul/sbs060.CrossRefGoogle ScholarPubMed
Fusar-Poli, P., de Pablo, G. S., Correll, C. U., Meyer-Lindenberg, A., Millan, M. J., Borgwardt, S., … Arango, C. (2020). Prevention of psychosis: Advances in detection, prognosis, and intervention. JAMA Psychiatry, 77(7), 755765. doi:10.1001/jamapsychiatry.2019.4779.CrossRefGoogle Scholar
Fusar-Poli, P., McGorry, P. D., & Kane, J. M. (2017). Improving outcomes of first-episode psychosis: An overview. World Psychiatry, 16(3), 251265. doi:10.1002/wps.20446.CrossRefGoogle ScholarPubMed
Gardner, D. M., Murphy, A. L., O'Donnell, H., Centorrino, F., & Baldessarini, R. J. (2010). International consensus study of antipsychotic dosing. American Journal of Psychiatry, 167(6), 686693. doi:10.1176/appi.ajp.2009.09060802.CrossRefGoogle ScholarPubMed
Garrido, M. I., Kilner, J. M., Stephan, K. E., & Friston, K. J. (2009). The mismatch negativity: A review of underlying mechanisms. Clinical Neurophysiology, 120(3), 453463. doi:10.1016/j.clinph.2008.11.029.CrossRefGoogle ScholarPubMed
Haigh, S. M., Coffman, B. A., & Salisbury, D. F. (2017). Mismatch negativity in first-episode schizophrenia: A meta-analysis. Clinical EEG and Neuroscience, 48(1), 310. doi:10.1177/1550059416645980.CrossRefGoogle ScholarPubMed
Hall, R. C. (1995). Global assessment of functioning. A modified scale. Psychosomatics, 36(3), 267275. doi:10.1016/S0033-3182(95)71666-8.CrossRefGoogle ScholarPubMed
Hamilton, H. K., Boos, A. K., & Mathalon, D. H. (2020). Electroencephalography and event-related potential biomarkers in individuals at clinical high risk for psychosis. Biological Psychiatry, 88(4), 294303. doi:10.1016/j.biopsych.2020.04.002.CrossRefGoogle ScholarPubMed
Hamilton, H. K., Roach, B. J., Bachman, P. M., Belger, A., Carrion, R. E., Duncan, E., … Mathalon, D. H. (2019). Association between P300 responses to auditory oddball stimuli and clinical outcomes in the psychosis risk syndrome. JAMA Psychiatry, 76(11), 11871197. doi:10.1001/jamapsychiatry.2019.2135.CrossRefGoogle ScholarPubMed
Higgins, A., Lewandowski, K. E., Liukasemsarn, S., & Hall, M. H. (2021). Longitudinal relationships between mismatch negativity, cognitive performance, and real-world functioning in early psychosis. Schizophrenia Research, 228, 385393. doi:10.1016/j.schres.2021.01.009.CrossRefGoogle ScholarPubMed
Ho, N. F., Holt, D. J., Cheung, M., Iglesias, J. E., Goh, A., Wang, M., … Zhou, J. (2017). Progressive decline in hippocampal CA1 volume in individuals at ultra-high-risk for psychosis who do not remit: Findings from the longitudinal youth at risk study. Neuropsychopharmacology, 42(6), 13611370. doi:10.1038/npp.2017.5.CrossRefGoogle Scholar
Howes, O. D., McCutcheon, R., Agid, O., de Bartolomeis, A., van Beveren, N. J., Birnbaum, M. L., … Correll, C. U. (2017). Treatment-resistant schizophrenia: Treatment response and resistance in psychosis (TRRIP) working group consensus guidelines on diagnosis and terminology. American Journal of Psychiatry, 174(3), 216229. doi:10.1176/appi.ajp.2016.16050503.CrossRefGoogle Scholar
Javitt, D. C., & Freedman, R. (2015). Sensory processing dysfunction in the personal experience and neuronal machinery of schizophrenia. American Journal of Psychiatry, 172(1), 1731. doi:10.1176/appi.ajp.2014.13121691.CrossRefGoogle ScholarPubMed
Javitt, D. C., Steinschneider, M., Schroeder, C. E., & Arezzo, J. C. (1996). Role of cortical N-methyl-D-aspartate receptors in auditory sensory memory and mismatch negativity generation: Implications for schizophrenia. Proceedings of the National Academy of Sciences of the United States of America, 93(21), 1196211967. doi:10.1073/pnas.93.21.11962.CrossRefGoogle Scholar
Jung, M. H., Jang, J. H., Kang, D. H., Choi, J. S., Shin, N. Y., Kim, H. S., … Kwon, J. S. (2010). The reliability and validity of the Korean version of the structured interview for prodromal syndrome. Psychiatry Investigation, 7(4), 257263. doi:10.4306/pi.2010.7.4.257.CrossRefGoogle ScholarPubMed
Kim, M., Cho, K. I., Yoon, Y. B., Lee, T. Y., & Kwon, J. S. (2017). Aberrant temporal behavior of mismatch negativity generators in schizophrenia patients and subjects at clinical high risk for psychosis. Clinical Neurophysiology, 128(2), 331339. doi:10.1016/j.clinph.2016.11.027.CrossRefGoogle ScholarPubMed
Kim, M., Lee, T. H., Yoon, Y. B., Lee, T. Y., & Kwon, J. S. (2018). Predicting remission in subjects at clinical high risk for psychosis using mismatch negativity. Schizophrenia Bulletin, 44(3), 575583. doi:10.1093/schbul/sbx102.CrossRefGoogle ScholarPubMed
Kim, M., Lee, T. Y., Lee, S., Kim, S. N., & Kwon, J. S. (2015). Auditory P300 as a predictor of short-term prognosis in subjects at clinical high risk for psychosis. Schizophrenia Research, 165(2–3), 138144. doi:10.1016/j.schres.2015.04.033.CrossRefGoogle ScholarPubMed
Koutsouleris, N., Riecher-Rossler, A., Meisenzahl, E. M., Smieskova, R., Studerus, E., Kambeitz-Ilankovic, L., … Borgwardt, S. (2015). Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers. Schizophrenia Bulletin, 41(2), 471482. doi:10.1093/schbul/sbu078.CrossRefGoogle ScholarPubMed
Koutsouleris, N., Upthegrove, R., & Wood, S. J. (2019). Importance of variable selection in multimodal prediction models in patients at clinical high risk for psychosis and recent onset depression-reply. JAMA Psychiatry, 76(3), 339340. doi:10.1001/jamapsychiatry.2018.4237.CrossRefGoogle ScholarPubMed
Kwon, J. S., Byun, M. S., Lee, T. Y., & An, S. K. (2012). Early intervention in psychosis: Insights from Korea. Asian Journal of Psychiatry, 5(1), 98105. doi:10.1016/j.ajp.2012.02.007.CrossRefGoogle ScholarPubMed
Lally, J., Ajnakina, O., Di Forti, M., Trotta, A., Demjaha, A., Kolliakou, A., … Murray, R. M. (2016). Two distinct patterns of treatment resistance: Clinical predictors of treatment resistance in first-episode schizophrenia spectrum psychoses. Psychological Medicine, 46(15), 32313240. doi:10.1017/S0033291716002014.CrossRefGoogle ScholarPubMed
Lappin, J. M., Morgan, C., Chalavi, S., Morgan, K. D., Reinders, A. A., Fearon, P., … Dazzan, P. (2014). Bilateral hippocampal increase following first-episode psychosis is associated with good clinical, functional and cognitive outcomes. Psychological Medicine, 44(6), 12791291. doi:10.1017/S0033291713001712CrossRefGoogle ScholarPubMed
Lho, S. K., Kim, M., Lee, T. H., Kwak, Y. B., & Kwon, J. S. (2019). Predicting prognosis in patients with first-episode psychosis using auditory P300: A 1-year follow-up study. Clinical Neurophysiology, 130(1), 4654. doi:10.1016/j.clinph.2018.10.011.CrossRefGoogle ScholarPubMed
Lho, S. K., Kim, M., Park, J., Hwang, W. J., Moon, S. Y., Oh, S., & Kwon, J. S. (2020). Progressive impairment of mismatch negativity is reflective of underlying pathophysiological changes in patients with first-episode psychosis. Frontiers in Psychiatry, 11, 587. doi:10.3389/fpsyt.2020.00587.CrossRefGoogle ScholarPubMed
Lieberman, J. A. (1993). Prediction of outcome in first-episode schizophrenia. The Journal of Clinical Psychiatry, 54(Suppl), 1317.Google ScholarPubMed
Lieberman, J. A., Perkins, D., Belger, A., Chakos, M., Jarskog, F., Boteva, K., & Gilmore, J. (2001). The early stages of schizophrenia: Speculations on pathogenesis, pathophysiology, and therapeutic approaches. Biological Psychiatry, 50(11), 884897. doi:10.1016/s0006-3223(01)01303-8.CrossRefGoogle ScholarPubMed
Lin, A., Wood, S. J., Nelson, B., Beavan, A., McGorry, P., & Yung, A. R. (2015). Outcomes of nontransitioned cases in a sample at ultra-high risk for psychosis. American Journal of Psychiatry, 172(3), 249258. doi:10.1176/appi.ajp.2014.13030418.CrossRefGoogle Scholar
Marshall, M., Lewis, S., Lockwood, A., Drake, R., Jones, P., & Croudace, T. (2005). Association between duration of untreated psychosis and outcome in cohorts of first-episode patients: A systematic review. Archives of General Psychiatry, 62(9), 975983. doi:10.1001/archpsyc.62.9.975.CrossRefGoogle ScholarPubMed
Mi, L., Wang, L., Li, X., She, S., Li, H., Huang, H., … Zheng, Y. (2021). Reduction of phonetic mismatch negativity may depict illness course and predict functional outcomes in schizophrenia. Journal of Psychiatric Research, 137, 290297. doi:10.1016/j.jpsychires.2021.02.065.CrossRefGoogle Scholar
Miller, T. J., McGlashan, T. H., Rosen, J. L., Cadenhead, K., Cannon, T., Ventura, J., … Woods, S. W. (2003). Prodromal assessment with the structured interview for prodromal syndromes and the scale of prodromal symptoms: Predictive validity, interrater reliability, and training to reliability. Schizophrenia Bulletin, 29(4), 703715. doi:10.1093/oxfordjournals.schbul.a007040.CrossRefGoogle ScholarPubMed
Miller, T. J., McGlashan, T. H., Rosen, J. L., Somjee, L., Markovich, P. J., Stein, K., & Woods, S. W. (2002). Prospective diagnosis of the initial prodrome for schizophrenia based on the structured interview for prodromal syndromes: Preliminary evidence of interrater reliability and predictive validity. American Journal of Psychiatry, 159(5), 863865. doi:10.1176/appi.ajp.159.5.863.CrossRefGoogle ScholarPubMed
Mouchlianitis, E., Bloomfield, M. A., Law, V., Beck, K., Selvaraj, S., Rasquinha, N., … Howes, O. D. (2016). Treatment-resistant schizophrenia patients show elevated anterior cingulate cortex glutamate compared to treatment-responsive. Schizophrenia Bulletin, 42(3), 744752. doi:10.1093/schbul/sbv151.CrossRefGoogle ScholarPubMed
Naatanen, R., & Escera, C. (2000). Mismatch negativity: Clinical and other applications. Audiology and Neurotology, 5(3–4), 105110. doi:10.1159/000013874.CrossRefGoogle ScholarPubMed
Nagai, T., Tada, M., Kirihara, K., Yahata, N., Hashimoto, R., Araki, T., & Kasai, K. (2013). Auditory mismatch negativity and P3a in response to duration and frequency changes in the early stages of psychosis. Schizophrenia Research, 150(2–3), 547554. doi:10.1016/j.schres.2013.08.005.CrossRefGoogle Scholar
Oh, S., Kim, M., Kim, T., Lee, T. Y., & Kwon, J. S. (2020). Resting-state functional connectivity of the striatum predicts improvement in negative symptoms and general functioning in patients with first-episode psychosis: A 1-year naturalistic follow-up study. Australian & New Zealand Journal of Psychiatry, 54(5), 509518. doi:10.1177/0004867419885452.CrossRefGoogle ScholarPubMed
Oribe, N., Hirano, Y., Del Re, E., Mesholam-Gately, R. I., Woodberry, K. A., Ueno, T., … Niznikiewicz, M. A. (2020). Longitudinal evaluation of visual P300 amplitude in clinical high-risk subjects: An event-related potential study. Psychiatry and Clinical Neurosciences, 74(10), 527534. doi:10.1111/pcn.13083.CrossRefGoogle Scholar
Palaniyappan, L., Marques, T. R., Taylor, H., Handley, R., Mondelli, V., Bonaccorso, S., … Dazzan, P. (2013). Cortical folding defects as markers of poor treatment response in first-episode psychosis. JAMA Psychiatry, 70(10), 10311040. doi:10.1001/jamapsychiatry.2013.203.CrossRefGoogle ScholarPubMed
Park, J., Lho, S. K., Hwang, W. J., Moon, S. Y., Oh, S., Kim, M., … Kwon, J. S. (2021). Impaired error-related processing in patients with first-episode psychosis and subjects at clinical high risk for psychosis: An event-related potential study. Psychiatry and Clinical Neuroscience. 75, 219–226. doi:10.1111/pcn.13219.CrossRefGoogle ScholarPubMed
Perez, V. B., Woods, S. W., Roach, B. J., Ford, J. M., McGlashan, T. H., Srihari, V. H., & Mathalon, D. H. (2014). Automatic auditory processing deficits in schizophrenia and clinical high-risk patients: Forecasting psychosis risk with mismatch negativity. Biological Psychiatry, 75(6), 459469. doi:10.1016/j.biopsych.2013.07.038.CrossRefGoogle ScholarPubMed
Perkins, D. O., Gu, H., Boteva, K., & Lieberman, J. A. (2005). Relationship between duration of untreated psychosis and outcome in first-episode schizophrenia: A critical review and meta-analysis. American Journal of Psychiatry, 162(10), 17851804. doi:10.1176/appi.ajp.162.10.1785.CrossRefGoogle ScholarPubMed
Rapado-Castro, M., Dodd, S., Bush, A. I., Malhi, G. S., Skvarc, D. R., On, Z. X., … Dean, O. M. (2017). Cognitive effects of adjunctive N-acetyl cysteine in psychosis. Psychological Medicine, 47(5), 866876. doi:10.1017/S0033291716002932.CrossRefGoogle ScholarPubMed
Renaldi, R., Kim, M., Lee, T. H., Kwak, Y. B., Tanra, A. J., & Kwon, J. S. (2019). Predicting symptomatic and functional improvements over 1 year in patients with first-episode psychosis using resting-state electroencephalography. Psychiatry Investigation, 16(9), 695703. doi:10.30773/pi.2019.06.20.1.CrossRefGoogle ScholarPubMed
Reniers, R. L., Lin, A., Yung, A. R., Koutsouleris, N., Nelson, B., Cropley, V. L., … Wood, S. J. (2017). Neuroanatomical predictors of functional outcome in individuals at ultra-high risk for psychosis. Schizophrenia Bulletin, 43(2), 449458. doi:10.1093/schbul/sbw086.Google ScholarPubMed
Sarpal, D. K., Argyelan, M., Robinson, D. G., Szeszko, P. R., Karlsgodt, K. H., John, M., … Malhotra, A. K. (2016). Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. American Journal of Psychiatry, 173(1), 6977. doi:10.1176/appi.ajp.2015.14121571.CrossRefGoogle ScholarPubMed
Schlosser, D. A., Jacobson, S., Chen, Q., Sugar, C. A., Niendam, T. A., Li, G., … Cannon, T. D. (2012). Recovery from an at-risk state: Clinical and functional outcomes of putatively prodromal youth who do not develop psychosis. Schizophrenia Bulletin, 38(6), 12251233. doi:10.1093/schbul/sbr098.CrossRefGoogle Scholar
Schmidt, A., Cappucciati, M., Radua, J., Rutigliano, G., Rocchetti, M., Dell'Osso, L., … Fusar-Poli, P. (2017). Improving prognostic accuracy in subjects at clinical high risk for psychosis: Systematic review of predictive models and meta-analytical sequential testing simulation. Schizophrenia Bulletin, 43(2), 375388. doi:10.1093/schbul/sbw098.Google ScholarPubMed
Semlitsch, H. V., Anderer, P., Schuster, P., & Presslich, O. (1986). A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology, 23(6), 695703. doi:10.1111/j.1469-8986.1986.tb00696.x.CrossRefGoogle ScholarPubMed
Simon, A. E., Borgwardt, S., Riecher-Rossler, A., Velthorst, E., de Haan, L., & Fusar-Poli, P. (2013). Moving beyond transition outcomes: Meta-analysis of remission rates in individuals at high clinical risk for psychosis. Psychiatry Research, 209(3), 266272. doi:10.1016/j.psychres.2013.03.004.CrossRefGoogle ScholarPubMed
Swerdlow, N. R., Bhakta, S., Chou, H. H., Talledo, J. A., Balvaneda, B., & Light, G. A. (2016). Memantine effects on sensorimotor gating and mismatch negativity in patients with chronic psychosis. Neuropsychopharmacology, 41(2), 419430. doi:10.1038/npp.2015.162.CrossRefGoogle ScholarPubMed
Tateno, T., Higuchi, Y., Nakajima, S., Sasabayashi, D., Nakamura, M., Ueno, M., … Suzuki, M. (2021). Features of duration mismatch negativity around the onset of overt psychotic disorders: A longitudinal study. Cerebral Cortex, 31(5), 24162424. doi:10.1093/cercor/bhaa364.CrossRefGoogle ScholarPubMed
Uno, Y., & Coyle, J. T. (2019). Glutamate hypothesis in schizophrenia. Psychiatry and Clinical Neuroscience, 73(5), 204215. doi:10.1111/pcn.12823.CrossRefGoogle Scholar
Yung, A. R., Yuen, H. P., McGorry, P. D., Phillips, L. J., Kelly, D., Dell'Olio, M., … Buckby, J. (2005). Mapping the onset of psychosis: The comprehensive assessment of at-risk mental states. Australian & New Zealand Journal of Psychiatry, 39(11–12), 964971. doi:10.1080/j.1440-1614.2005.01714.x.CrossRefGoogle ScholarPubMed
Zheng, W., Zhang, Q. E., Cai, D. B., Yang, X. H., Qiu, Y., Ungvari, G. S., … Xiang, Y. T. (2018). N-acetylcysteine for major mental disorders: A systematic review and meta-analysis of randomized controlled trials. Acta Psychiatrica Scandinavica, 137(5), 391400. doi:10.1111/acps.12862.CrossRefGoogle ScholarPubMed
Figure 0

Table 1. Demographic, clinical, and duration deviant mismatch negativity (dMMN) characteristics of the participants at baseline

Figure 1

Fig. 1. (a) Grand-averaged duration deviant mismatch negativity (dMMN) waveforms across the patients with first-episode psychosis (FEP), subjects at clinical high risk (CHR) for psychosis, and healthy controls (HCs) at the FCz electrode site. (b) dMMN amplitude across the groups at the FCz electrode site. Horizontal lines in groups indicate means, and vertical lines indicate 95% confidence intervals. *Indicates statistical significance at p < 0.05. **Indicates statistical significance at p < 0.005. (c) Two-dimensional topographic maps of dMMN in FEP patients, CHR individuals, and HCs. The colored bar with numbers indicates the dMMN amplitude (μV).

Figure 2

Fig. 2. (a) Grand-averaged duration deviant mismatch negativity (dMMN) waveforms across the patients with first-episode psychosis (FEP) who were treatment resistant (FEP-TR) and those who were not (FEP-nonTR) at the FCz electrode site. (b) dMMN amplitude across the clinical outcome groups at the FCz electrode site. Horizontal lines in groups indicate means, and vertical lines indicate 95% confidence intervals. *Indicates statistical significance at p < 0.05. (c) Two-dimensional topographic maps of dMMN in FEP-TR and FEP-nonTR patients. The colored bar with numbers indicates the amplitude of the dMMN (μV).

Figure 3

Fig. 3. (a) Grand-averaged duration deviant mismatch negativity (dMMN) waveforms across the individuals at clinical high risk (CHR) for psychosis who were remitted and those who were not at the FCz electrode site. (b) dMMN amplitude across the clinical outcome groups at the FCz electrode site. Horizontal lines in groups indicate means, and vertical lines indicate 95% confidence intervals. *Indicates statistical significance at p < 0.05. (c) Two-dimensional topographic maps of dMMN in CHR remitters and CHR nonremitters. The colored bar with numbers indicates the amplitude of the dMMN (μV).

Figure 4

Table 2. Significant predictors of treatment resistance in first-episode psychosis (FEP) patients and nonremission or transition to psychotic disorder in subjects at clinical high risk (CHR) for psychosis in binary logistic regression analysis with the backward selection method

Supplementary material: File

Kim et al. supplementary material

Figures S1-S3 and Tables S1-S7

Download Kim et al. supplementary material(File)
File 409 KB
You have Access
Open access

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Forecasting prognostic trajectories with mismatch negativity in early psychosis
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Forecasting prognostic trajectories with mismatch negativity in early psychosis
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Forecasting prognostic trajectories with mismatch negativity in early psychosis
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *