Frontal and Parietal Alpha Asymmetry as Biomarkers for Negative Symptoms in Schizophrenia

Yao-Cheng Wu; Chih-Chung Huang; Yi-Guang Wang; Chu-Ya Yang; Wei-Chou Chang; Chuan-Chia Chang; Hsin-An Chang

doi:10.30773/pi.2024.0354

Psychiatry Investig > Volume 22(4); 2025 > Article

Wu, Huang, Wang, Yang, Chang, Chang, and Chang: Frontal and Parietal Alpha Asymmetry as Biomarkers for Negative Symptoms in Schizophrenia

Original Article

Psychiatry Investigation 2025;22(4):435-441.

Published online: April 11, 2025

DOI: https://doi.org/10.30773/pi.2024.0354

Frontal and Parietal Alpha Asymmetry as Biomarkers for Negative Symptoms in Schizophrenia

Yao-Cheng Wu¹

, Chih-Chung Huang¹

, Yi-Guang Wang¹

, Chu-Ya Yang¹

, Wei-Chou Chang²

, Chuan-Chia Chang¹

, Hsin-An Chang^1,³

¹Department of Psychiatry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan

²Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan

³Non-Invasive Neuromodulation Consortium for Mental Disorders, Society of Psychophysiology, Taipei, Taiwan

Correspondence: Hsin-An Chang, MD Department of Psychiatry, Tri-Service General Hospital, No. 325, Cheng-Kung Road, Sec. 2, Nei-Hu District, Taipei 114, Taiwan Tel: +886-2-8792-7220, Fax: +886-2-8792-7221 E-mail: chang.ha@mail.ndmctsgh.edu.tw

Correspondence: Chuan-Chia Chang, MD, PhD Department of Psychiatry, Tri-Service General Hospital, No. 325, Cheng-Kung Road, Sec. 2, Nei-Hu District, Taipei 114, Taiwan Tel: +886-2-8792-7220, Fax: +886-2-8792-7221 E-mail: changcc@mail.ndmctsgh.edu.tw

Received November 21, 2024 Revised January 19, 2025 Accepted February 6, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

Negative symptoms in schizophrenia indicate a poor prognosis. However, the mechanisms underlying the development of negative symptoms remain unclear. This study investigated the relationship between negative symptoms in schizophrenia and frontal alpha asymmetry (FAA).

Methods

The study used a 32-channel electroencephalography to acquire alpha power in 4 target-paired sites in each patient. Regional alpha asymmetry was calculated based on the alpha power using EEGLAB Frontal Alpha Asymmetry Toolbox.

Results

Sixty schizophrenia patients with predominant negative symptoms (PNS), 72 stabilized schizophrenia (SS) patients, and 73 healthy control (HC) participants were enrolled in this study. No significant differences were observed in FAA between the PNS and SS groups, although both groups exhibited reduced P3-P4 alpha asymmetry compared to HCs. A positive correlation was found between F7-F8 alpha asymmetry and illness duration. Additionally, a predictive model based on P3-P4 alpha asymmetry scores was able to differentiate schizophrenia patients from HCs, achieving a sensitivity of 71.2% and a specificity of 72.6%.

Conclusion

This study highlighted that parietal alpha asymmetry could serve as a valuable diagnostic tool for schizophrenia.

Keywords: Schizophrenia, Negative symptoms, EEG, Functional laterality, Alpha rhythm

INTRODUCTION

Negative symptoms in schizophrenia, including avolition, anhedonia, asociality, alogia, and blunted affect, refer to absence or reduction in affect, cognition, motivation, and volition. These symptoms often emerge during the prodromal phase of schizophrenia and not only impair patients’ functioning but also indicate a poor prognosis [1,2]. Currently, possible mechanisms for negative symptoms include dysregulation of the dopaminergic system, neuroinflammatory responses, abnormalities in the glutamatergic system, and impaired prefrontal cortex function. However, the mechanisms underlying the development of negative symptoms remain unclear. There is a need for novel pathophysiological markers that correlate with negative symptoms, with the goal of enabling for early intervention and prevention.

Numerous genes have been utilized to predict the risk of schizophrenia [3,4], particularly regarding the manifestation of negative symptoms, with genetic prediction models enhancing our understanding of individual susceptibility and laying the foundation for personalized treatment approaches. However, additional research is required to clarify the prioritized genes and risk variants associated with schizophrenia. Previous research employed a validated electroencephalography (EEG) measure called frontal alpha asymmetry (FAA) to study the motivational system associated with negative symptoms [5]. This measure assessed the lateralization of alpha power in the right or left frontal regions, with alpha power being inversely relat-ed to neural activity [6]. Higher left lateralized activity (i.e., increased right alpha power) was believed to reflect positive affect and approach tendencies, while higher right lateralized activity (i.e., increased left alpha power) was thought to indicate negative affect and withdrawal. Nonetheless, although some studies have reported associations between FAA and negative symptoms in schizophrenia, findings have been inconsistent. Horan et al. [5] found that schizophrenic patients demonstrated decreased left frontal activation compared to controls, aligning with reduced approach motivation. Jang et al. [7] suggested patients with schizophrenia showed significantly lower left FAA than healthy controls (HCs). Bartolomeo et al. [8] suggested that individuals at clinical high risk (CHR) for psychosis and HCs did not differ in FAA scores. Overall, these studies highlight the potential of EEG-based biomarkers, such as frontal and parietal alpha asymmetry in identifying neural dysfunction, aiding in early diagnosis, tracking disease progression, and guiding personalized interventions. Further validation of these biomarkers could enhance the quality in clinical practice and research, improving outcomes for individuals with schizophrenia.

Hence, the primary aim of the current study was to examine whether schizophrenic patients who exhibited more prominent negative symptoms would show relatively more right frontal lateralization (i.e., a lower FAA score). We also investigated whether FAA score correlated to negative symptom severity. Given a research gap regarding how chronicity of schizophrenia impacts FAA [7], we further explored the relationship between duration of illness and FAA score.

METHODS

Participants

This is a secondary analysis of the baseline EEG data from clinical trials investigating the efficacy of transcranial electrical stimulation (tES) in treating patients aged 20-65 years who met Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnostic criteria for schizophrenia [9-13]. These trials and all experimental procedures conformed to the standards set by the latest revision of the Declaration of Helsinki and were approved by the ethics committee of Tri-Service General Hospital (codes: 2-106-05-123, 2-107-03-001, 1-108-03-002 and 1-106-05-001). Written informed consent was obtained from all subjects. The inclusion criteria for schizophrenia patients with predominant negative symptoms (PNS) were: 1) patients aged 20-65 with the DSM-5-defined schizophrenia or schizoaffective disorder, 2) duration of illness ≥1 year, 3) with a clinical presentation of PNS, and 4) receiving stable antipsychotic drug regimen >8 weeks. The exclusion criteria were: 1) subjects with current psychiatric comorbidity or active substance use disorder with the exception of caffeine and/or tobacco, 2) having contraindications for transcranial direct current stimulation, e.g., implanted brain medical devices or metal in the head, 3) pregnancy at enrollment, and 4) having a history of seizures, intracranial neoplasms or surgery, severe head injuries, or cerebrovascular diseases. Subjects with SS came from the pooled baseline data of two samples participating in independent clinical trials. Detailed inclusion and exclusion criteria can be found in the Supplementary Materials. The clinician-rated Positive and Negative Syndrome Scale was used to measure the severity of positive, negative, and general psychopathology of schizophrenia [14]. Almost all patients in the study were taking second-generation antipsychotic drugs. The daily dosage of antipsychotic drugs equivalent to 100 mg chlorpromazine was calculated for subsequent comparisons [15]. Recruitment and exclusion processes have been described elsewhere. [16] Briefly, all healthy participants received a medical checkup at Tri-Servie General Hospital (TSGH) that included biochemical analyses, blood pressure measurement, electrocardiography, physical examination and thoracic radiography. None of them had any organic diseases, including kidney or liver disease, cardiovascular disease, metabolic disorders, neurologic disorders, malignancy, or obesity. They were also free of mental disorders based on the assessment using the Chinese Version of the Mini-International Neuropsychiatric Interview by a well-trained research assistant [17]. In addition, none of them had been taking any medications, as determined by self-report, for at least 1 month before entering the study.

Resting-state EEG acquisition and preprocessing

The participants sat comfortably in a recliner in a light, sound attenuated room and were instructed to stay awake and relaxed in a state of mind wandering with their eyes closed for limiting eye movements during the recording. Resting-state eyes-closed EEG was acquired for five minutes with either 32-channel Neuro Prax^® TMS/tES compatible full band DCEEG system (NeuroConn GmbH) using EEG electrode cap (NP32, GmbH) or 32-channel Neuroscan NuAmp System (Neuroscan, Compumedics USA) using EEG electrode cap (Quikcap; NuAmps) with Ag/AgCl sintered ring electrodes placed according to the extended international 10-20 system. The ground electrode was placed at Fpz. The recording channels were connected to a reference electrode located at the tip of the nose. The impedance of each electrode was checked to remain below 5 kΩ. Offline, the data were referenced to averaged mastoids downsampled to 256 Hz, band-pass filtered to 1-100 Hz with the Finite Impulse Response method and analog 60 Hz-notch filtered using EEGLAB v2020.0 [18]. Bad channels were automatically detected and removed based on artifact subspace reconstruction [19]. Independent component analysis followed by ICLabel was used to automatically remove artifacts caused by muscle activity, heartbeats, eye movements, and eye blinks [20]. A spline interpolation was used to interpolate bad channels [21].

Alpha asymmetry calculation

Research indicates FAA indices were found to be more reliable in an eyes-closed condition relative to an eyes-open condition [22]. In this study, power spectrum was calculated using the Fast Fourier transformation (FFT) and averaged in an eyes-closed condition. Conventional FFT-derived alpha power (8-13 Hz) was obtained at the 4 target-paired sites: Fp1-Fp2, F3-F4, F7-F8, and P3-P4 (i.e., fontopolar, mid-frontal, lateral frontal, parietal regions, respectively) (Figure 1). EEBLAB Frontal Alpha Asymmetry Toolbox was used to calculate regional alpha asymmetry from log-transformed EEG alpha power in the left hemisphere and from log-transformed power at homologous sites in the right hemisphere [5]. Alpha asymmetry score can be calculated as follows:

FAA=mean (|log (Power_Right) - log (Power_Left)|)

A higher alpha asymmetry scores reflect greater left-side activity, which is also associated with the opposite of negative symptoms, as a higher value of alpha band power usually indicates less cortical activity and vice versa [6].

Statistics

Statistical analyses were performed either using IBM SPSS Statistics 24.0 software (IBM Corp.). Demographics and clinical characteristics between participant groups were tested using analysis of variance or chi-squared tests. Comparisons of alpha asymmetry were performed using multivariate analysis of covariance (MANCOVA). The groups constituted the between-subject factors. Age and sex were considered as covariates. Any significant difference was followed by Bonferroniadjusted post-hoc analysis. Partial correlations between alpha asymmetry and clinical variables were analyzed to account for age and sex. Statistical significance was defined as p<0.05 (two-tailed). The p-values for multiple comparisons were corrected using the Bonferroni method. A binary logistic regression analysis was used in the participants with schizophrenia and HCs to determine the optimal model for the prediction of schizophrenia patients. Nagelkerke’s R² was used to approximate the percent of variance explained by the model. The area under the receiver-operating characteristic (ROC) curve (AUC) was used to determine the predictive power of the logistic model. The predicted probability with the highest Youden index was selected as the optimal cut-off point.

RESULTS

Sample characteristics

The study included 60 schizophrenia patients with PNS, 72 stabilized schizophrenia (SS) patients, and 73 HC participants. The demographic and clinical characteristics revealed no significant differences in age and sex across the three groups. However, schizophrenia patients with PNS and SS had lower education levels compared to HC (Table 1). The PNS and SS groups did not differ in onset age, duration of illness, antipsychotic doses, positive and general psychopathology severity, though the PNS group exhibited greater negative symptoms than the SS group.

Alpha asymmetry score

The results of MANCOVA indicated there were statistically significant mean differences in alpha asymmetry between both schizophrenia groups and HCs after adjusting age and sex. A significant between-subjects difference in P3-P4 was observed (F=9.61, p<0.001, ηp²=0.088). However, there were no significant differences in Fp1-Fp2 (F=2.02, p=0.14, ηp²=0.02), F3-F4 (F=0.75, p=0.47, ηp²=0.007), and F7-F8 (F=0.24, p=0.79, ηp²=0.002) among groups. Compared to HCs, P3-P4 alpha asymmetry in both PNS group (corrected p<0.001, 95% confidence interval [CI]=0.05 to 0.22) and SS group (corrected p=0.001, 95% CI=0.05 to 0.22) was reduced (Figure 2). Additionally, MANCOVA was used to compare alpha asymmetry between PNS group and SS group with age, sex, duration of illness, and antipsychotic doses as covariates. There were no significant between-group differences in Fp1-Fp2 (F=1.14, p=0.29, ηp²=0.01), F3-F4 (F=1.03, p=0.31, ηp²=0.01), F7-F8 (F< 0.01, p=0.98, ηp²<0.001), and P3-P4 (F=0.09, p=0.77, ηp²=0.001) alpha asymmetry.

Correlation analyses

In correlation analysis among all patients with schizophrenia (n=132), there were no significant associations between alpha asymmetry (Fp1-Fp2, F7-F8, F3-F4, and P3-P4) and negative symptoms (all p-values>0.05) (Supplementary Table 1). In exploratory analyses, F7-F8 alpha asymmetry score was positively correlated with duration of illness (r=0.32, p<0.001) (Figure 3). The correlation remained significant after adjusting age and sex (r=0.22, p=0.011).

Mode of differentiation between schizophrenia patients and HCs

Results are shown in Table 2. The Nagelkerke R² values showed that 12.4% of the variance was explained by the model of P3-P4 alpha asymmetry score. ROC is based on the relationships of sensitivity (true positive findings associated with schizophrenia patients) vs. 1-specificity (false positive association with HCs). The AUC was 0.77 (CI: 0.70-0.84), suggesting that the model of P3-P4 alpha asymmetry score is fair to predict schizophrenia patients vs. HCs (Supplementary Figure 1). The following prediction model was derived: predicted probability=1/[1+exp(3.53-4.60×log P3-P4 alpha asymmetry score value)]. At the optimal log P3-P4 alpha asymmetry score value cut-off point of 1.1514 determined by the Youden index, the sensitivity and specificity of differentiating schizophrenia patients from HCs were 71.2% and 72.6%, respectively.

DISCUSSION

Although many studies have suggested a potential association between frontal lateralization (with alpha asymmetry score as the primary index) and the negative symptoms of schizophrenia, the results were inconsistent [5,7]. Contrary to previous research, we did not find significant differences in FAA between PNS and SS groups. However, we found that both the PNS and SS groups showed significant differences in alpha asymmetry at P3-P4 compared to HCs, while no differences were observed at Fp1-Fp2, F3-F4, or F7-F8.

Alpha asymmetry based on the frontal lobe has been demonstrated in patients with schizophrenia or psychosis in previous studies. Among these studies, three found reduced left lateralization in patients with schizophrenia, with two showing lower left FAA when measured at F4-F3 [5,7] and one showing greater left lateralization of alpha power at F4-F3 in patients with schizophrenia [23]. Another study found that youth at CHR for psychosis did not differ in FAA scores from HCs [8].

Building on our findings and previous research, we propose that during the prodromal phase of schizophrenia, such as in individuals at CHR, FAA does not exhibit significant changes. However, as the disease progresses, FAA gradually decreases. In the present study, higher doses of antipsychotic treatment (chlorpromazine equivalent dose around 600 mg/day) appeared to restore FAA to levels comparable to those of HCs. Despite this, treatment has its limitations. In a longer-term study which had a duration of illness around 27 years, a subsequent decline in FAA was observed, possibly linked to compensation failure [5]. This decline may be driven by several underlying pathophysiological mechanisms also implicated in treatmentresistant schizophrenia, including dopamine supersensitivity, hyperdopaminergic and normodopaminergic subtypes, glutamate dysregulation, inflammation and oxidative stress, as well as serotonin dysregulation [24]. This suggests that over time, the therapeutic effects and compensatory mechanisms may gradually diminish.

In the present study, we reported that F7-F8 alpha asymmetry score was positively correlated with the duration of illness. These findings align with our hypothesis, suggesting that FAA gradually improves as schizophrenia patients receive medication treatment. However, the current evidence regarding the relationship between the duration of illness and clinical or functional outcomes, as well as brain volume changes in patients with schizophrenia, remains inconclusive [25]. Some studies have suggested a neuroprogressive process in certain brain regions as the illness progresses [26,27]. However, Assunção Leme et al. [28] reported that while progressive cortical thinning is associated with schizophrenia, no significant correlations were found between frontotemporal cortical thickness and the duration of the disease. Findings from another systematic review and meta-analysis indicated that, compared to HCs, patients with schizophrenia show statistically significant progressive reductions in whole brain volume, whole brain gray matter, and frontal lobe volume, along with decreases in frontal, parietal, and temporal lobe white matter [29]. The correlation between duration of illness in schizophrenia and abnormalities in brain structure, function and FAA remains to be elucidated. Furthermore, as noted earlier, it remains to be determined whether the improvements in FAA resulting from medication treatment, along with the potential compensation failure over time, may also occur at the F7-F8.

While most studies concentrated on FAA in patients with schizophrenia, our findings suggested that parietal lobe alpha asymmetry might also be an effective tool for diagnosing schizophrenia. There is limited literature on parietal alpha asymmetry in patients with schizophrenia or psychosis. One study [5] reported no significant difference in parietal asymmetry scores at P4-P3 and another study [30] reported no statistically significant relations between the negative symptom and the parietal alpha asymmetry measures. The relationship between parietal lobe function and schizophrenia is not well established [31]. Toga et al. [32] described a dynamic wave of grey matter loss that starts in the parietal lobe and suggested that changes in the parietal lobe occur early in the schizophrenia. In another study [33], patients who were experiencing prodromal symptoms exhibited significant cortical thinning in the inferior parietal cortex compared to HCs.

Additionally, one study found that grey matter volumes in the parietal, frontal, and temporal lobes continued to decline more rapidly in schizophrenia patients with poor clinical outcomes compared to those with better outcomes [34]. In another study analyzing working memory processing in patients with recent-onset schizophrenia [35], lower levels of activation in the left hemisphere during verbal working memory tasks across frontal and parietal regions were associated with poorer role functioning and greater severity of negative and disorganized symptoms. Collectively, these studies indicated potential structural abnormalities or dysfunctions in the parietal lobe in schizophrenia and suggested that parietal lobe-related markers, such as parietal alpha asymmetry, could be valuable diagnostic tools.

Contrary to previous investigations into brain asymmetric activity, it is important to highlight that our study has a significantly larger sample size, providing robust evidence for the role of frontal and parietal alpha asymmetry in schizophrenia. Furthermore, all participants in our study had been diagnosed with schizophrenia for over a year and had maintained stable medication regimens for at least eight weeks. This contrasts with prior research, which often included newly diagnosed patients, undiagnosed high-risk groups, or individuals with chronic schizophrenia. The diversity of these samples raises the possibility that factors such as the duration of illness or the stability of medication may influence outcomes, warranting further exploration.

Several limitations should be considered when interpreting our results. First, this cross-sectional study on alpha asymmetry cannot determine whether this pattern predates the onset of schizophrenia or is a consequence of living with the condition. Secondly, all of our participants were on long-term stable medication, and it remains unclear whether medication treatment influences EEG patterns in different brain regions, which may require further investigation.

In conclusion, the study revealed significant differences in alpha asymmetry at the P3-P4 between individuals with schizophrenia and HCs, suggesting that the P3-P4 alpha asymmetry score could potentially serve as a biomarker for distinguishing schizophrenia in the future. However, the absence of significant differences in FAA between the PNS and SS groups underscores the need for further research to better predict and identify negative symptoms in schizophrenia.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2024.0354.

SUPPLEMENTARY MATERIALS

pi-2024-0354-Supplementary-Materials.pdf

Supplementary Table 1.

Correlation coefficients between EEG alpha symmetry and demographic/clinical features among schizophrenia patients (N=132)

pi-2024-0354-Supplementary-Table-1.pdf

Supplementary Figure 1.

Correlation coefficients between EEG alpha symmetry and demographic/clinical features among schizophrenia patients (N=132)

pi-2024-0354-Supplementary-Fig-1.pdf

Notes

Availability of Data and Material

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Hsin-An Chang. Data curation: Hsin-An Chang. Formal analysis: Yi-Guang Wang, Hsin-An Chang. Funding acquisition: Hsin-An Chang, Chuan-Chia Chang. Investigation: Yao-Cheng Wu, Hsin-An Chang. Methodology: Chih-Chung Huang, Hsin-An Chang. Project administration: Hsin-An Chang, Chuan-Chia Chang. Resources: Wei-Chou Chang, Hsin-An Chang. Software: Yi-Guang Wang, Hsin-An Chang. Supervision: Wei-Chou Chang, Chuan-Chia Chang. Validation: Chih-Chung Huang. Visualization: Chu-Ya Yang. Writing—original draft: Yao-Cheng Wu, Hsin-An Chang. Writing—review & editing: Chih-Chung Huang, Chu-Ya Yang, Yi-Guang Wang.

Funding Statement

This study was supported in part by grants from Advanced National Defense Technology & Research Program, Medical Affairs Bureau, Ministry of National Defense (MND-MAB-D-112164 and MND-MAB-113100), National Science and Technology Council of Taiwanese Government (NSTC-112-2314-B-016-017-MY3), and Tri-Service General Hospital (TSGHD-112118 and TSGH-D-113149). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Acknowledgments

None

Figure 1.

Electrodes sites for calcuation of alpha asymmetry among the participants obtained at the 4 target-paired sites: Fp1-Fp2, F3-F4, F7-F8, and P3-P4 (i.e., fontopolar, mid-frontal, lateral frontal, and parietal regions, respectively). EEBLAB Frontal Alpha Asymmetry Toolbox was used to calculate regional alpha asymmetry from log-transformed electroencephalography alpha power in the left hemisphere and from log-transformed power at homologous sites in the right hemisphere.

Figure 2.

Comparison of alpha asymmetry among schizophrenia patients with predominant negative symptoms (PNS), stabilized schizophrenia (SS) patients, and healthy controls (HCs). Each circle represents the alpha asymmetry score of each participant. The cyan dot indicates the mean of the alpha asymmetry score. The gray box indicates standard deviations. *p<0.05 (corrected).

Figure 3.

Correlations between duration of illness and F7-F8 alpha asymmetry score. Each red circle represents a single participant with schizophrenia. The regression line and 95% confidence intervals for the linear regression slope are shown.

Table 1.

Demographics, clinical characteristics, and symptomatology of the participants

	PNS (N=60)	SS (N=72)	HC (N=73)	Omnibus (p)	Significant comparisons
Demographics
Female	30 (50.00)	33 (45.83)	28 (38.36)	0.39	ND
Age (yr)	43.47±11.79	42.90±10.87	40.00±4.96	0.07	ND
Education (yr)	12.73±2.93	13.31±3.02	15.80±1.19	<0.001	PNS vs. HC; SS vs. HC
Clinical characteristics
Age at onset (yr)	28.03±8.42	25.96±8.21	NA	0.16	ND
Duration of illness (yr)	15.42±10.53	17.44±10.97	NA	0.28	ND
Chlorpromazine equivalent dose (mg/day)	630.26±361.27	603.64±359.05	NA	0.67	ND
Symptomatology
PANSS positive subscale	14.58±3.63	15.18±4.32	NA	0.40	ND
PANSS negative subscale	29.33±3.48	19.21±3.46	NA	<0.001	ND
PANSS general subscale	38.30±6.90	37.18±4.65	NA	0.29	ND

Values are presented as number (%) or mean±standard deviation. PNS, predominant negative symptoms; SS, stabilized schizophrenia; HC, healthy control; PANSS, Positive and Negative Syndrome Scale; NA, not applicable; ND, not detected

Table 2.

Binary logistic regression analyses and ROC for schizophrenia patients (N=132) vs. healthy controls (N=72)

Predictor	Binary logistic regression analyses
Predictor	Beta	S.E.	Wald	p	OR	95% CI	AUC	Nagelkerke R²
P3-P4 alpha asymmetry score	3.53	0.92	14.71	<0.001^*	32.95	5.61-205.65	NA	0.124
Constant	-4.60	1.06	18.67	<0.001^*	0.01	NA	NA	NA
P3-P4 alpha asymmetry score	NA	0.03	NA	<0.001^*	NA	0.70-0.84	0.77	NA

^* represents statistical significance.

S.E., standard error; Wald, Wald statistic; OR, odds ratio; ROC, receiver-operating characteristic; AUC, area under the ROC curve; CI, confidence interval; NA, not applicable

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