The Neural Correlates of Social Appraisal Dysfunction in Major Depressive Disorder: An Event-Related Potential Study

doi:10.21203/rs.3.rs-4885185/v1

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The Neural Correlates of Social Appraisal Dysfunction in Major Depressive Disorder: An Event-Related Potential Study

https://doi.org/10.21203/rs.3.rs-4885185/v1

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Background: Research has demonstrated that some individuals with major depressive disorder (MDD) exhibit more distorted social appraisal than non-depressed individuals. This study aimed to explore the neuroelectrophysiological mechanisms of emotional processing bias in patients with MDD and thus to understand their functional properties of social appraisal.

Methods: Thirty-four patients with MDD and 34 healthy controls (HCs) were selected. The behavioral and event-related potentials (ERPs) data of the Socio-emotional Preference Task (SePT) were recorded and analyzed.

Results: The behavioral results showed that the MDD group showed longer reaction times (RTs) to both negative and positive stimuli compared to the HCs group, preferring negative stimuli. The ERP data indicated that the MDD group exhibited higher P200 amplitudes for negative and neutral stimuli compared to the HCs group. Additionally, they showed higher LPP amplitudes for negative and positive stimuli, with longer LPP latencies for negative stimuli. In the MDD group, multiple linear stepwise regression analysis showed that LPP amplitudes were positively correlated with RTs for positive stimuli and negatively correlated with RTs for negative stimuli. Conversely, P200 amplitudes were negatively correlated with RTs for negative stimuli but positively correlated with Hamilton Depression Rating Scale (HDRS-17) scores. Moreover, HDRS-17 scores positively correlated with the self-reported preference for negative stimuli but negatively correlated with the self-reported preference for positive stimuli.

Conclusion: Patients with MDD tend to choose negative information that is consistent with negative self-schema. The brain devotes more cognitive resources and longer RTs to processing negative stimuli, starting from the early stages (P200) and extending into the later stages (LPP), there is a tendency for processing fixation. RTs of patients with MDD to negative and positive stimuli can predict the amplitudes of LPP. Additionally, their preference for negative stimuli and avoidance of positive stimuli can predict depressive symptom severity.

major depressive disorder

event-related potentials

social cognition

social appraisal

Major Depressive Disorder (MDD) is a highly common, chronic mood disorder that leads to severe adverse outcomes. Currently, there are approximately 350 million people worldwide suffering from MDD [1]. Due to the previous global COVID-19 pandemic and the subsequent post-pandemic situation, it is estimated that this number will continue to rise, with a more noticeable increase in incidence among young people [2]. Additionally, in individuals with MDD, depressive episodes tend to relapse with greater severity and diminished responsiveness to conventional therapy [3]. These factors make MDD one of the diseases contributing significantly to the global economic burden and health burden.

Depressive symptoms in patients with MDD typically start with emotional fluctuations and brief emotional responses, progressing to feelings of despair and helplessness. They experience a lack of motivation or interest, finding no joy in life, which permeates various aspects of daily life. This condition negatively impacts their performance in school, work, and family life, significantly reducing their overall quality of life. Among them, difficulties in social and interpersonal relationships are particularly prominent [4]. On the one hand, the shame and sense of discrimination brought about by the illness lead patients with MDD to withdraw from normal social interactions and isolate themselves. They may feel inferior, helpless, and misunderstood, which could lead them to actively avoid social situations and reduce their interactions with others, resulting in a form of social disconnection [5]. On the other hand, negative cognitive biases can lead to distorted emotional perceptions of oneself and others, making individuals more prone to negative interpretations and emotional distortions. These cognitive biases contribute to increased negative interactions with others and are more likely to cause conflicts within intimate relationships [6]. One possible explanation for these cognitive distortions could be related to the abnormal social appraisal function in patients with MDD. Social appraisal refers to the psychological process by which individuals interpret and infer their own and others' behaviors based on their perception of the social environment and the formation of social impressions. Positive appraisal in social interactions fosters feelings of social acceptance, whereas negative appraisal can lead to experiences of social rejection. Persistent feelings of social rejection have been linked to adverse effects on mental health, potentially exacerbating conditions such as depression [7] and anxiety [8]. Research has demonstrated that depressed individuals exhibit more distorted social appraisal than non-depressed individuals [9–11].

The assessment of social stimuli among patients with MDD has predominantly focused on social cognition, typically involving objective tasks like recognizing facial emotions. Faces are encountered frequently as social stimuli, serving as vital conveyors of essential information crucial for effective social communication. Within social interactions, facial expressions act as fundamental cues of emotional states, offering indispensable insights [12]. Individuals with MDD, often struggle with identifying emotional expressions [13–16]. However, critical aspects of appraisal that require subjective preference have not been extensively explored in MDD. More importantly, there are no event-related potentials (ERPs) studies that explore social appraisal in a real social situation. ERPs are scalp-recorded voltage responses in the brain that occur in response to specific sensory, cognitive, or motor events. They provide insights into the sequence and timing of neural events underlying cognitive functions, making them a powerful tool for testing theories of cognition and understanding how the brain processes information in real-time [17–21].

Currently, there is still insufficient comprehension regarding the neurophysiological foundations of social appraisal dysfunction in MDD, hindering the development of more effective interventions and treatments. To address this question, this present study used the Socio-emotional Preference Task (SePT) on emotional face stimuli sought to evaluate neural correlates of social appraisal in MDD. This task had never before been studied in patients with MDD. During the task, participants viewed facial stimuli displaying negative, neutral, or positive expressions, and indicated their preference for each face. MDD is a highly heterogeneous mental illness, and its diagnosis is mostly based on clinical manifestations, lacking objective biological markers. We extracted mental activity information from Electroencephalogram (EEG) signals by integrating neuropsychological testing and neuroelectrophysiology. This approach helps identify characteristic neurophysiological changes to uncover the brain mechanisms of mental activity, discover potential biological markers, and generate new treatment strategies for MDD.

Participants

All participants in this study were recruited from patients with MDD hospitalized in the affiliated Wuxi Mental Health Center of Jiangnan University from January 2022 to January 2023. They met the study inclusion criteria: ① met the depression criteria set forth in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), ② 18-55 years of age, ③ The Hamilton Depression Scale 17-item version (HDRS-17) scores > 17, ④ no history of electroconvulsive therapy (ECT) for nearly 6 months and ⑤ right-handedness. The exclusion criteria were: ① diagnosed with other Axis I disorders according to DSM-IV, including bipolar affective disorder, mixed episodes, and psychotic symptoms, ② history of neurological illness or any other physical condition that might negatively impact cognitive function, ③ substance or non-substance abuse history, ④ pregnant or lactating women and ⑤ significant visual and auditory impairment that would interfere with task performance. Meanwhile, healthy participants matched for age, gender, and educational level were recruited through online and social media advertising. All participants with a family history of psychiatric disorders were excluded.

This study was approved by the Ethics Committee of Wuxi Mental Health Center (WXMHCIRB2022LLky012), and all participants voluntarily participated for the purpose and experimental content of the study and had signed written informed consent.

Clinical Assessments

A self-designed general information questionnaire was used for participants, which included fields for the participant's name, gender, age, education level, marital status, occupation, medical history, treatment status, treatment methods, and medication usage, among other details. The Mini International Neuropsychiatric Interview (MINI) and the HDRS-17 were conducted by the associate chief physician of psychiatry with uniform training to clarify the diagnosis and evaluate the severity of the patients. For the control group, only the MINI was used to exclude various psychiatric disorders.

The MINI is a screening tool for psychiatric disorders developed by American psychiatrists Sheehan and Lecrubier. This tool targets 16 Axis I psychiatric disorders in the DSM-IV and the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10), utilizing a brief structured interview for assessment [22, 23]. The Chinese version demonstrates good reliability and validity, as well as high consistency among different researchers [24]. The Hamilton Depression Rating Scale (HAMD) belongs to hetero-rating scales and is widely used in clinical practice to assess the severity of depressive symptoms and treatment outcomes in patients with MDD [25]. It is considered the "gold standard" for evaluating depressive symptoms [26]. Our study utilized the 17-item version. The total HDRS-17 score ranges from 0 to 52, with higher scores indicating more severe depression. A decrease in scores from the baseline reflects a reduction in depression severity [27].

Measurements

SePT

SePT is a simple social appraisal task. In prior research, Stephan used this task to investigate the relationship between activity in the parietal cortex of patients with schizophrenia and impairment in social appraisal function [28]. In our study, to mitigate the influence of ethnic diversity, we curated 60 facial emotion images from the Chinese facial affective picture system [29] as stimuli. Emotional valence was stratified into three categories: positive (e.g., happiness), negative (e.g., fear, sadness, anger, and disgust), and neutral emotions. The task began with five practice trials to help participants become acquainted with the procedure. Each trial commenced with a fixation cross "+" appearing at the center of the computer screen, aiming to sustain participants' attention throughout the experiment. Subsequently, a prompt such as " What are your feelings when you see the following picture?" (for preference selection) or "Please judge the gender of the person in the picture?" (for gender judgment) was presented. Following a 3000 ms interval, a facial emotion image appeared on the computer screen, granting participants 1000 ms to observe it. Thereafter, participants rendered their judgments by the preceding prompt: " Like (A) or Dislike (L) " for preference, "Male (N) or Female (V)" for gender, while concomitantly engaging the corresponding alphanumeric keys (A/L, N/V) on the computer keyboard. Six facial emotion images constituted a block, with each block randomly featuring either preference selection or gender judgment tasks. A rest period lasting 4-8 seconds was provided between each block. The entire task paradigm consists of 360 trials, and the total duration of the experiment is approximately 50 minutes. It is noteworthy that within this paradigm, the inclusion of gender judgment trials served the dual purpose of assessing participants' levels of attention and evaluating accuracy. These specific trials constituted 20% of the overall trials. During the final data screening, only experimental data from participants with an accuracy rate surpassing 90% were incorporated into the final data analysis. Additionally, to mitigate the potential effects of diurnal variations on individual cognitive functions, all our experiments were conducted in the Cognitive Function Laboratory of our institute at 9 a.m., ensuring a quiet environment with ample lighting, electromagnetic shielding, and a temperature maintained at 26°C. Before the experiment, the researchers observed the participants' level of consciousness and concentration. They communicated with the participants to facilitate learning and understanding of the task content, achieving a high level of cooperation to focus attention. Stimulus presentation and data collection were executed using E-Prime software version 3.0 (Psychology Software Tools, Pittsburgh, PA). The operational interface was displayed on a 19-inch computer monitor with a screen resolution of 1280×1024 pixels and a refresh rate of 60 Hz. Participants were situated at a distance of 60 cm from the computer screen. The procedure of SePT is sketched in Fig. 1.

EEG signal recording and ERP analysis

EEG data was collected using a 64-channel Ag/AgCl electrode electroencephalogram system (EasyCap, Herrsching, Germany). Electrodes were placed on the scalp according to the international 10/20 system, with the reference electrode at the frontal pole midline point (Fpz), and signals from the left and right mastoids were simultaneously recorded. Electrodes for vertical electrooculogram (VEOG) were placed 1 cm below the left orbital, and electrodes for horizontal electrooculogram (HEOG) were placed 1.5 cm outside the outer canthi of both eyes. The sampling frequency was 500 Hz, with continuous sampling, bandpass filtering from 0.01 to 30 Hz, and scalp-to-electrode impedance less than 5 kΩ.

The raw EEG data were analyzed offline using Brain Vision Analyzer 2.1 (Brain Products GmbH, Munich, Germany). The preprocessing steps were primarily divided into baseline drift removal, ocular artifact removal, filtering, and segmentation. A detailed description is provided below:

Baseline calibration: In our experiment, a baseline estimation method was utilized to remove the influence of baseline drift on the temporal features of the EEG.
Re-referencing: Bilateral mastoids (A1, A2) were used as the reference in this study, which means that the EEG signals were measured and recorded relative to this specific location. The preprocessed EEG data underwent a re-referencing operation.
Bandpass filtering: The collected raw EEG data were subjected to bandpass filtering using a high-pass filter at 0.1 Hz and a low-pass filter at 30 Hz. A notch filter at 48-52 Hz was applied to remove power line interference, allowing for a clearer observation of the EEG signal features.
Rejection of bad segments and interpolation of bad channels: The data were manually inspected again, and segments with significant drift in the EEG signal were removed. Interpolation was performed on problematic channels to further enhance the data quality.
Independent Component Analysis (ICA): To correct for periodically occurring artifacts such as eye movement, electrocardiographic, and electromyographic artifacts, ICA was used to separate the mixed signals into independent components and remove the artifacts.
Data segmentation: Based on previously set marker annotations, the data were segmented from 200 ms before stimulus presentation to 800 ms after presentation. Baseline calibration was performed again to eliminate the relative deviation between the EEG and the baseline.
Grand averaging: The EEG signal segments under the same stimulus conditions, processed through the aforementioned steps, were averaged together to obtain ERP data.

Demographics and clinical sample characteristics

34 patients with MDD and 34 healthy individuals were ultimately included in this trial. Among the patients with MDD, the average course of disease was 47.74 months (SD = 29.820), and the mean score on the HDRS-17 was 23.94 (SD = 6.915). Specifically, the mean scores for psychomotor retardation, cognitive impairment, anxiety/somatization, sleep disturbance, and weight factors were 6.65 (SD = 2.538), 4.00 (SD = 2.449), 7.15 (SD = 2.414), 4.88 (SD = 1.038), and 1.47 (SD = 1.440), respectively. 34 patients were treated with antidepressants medications: five with venlafaxine (mean dose 190.0 ± 33.91 mg/day), nine with escitalopram oxalate (mean dose 16.11 ± 4.17 mg/day), six with mirtazapine (mean dose 28.75 ± 9.97 mg/day), four with paroxetine (mean dose 25.0 ± 5.77 mg /day), seven with sertraline (mean dose 167.86 ± 37.40 mg /day), and three with fluoxetine (mean dose 26.67 ± 11.55 mg /day). For patients, the mean Fluoxetine-equivalent dose was 37.43 ± 13.79 mg/d as calculated according to the previous report [30].

Age and years of education data did not follow a normal distribution, the Mann-Whitney U test indicated no statistically significant differences in age and years of education between the two groups (P > 0.05). Gender-ratio met the conditions for a chi-square test, which showed no statistically significant differences between the two groups as well (P > 0.05) (Table 1).

Table 1

**Demographic characteristics and clinical information of participants (means and standard deviations)**
Variable	MDD (n = 34)	HCs (n = 34)	Test statistic
Age (years)*	37.50(27,44)	34.0(28,36.50)	U = 521.0, P = 0.484
Sex (M/F)	16/18	17/17	χ² = 0.059, P = 0.808
Education (years)*	13(9,16)	15(12,15)	U = 458.0, P = 0.133
course of disease (month)	47.74(29.820)	-	-
HDRS-17	23.94(6.915)	-	-
psychomotor retardation cognitive impairment anxiety/somatization sleep disturbance weight	6.65(2.538) 4.00(2.449) 7.15(2.414) 4.88(1.038) 1.47(1.440)	-	-
Medicine (S/E/M/P/V/F)	7/9/6/4/5/3
Abbreviations: MDD = major depressive disorder; HCs = healthy controls; Sex (M = Male, F = Female); HDRS-17 = the Hamilton Depression Rating Scale, 17 item; Medicine (S: Sertraline, E: Escitalopram oxalate, M: Mirtazapine, P: Paroxetine, V: Venlafaxine, F: fluoxetine). *Age and years of education data are skewed, represented by the median (P₂₅, P₇₅).

SePT

RTs for emotional selection task between the MDD group and the HCs group across emotional valence (positive, negative, and neutral) are illustrated in Fig. 2. ANOVA of RTs yielded no significant effect of emotional valence (F_2,132 = 0.025, P = 0.975, η² = 0.000), but revealed a significant of participant group (F_1,66 = 5.487, P = 0.022, η² = 0.077). The interaction between emotional valence and participant group was significant (F_2,132 = 4.762, P = 0.010, η² = 0.067). Simple effect analysis revealed that MDD participants exhibited longer mean RTs for negative stimuli (F_1,66 = 5.124, P = 0.027, η² = 0.072) and positive stimuli (F_1,66 = 10.740, P = 0.002, η² = 0.002) than healthy controls, but there was no significant difference for neutral stimuli (F_1,66 = 0.075, P = 0.785, η² = 0.001). For gender judgment task, the difference in RTs between the two groups was not statistically significant (P > 0.05).

Self-reported preference for emotional selection task between the MDD group and the HCs group across emotional valence (positive, negative, and neutral) are illustrated in Fig. 3. ANOVA of Self-reported preference yielded no significant effect of participant group (F_1,66 = 3.116, P = 0.082, η² = 0.045), but revealed a significant effect of emotional valence (F_2,132 = 96.379, P < 0.001, η² = 0.594). The interaction between emotional valence and participant group was significant (F_2,132 = 6.907, P = 0.009, η² = 0.095). Simple effect analysis revealed that MDD participants showed a higher preference rate for negative stimuli (F_1,66 = 5.903, P = 0.018, η² = 0.082) and a lower preference rate for positive stimuli (F_1,66 = 7.884, P = 0. 007, η² = 0.107) compared to healthy controls. However, the difference in the preference rate for the neutral stimuli was not statistically significant (F_1,66 = 0.255, P = 0. 616, η² = 0.004). Additionally, within-group comparisons revealed that MDD participants exhibited a significantly higher preference for negative stimuli compared to positive and neutral stimuli (P < 0.001).

The average accuracy rate of gender judgment in the MDD group was 90.02% (median = 90.32%); in the HCs group, the average accuracy rate was 91.26% (median = 91.67%). The results suggest no statistical difference in gender judgment accuracy rates between the two groups (Z = -1.048, P = 0.295).

ERP Results

LPP. A 2 (group: MDD, HCs) × 3 (emotion valence: positive, negative, and neutral) × 5 (electrode site: CP1, CP2, CP3, CP4, CPZ) mixed-design ANOVA was conducted for the analysis of LPP.

The results indicated a significant main effect of participant group (F_1,66 = 8.381, P = 0.005, η² = 0.113) and a significant main effect of emotion valence (F_2,132 = 6.991, P = 0.010, η² = 0.096). The interaction between emotional valence and participant group was significant (F_2,132 = 3.281, P = 0.012, η² = 0.047). However, there was no interaction effect between emotion valence, participant group, and electrode site (F_8,528 = 0.376, P = 0.856, η² = 0.006). Simple effect analysis revealed that MDD participants showed higher LPP amplitudes for negative stimuli (F_1,66 = 8.656, P = 0.004, η² = 0.116) and positive stimuli (F_1,66 = 2.983, P = 0. 025, η² = 0.159) compared to healthy controls (Fig. 4).

For LPP latency, there was a significant main effect of participant group (F_1,66 = 5.030, P = 0.028, η² = 0.071) and a significant main effect of emotion valence (F_2,132 = 2.624, P = 0.035, η² = 0.038). The interaction between emotional valence and participant group was significant (F_8,528 = 1.940, P = 0.027, η² = 0.029). No interaction effect was found between emotion valence, participant group, and electrode site (F_10,660 = 1.464, P = 0.132, η² = 0.022). Simple effect analysis revealed that MDD participants showed longer LPP latency for negative stimuli (F_1,66 = 3.776, P = 0.039, η² = 0.056) compared to healthy controls (Fig. 5).

P200. A 2 (group: MDD, HCs) × 3 (emotion valence: positive, negative, and neutral) × 6 (electrode site: F3、FZ、F4、FC3、FCZ、FC4) mixed-design ANOVA was conducted for the analysis of P200. The results indicated a significant main effect of participant group (F_1,66 = 6.098, P = 0.016, η² = 0.085), a significant main effect of emotion valence (F_2,132 = 7.976, P < 0.001, η² = 0.108), and a significant main effect of electrode site (F_5,330 = 4.793, P = 0.029, η² = 0.070). The interaction between emotional valence and participant group was significant (F_2,132 = 7.069, P = 0.010, η² = 0.097). The marginal interaction effect between the participant group and electrode site was observed (F_5,330 = 2.222, P = 0.052, η² = 0.033). There was also a significant main effect between emotion valence, participant group, and electrode site (F_10,660 = 4.300, P = 0.042, η² = 0.061). Simple effect analysis revealed that MDD participants showed higher P200 amplitudes for negative stimuli (F_1,66 = 5.065, P = 0.028, η² = 0.071) and neutral stimuli (F_1,66 = 4.828, P = 0. 032, η² = 0.068) compared to healthy controls (Fig. 6). For negative stimuli, the MDD participants had significantly higher P200 amplitudes compared to the healthy controls at all six electrode sites (P < 0.05). Among them, the largest difference in mean P200 amplitude between the two groups was observed at electrode site F3 (2.155 ± 0.899 µV) (Fig. 6A). For neutral stimuli, the MDD participants had significantly higher P200 amplitudes compared to the healthy controls at three electrode sites (F3, FZ, and F4) (P < 0.05). Among them, the largest difference in mean P200 amplitude between the two groups was observed at electrode site F4 (1.904 ± 0.852 µV) (Fig. 6B). For positive stimuli, no statistically significant difference in P200 amplitudes were found between the two groups across six electrode sites (P > 0.05). However, within-group comparisons revealed that MDD participants exhibited significantly higher P200 amplitudes for negative stimuli and neutral stimuli compared to positive stimuli (P < 0.05) (Fig. 7).

For P200 latency, the results indicated a significant main effect of electrode site (F_5,330 = 2.306, P = 0.044, η² = 0.034). There was no significant main effect of participant group (F_1,66 = 2.046, P = 0.157, η² = 0.030) and no significant main effect of emotion valence (F_2,132 = 0.298, P = 0.743, η² = 0.004). The interaction effect between electrode site and participant group was not significant (F_5,330 = 1.968, P = 0.083, η² = 0.029), and the interaction effect between electrode site and emotion valence was not significant (F_10,660 = 1.219, P = 0.275, η² = 0.018). No interaction effect was found between emotion valence, participant group, and electrode site (F_10,660 = 0.683, P = 0.741, η² = 0.010).

Multiple linear stepwise regression analysis between behavioral data, ERP data, and HDRS-17 scores

In order to find biomarkers for correct diagnosis, appropriate treatment, and predicted outcomes of MDD patients, we specifically investigated the relationships among clinical symptoms, behavioral data, and electrophysiological characteristics in MDD participants.

LPP amplitude. Multiple linear stepwise regression analysis was employed using behavioral data, fluoxetine equivalent dose, total disease duration, and HDRS-17 scores as independent variables and LPP amplitude as the dependent variable. The results found that the negative _ RTs and positive _ RTs entered the regression equation, LPP amplitude was negatively correlated with negative _ RTs (B = -0.008, P = 0.008) and positively associated with positive _ RTs (B = 0.009, P = 0.023) (Table 2). The regression equation is as follows: Y^ = 4.014 − 0.008 * negative _ RTs + 0.009 * positive _ RTs

Table 2

Factors influencing LPP amplitude (n = 34)
	Non Standardized Coefficient		Standardized Coefficient	t	P	VIF	R²	AdjustedR²	F
	B	Standard Error	Beta	t	P	VIF	R²	AdjustedR²	F
Constant	4.014	6.76	0	0.594	0.557	-	0.295	0.25	F = 6.489, P = 0.004^***
negative_RTs	-0.008	0.003	-0.432	-2.858	0.008^***	1.005
positive_RTs	0.009	0.004	0.36	2.384	0.023^**	1.005
Notes: P < 0 .05, * P < 0.01.

P200 amplitude. Multiple linear stepwise regression analysis was employed using behavioral data, fluoxetine equivalent dose, total disease duration, and HDRS-17 scores as independent variables and P200 amplitude as the dependent variable. The results found that the negative _ RTs and HDRS-17 scores entered the regression equation, P200 amplitude was negatively correlated with negative _ RTs (B = -0.005, P = 0.050) and positively associated with HDRS-17 scores (B = 0.337, P = 0.032) (Table 3). The regression equation is as follows: Y^ = 7.405 − 0.005* negative _ RTs + 0.337* HDRS-17

Table 3

Factors influencing P200 amplitude (n = 34)
	Non Standardized Coefficient		Standardized Coefficient	t	P	VIF	R²	AdjustedR²	F
	B	Standard Error	Beta	t	P	VIF	R²	AdjustedR²	F
Constant	7.405	3.374	0	2.195	0.036**	-	0.308	0.263	F = 6.886, P = 0.003***
negative_RTs	-0.005	0.003	-0.323	-2.04	0.050**	1.125
HDRS-17	0.337	0.15	0.356	2.243	0.032**	1.125
Notes: P < 0 .05, * P < 0.01.

HDRS-17. Multiple linear stepwise regression analysis was employed using behavioral data, fluoxetine equivalent dose, total disease duration, and P200 amplitude as independent variables and HDRS-17 scores as the dependent variable. The results found that the positive_Preference and negative_Preference entered the regression equation, HDRS-17 scores were positively correlated with negative_Preference (B = 6.345, P = 0.009) and negatively associated with positive_Preference (B = − 205.181, P < 0.001) (Table 4). The regression equation is as follows: Y^ = 37.843–205.18* positive_Preference + 6.345* negative_Preference

Table 4

**Factors influencing HDRS-17 (n = 34)**
	Non Standardized Coefficient		Standardized Coefficient	t	P	VIF	R²	AdjustedR²	F
	B	Standard Error	Beta	t	P	VIF	R²	AdjustedR²	F
Constant	37.843	1.303	0	29.053	0.000***	-	0.89	0.883	F = 125.911 P = 0.000^***
positive_ Preference	-205.181	14.116	-0.889	-14.536	0.000***	1.057
negative_ Preference	6.345	2.261	0.172	2.807	0.009***	1.057
Notes: P < 0 .05, * P < 0.01.

MDD is a serious psychological disorder that affects the mood, thinking, and behavior of patients. This disease causes patients to experience lasting sadness and negative emotions, making them feel depressed, helpless, and lose interest. Typically, patients with MDD strive to alleviate this emotional distress through various means such as seeking psychological counseling, undergoing medication treatment, participating in support groups, or engaging in exercise, hoping to regain inner balance and happiness. However, studies have found that some patients with MDD actively choose to expose themselves to negative stimuli (such as music or images) [31–33], a behavior referred to in psychology as "depressive maintenance behavior" [34], which means that some patients with MDD maintain their emotional state through voluntary exposure to negative stimuli. Given the contradictory findings described above, this study aimed to explore the neuroelectrophysiological mechanisms of emotional processing bias in patients with MDD and thus to understand their functional properties of social appraisal.

We compared 34 patients with MDD and 34 healthy controls using the social-emotional preference task paradigm. The behavioral data results showed that patients with MDD preferred negative emotional stimuli compared to healthy controls. This result is consistent with previous studies. For example, Werner-Seidler found through questionnaire surveys that patients with MDD tend to choose to indulge in negative emotions rather than trying to avoid them [35]. In another study, patients with MDD and healthy individuals were asked to subjectively report the degree of experience of happy, sad, and neutral emotions, which showed that patients with MDD tended to prefer sad emotions [36]. In Vishkin’s study, patients with MDD were asked to rate their preference and corresponding emotional experience for three types of emotional stimuli (neutral, happy, and sad). The results indicated that they had a higher preference for sad emotional pictures [37]. The latest research has found that patients with MDD tend to prefer sad music over happy or neutral music, as they believe that listening to sad music helps them express and release their emotions. This explanation aligns with the self-verification theory, which suggests that individuals tend to seek affirmation of their existing self-conceptions, whether positive or negative. People are motivated to maintain consistency and compatibility with their self-conceptions, actively seeking situations and relationships that are consistent with these self-conceptions [38]. Therefore, patients with MDD tend to select negative information that is consistent with their negative self-schemas and pay excessive attention to negative emotions. Additionally, we found that patients with MDD showed longer RTs to both negative and positive emotional stimuli compared to the healthy controls, indicating that individuals with MDD process both positive and negative emotions at a slower pace. This result is consistent with the previous research findings of Sass K, who suggested that patients with MDD require longer RTs for perceptual processing of negative emotional stimuli compared to healthy individuals [39]. A large-scale study found that adolescent patients with MDD had a significantly higher error rate in identifying negative emotional faces compared to happy faces, and their RTs in identifying negative emotional faces were longer than those of the healthy controls [40]. Overall, the specific reactions of patients with MDD to negative emotional stimuli can be explained by Beck's cognitive theory of depression. According to this theory, patients with MDD have a negative bias in information processing, leading them to selectively focus on negative information [41].

For ERP data analysis, we primarily used amplitude and latency as the main measures. First, amplitude reflects the brain's level of excitement in response to stimuli, and by measuring the amplitude of the EEG signal, we can understand the excitation level that the brain exhibits when processing specific stimuli. Higher amplitude typically indicates stronger cognitive processing of target stimuli, requiring more cognitive resources. On the other hand, latency reflects the speed of individual neural activity. Shorter latency indicates faster cognitive processing of stimuli, while longer latencies indicate slower processing. By analyzing latency, we can understand the speed at which the individual's nervous system processes different stimuli, revealing the speed of cognitive processing. In this study, we focused on analyzing two ERP components: P200 and LPP.

The P200 component is a positive waveform that typically peaks around 200 ms after the onset of stimuli. It reflects early perceptual and attention-related brain activity in information processing and is often observed in studies related to emotion [42]. A previous meta-analysis found that the elevation of P200 amplitude reflects an individual's heightened attention to negative emotion [43]. In our study, we found that patients with MDD exhibited significantly higher P200 amplitudes compared to healthy controls for negative emotional stimuli, with the most prominent difference observed at the F3 electrode site. When facing neutral emotional faces, patients with MDD also showed significantly higher P200 amplitudes, with the most notable difference observed at the F4 electrode site. However, there was no significant difference in P200 amplitude between the two groups when facing positive emotional faces. This result also supports the theory that P200 component may be a biological marker of negative cognitive processing in the brains of patients with MDD [44]. Furthermore, we also found that patients with MDD exhibited differences in P200 amplitudes when facing emotional stimuli of different valences. Specifically, the P200 amplitudes were higher when facing negative and neutral emotion stimuli compared to positive ones. This finding is similar to a previous study on the cross-modal audiovisual emotional integration cognitive processing related to MDD. In their study, patients with MDD showed significantly enhanced P200 amplitudes when confronted with emotional stimuli of all valences [45]. In our study, we did not find the P200 effect for positive emotional stimuli. The reason for this difference may be related to the different study samples. Lu's study sample consisted of college students, which could have led to different results.

The LPP component is an important ERP component related to emotional regulation. It typically begins around 400 ms after stimulus presentation and lasts for several hundred milliseconds or even longer (with both the onset time and duration varying depending on the type of stimulus and task requirements). LPP is an ideal EEG component for studying the controlled processing of emotional stimuli [46]. It can be used to reflect an individual's sensitivity to the processing and evaluation of the stimulus, especially in emotional information processing with strong intrinsic significance [47]. To some extent, this can indicate an individual's bias in the processing and response to emotional information, while also revealing the higher cognitive processing involved in emotions. Related studies have shown that in the field of emotional information processing, patients with MDD will show a strong LPP effect on negative stimuli (such as negative words or faces), indicated by an increased LPP amplitude [48, 49]. In our study, we found consistent results indicating that patients with MDD exhibit significantly higher LPP amplitudes in response to negative emotional stimuli compared to healthy controls. According to the emotional motivation theory, LPP can also reflect the intensity of motivation, and the expected stimulus associated with higher motivation strength can elicit higher LPP amplitudes [50]. It means that patients with MDD exhibit a stronger motivation to approach negative emotional stimuli. This result is consistent with behavioral data observed in the MDD group. Meanwhile, we also found that patients with MDD exhibited higher LPP amplitudes in response to positive emotional stimuli compared to healthy controls. This finding is inconsistent with other literature [51, 52]. The reason for this discrepancy may be related to the high heterogeneity of MDD, including differences in pathology, clinical symptoms, as well as treatment and post-intervention methods.[53, 54] Therefore, whether the heterogeneity of MDD has an impact on LPP deserves further exploration in future studies. For LPP latency, we found that patients with MDD have significantly longer latencies in response to negative emotional stimuli compared to healthy controls, suggesting that they have longer processing times for negative emotional stimuli, reflecting a potential tendency for processing fixation.

In addition, a multiple linear stepwise regression analysis was conducted to examine the associations between clinical symptoms, cognitive functions, and EEG indicators in the MDD group. The results revealed positive correlations between LPP amplitudes and RTs for positive stimuli, and negative correlations between LPP amplitudes and RTs for negative stimuli, suggesting that RTs for negative and positive emotional stimuli in patients with MDD can predict the magnitude of LPP amplitude; The P200 amplitudes were negatively correlated with RTs to negative emotional stimuli and positively correlated with HDRS-17 scores, suggesting that patients with MDD with more severe depressive symptoms elicit higher P200 amplitudes; The HDRS-17 scores were positively correlated with the negative _Preference and negatively correlated with positive_Preference, which is consistent with the findings of Moshe [55]. It is suggested that the preference of MDD patients for negative emotional stimuli as well as the avoidance of positive emotional stimuli can predict the severity of depressive symptoms, providing an objective indicator for the outcome of MDD.

The purpose of this study is to investigate whether there are differences in the neural mechanisms underlying social appraisal function between patients with MDD and healthy individuals using the SePT task combined with ERP technology. We found that patients with MDD indeed have abnormal neurophysiological processes when facing stimuli of different emotional valences. Firstly, from a behavioral perspective, patients with MDD exhibit longer RTs to negative and positive emotional stimuli, and show obvious preference for negative emotional stimuli. Secondly, from an ERP perspective, patients with MDD show increased P200 amplitudes in response to negative and neutral emotional stimuli, increased LPP amplitudes in response to negative and positive emotional stimuli, and longer LPP latencies for negative emotional stimuli. In addition, multiple linear stepwise regression analysis showed that LPP amplitudes were positively correlated with RTs for positive stimuli and negatively correlated with RTs for negative stimuli; P200 amplitudes were negatively correlated with RTs to negative emotional stimuli and positively correlated with HDRS-17 scores; HDRS-17 scores were positively correlated with the preference rate of negative emotional stimuli and negatively correlated with the preference rate of positive emotional stimuli. Based on these results, we conclude that patients with MDD tend to select negative information that is consistent with their negative self-schema. During cognitive processing, the brain demonstrates a heightened recognition of negative emotional stimuli, as evidenced by the increased allocation of cognitive resources and longer RTs. This effect is observed from the early stage of cognitive processing (P200) to the later stage (LPP), indicating a tendency for fixation on processing negative emotional stimuli. Based on the self-protective mechanisms that have evolved in human evolution[56], when individuals develop a persistent and generalized cognitive pattern of automatically capturing and sensitively identifying negative information, negative cognitive processing becomes dominant. This dominant processing may inhibit the occurrence of positive resources, thereby driving the development of MDD under interaction. Currently, the diagnosis of MDD still relies on clinical symptoms and lacks clear biological markers. This makes it difficult to predict the onset and progression of MDD, which restricts both clinical practice and theoretical research. Therefore, it is crucial in future research to identify objective biological markers for the accurate diagnosis, appropriate treatment, and prediction of outcomes for MDD. In our study, we found that the amplitudes of the LPP can be predicted by patients' RTs to negative and positive emotional stimuli. The LPP component is particularly sensitive to changes in emotional stimuli. Additionally, the preference for negative emotional stimuli and avoidance of positive emotional stimuli can predict the severity of depressive symptoms. There is also a positive correlation between the severity of depressive symptoms and the amplitudes of P200. Therefore, these combined indicators hold promise as biological markers of social appraisal function of MDD from a neuroelectrophysiological perspective. Of course, these indicators still need to be further verified. Firstly, it is necessary to research a larger sample size to improve the reliability of the results. Secondly, the developmental stages of the study subjects, such as those receiving early intervention and those currently undergoing treatment, need to be taken into consideration. Additionally, longitudinal follow-up studies are needed to determine the changes in these indicators over different time points and establish their value in diagnosis, prediction, and treatment monitoring.

DSM-IV Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

ECT Electroconvulsive therapy

EEG Electroencephalogram

ERPs Event-related potentials

HAMD Hamilton Depression Rating Scale

HCs Healthy controls

HDRS-17 Hamilton Depression Rating Scale 17-item version

HEOG Electrodes for horizontal electrooculogram

ICA Independent Component Analysis

ICD-10 10th Revision of the International Statistical Classification of Diseases and Related

Health Problems

MDD Major depressive disorder

MINI Mini International Neuropsychiatric Interview

RTs Reaction times

SePT Socio-emotional Preference Task

VEOG Electrodes for vertical electrooculogram

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Wuxi Mental Health Center, and all participants voluntarily participated for the purpose and experimental content of the study and had signed written informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The original contributions presented in the study are included in the article; Further inquiries can be directed to the corresponding authors.

Competing interests

The author reports no conflicts of interest in this work.

Funding

This study is supported by Wuxi Municipal Health Commission Major Project (No. 202107).

Author contributions

HZZ and HLZ conceived and designed this study. ML, JXL, JMH, and LMC contributed to manuscript preparation. XHL and XZG helped collect the data. ML analyzed the data. All authors took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted, and agree to be accountable for all aspects of the work.

Acknowledgments

We express our gratitude to all those who generously contributed to this study.

Authors' information

¹Department of Psychiatry, The Affiliated Mental Health Center of Jiangnan University, Wuxi City, 214151, Jiangsu Province, P.R.C.

²Department of Psychology, Wuxi Mental Health Center, Training Base of Hubei University of Medicine, Shiyan City, 442000, Hubei Province, P.R.C

³Department of Psychology, The Affiliated Hospital of Jiangnan University, Wuxi City, 214151, Jiangsu Province, P.R.C.

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The Neural Correlates of Social Appraisal Dysfunction in Major Depressive Disorder: An Event-Related Potential Study

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Abstract

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Introduction

Methods

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Clinical Assessments

Measurements

SePT

Results

Demographics and clinical sample characteristics

SePT

ERP Results

Multiple linear stepwise regression analysis between behavioral data, ERP data, and HDRS-17 scores

Discussion

Conclusion

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Author contributions

Acknowledgments

Authors' information

References

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