
==== Front
Neuroimage Clin
Neuroimage Clin
NeuroImage : Clinical
2213-1582
Elsevier

S2213-1582(24)00106-2
10.1016/j.nicl.2024.103667
103667
Regular Article
Neural responses to decision-making in suicide attempters with youth major depressive disorder
Bao Ciqing ab1
Zhang Qiaoyang ac1
He Chen a
Zou Haowen ad
Xia Yi a
Yan Rui a
Hua Lingling a
Wang Xiaoqin a
Lu Qing luq@seu.edu.cn
ef⁎
Yao Zhijian zjyao@nju.edu.cn
ad⁎
a Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
b Wenzhou Seventh People’s Hospital, Wenzhou 325000, China
c Department of Psychology, the Affiliated Changzhou No. 2 People’s Hospital of Nanjing Medical University, Changzhou 213000, China
d Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing 210093, China
e School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China
f Child Development and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing 210096, China
⁎ Corresponding authors at: Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210096, China (Z. Yao); School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China (Q. Lu). luq@seu.edu.cnzjyao@nju.edu.cn
1 Contributed equally.

03 9 2024
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03 9 2024
43 10366726 4 2024
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© 2024 The Author(s)
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https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• MDD patients with suicide attempts (SA) have severe decision-making (DM) defects.

• MDD patients exhibit a blunted ΔFN nerve response.

• Larger negative ΔFN is linked to better decision-making and lower impulsivity.

An improved understanding of the factors associated with suicidal attempts in youth suffering from depression is crucial for the identification and prevention of future suicide risk. However, there is limited understanding of how neural activity is modified during the process of decision-making. Our study aimed to investigate the neural responses in suicide attempters with major depressive disorder (MDD) during decision-making. Electroencephalography (EEG) was recorded from 79 individuals aged 16–25 with MDD, including 39 with past suicide attempts (SA group) and 40 without (NSA group), as well as from 40 age- and sex- matched healthy controls (HCs) during the Iowa Gambling Task (IGT). All participants completed diagnostic interviews, self-report questionnaires. Our study examined feedback processing by measuring the feedback-related negativity (FRN), ΔFN (FRN-loss minus FRN-gain), and the P300 as electrophysiological indicators of feedback evaluation. The SA group showed poorest IGT performance. SA group and NSA group, compared with HC group, exhibited specific deficits in decision-making (i.e., exhibited smaller (i.e., blunted) ΔFN). Post hoc analysis found that the SA group was the least sensitive to gains and the most sensitive to losses. In addition, we also found that the larger the value of ΔFN, the better the decision-making ability and the lower the impulsivity. Our study highlights the link between suicide attempts and impaired decision-making in individuals with major depressive disorder. These findings constitute an important step in gaining a better understanding of the specific reward-related abnormalities that could contribute to the young MDD patients with suicide attempts.

Keywords

MDD
Iowa gambling task
ΔFN
Event-related potentials
Suicidal attempts
==== Body
pmc1 Background

Suicide is a serious social and public problem, widely acknowledged as the primary cause of mortality among youth worldwide ([1], Jopling et al., 2022). It is estimated that approximately 4–10% of young people age 13–25 attempt suicide (Orri et al., 2020, Orri et al., 2021), with about 50% of these suicidal behaviors occurring during episodes of depression. People with depression have a 20-fold increased risk of dying by suicide. A previous meta-analysis revealed that the lifetime prevalence of suicide attempts (SA) in individuals with MDD was 31% (Dong et al., 2019). Furthermore, suicide attempts were more prevalent among youth with MDD, ranging from 19.5% to 43.7% (Seo et al., 2015, Ye et al., 2022). Vulnerability to suicidal behavior may be mediated by psychosocial factors, and in addition to psychological factors such as depression and anxiety, impulsive personality traits are considered risk factors for suicide. In addition, impulsive decision-making deficits (measured by impulsive neurocognition) are also considered to be moderately associated with suicidal behaviour (McHugh et al., 2019). Because prior attempted suicide increases the risk of future suicide (Bostwick et al., 2016, O'Connor et al., 2023), it is crucial to better understand factors that contribute to suicide attempts. Suicide attempts are defined as non-lethal, self-directed, and potentially harmful behaviors that result in intentional death (Meyer et al., 2010). Identifying risk factors for suicide attempts in youth with depression can improve awareness of suicide risk and facilitate targeted interventions.

Decision-making (DM), as a component of executive function (Brand et al., 2006), refers to the process of choosing a particular action among a number of alternative options that is expected to produce the most favorable outcome to the decision maker (Kim and Lee, 2011). Poor decision-making is a key neurocognitive factor leading to impulsivity (McHugh et al., 2019). Additionally, risky decision-making as part of impulsive decision-making is thought to distinguish suicidal behaviors in young adults from healthy controls (McHugh et al., 2019). Decision-making deficits is the vulnerability of suicide of depression (Ji et al., 2022, Ji et al., 2021), among them, the assessment of deviation in the decision-making process is considered to be a key factor of suicide (Gould et al., 2017). That is, these individuals with suicidal ideation, under intense psychological distress, may use suicide as a means to stop their current suffering, rather than seeing it as a “loss” (Baek et al., 2017, Hadlaczky et al., 2018).

The Iowa Game Task (IGT) (Bechara et al., 1994) is considered a popular tool for assessing decision-making. During the task, participants receive feedback after each choice and subsequently adjust their decision-making in subsequent trials based on their processing of the feedback. The present studies have shown that riskier decision-making in suicide attempters (Conejero et al., 2018, Liu et al., 2023, Perrain et al., 2021),which indicated that patients with SA make fewer beneficial and goal-oriented decisions and exhibit reduced learning abilities. Furthermore, individuals with SA encounter specific challenges in the decision-making process. These challenges include difficulties in assessing the potential positive and negative outcomes of their actions, as well as a diminished capacity to adapt their choices (Gvion and Levi-Belz, 2018). Decision deficits have also been confirmed to occur in attempted suicide patients with depression (Alacreu-Crespo et al., 2020, Sastre-Buades et al., 2021), and a strong correlation between DM-related brain regions and suicidal behavior has been found in previous studies (Baek et al., 2017, Ji et al., 2021). Indeed, neuroimaging shows structural and functional changes in brain regions related to reward responsiveness, such as the anterior cingulate cortex (ACC), orbitofrontal cortex, and ventral and dorsal striatum, associated with suicidal behavior (van Heeringen and Mann, 2014). These brain regions are the central components of the reward network and play a crucial role in decision-making (Russo and Nestler, 2013). However, to our knowledge, fewer studies have addressed the neurophysiological processes of decision-making in SA patients. Therefore, this study aims to investigate feedback processing in SA to better understand their decision-making dysfunction.

Event-related potential (ERP) from electroencephalography (EEG), with its high temporal resolution, is well suited for assessing underlying mental activities and cognitive processes during decision-making. In this study, our primary focus is on the feedback evaluation phase. Two well-researched ERP components are linked to task-related feedback after risky decisions (Gu et al., 2019, Sehrig et al., 2019), the feedback-related negativity (FRN) (Fukunaga et al., 2012) and the P300 (Nieuwenhuis et al., 2005). FRN is a negative deflection after feedback that peaks at approximately 300ms and has a maximum amplitude close to the frontocentral area (Gehring and Willoughby, 2002). Studies from fMRI and EEG demonstrate that the FRN is generated by activity in the ACC (Gehring and Willoughby, 2002, Martinez-Selva et al., 2019) and reflects neural processes in reinforcement learning and behavioral adjustment (Holroyd and Coles, 2002). The FRN is modulated by feedback outcomes in decision-making tasks, with losses showing larger amplitudes than gains (Cui et al., 2013, Garrido-Chaves et al., 2021). Moreover, FRN can be interpreted as a manifestation of reward-related positivity (RewP), wherein the level of positivity is higher in response to rewarded feedback compared to non-rewarded feedback (Bowyer et al., 2021, Proudfit, 2015). In order to isolate neural sensitivity to gains versus losses, numerous studies have relied on the ΔFN (i.e., FN to losses – FN to gains) (Foti and Hajcak, 2009, Foti et al., 2011), which is related to reward-related neural activity (Carlson et al., 2011). The P300 is a positively deflected wave that occurs 300–500 ms after feedback presentation and peaks in the central midline region of the parietal lobe (Tamburin et al., 2014). The P300 component involves cognitive processes involved in attention allocation and evaluative processing (Polich, 2012), and is believed to be linked to the detailed evaluation of outcomes (Euser et al., 2013). Furthermore, the P300 component is influenced by both the magnitude and valence of the feedback outcome, with responses to gain feedback being larger than responses to loss feedback in gambling tasks (Gu et al., 2010, Santopetro et al., 2021).

To date, one recent study showed that no difference in ΔFN was found between patients with suicide attempts and the patients without SA in a guessing task (Tsypes et al., 2021). In contrast, abnormalities in reward feedback have been found in children of suicide attempters (Tsypes et al., 2017). More specifically, children, whose parents had a history of SA, showed stronger responses (more negative FRN) to loss feedback, whereas no between-group differences were observed for gain feedback. This indicates that children of parents with a history of SA had a more negative Δ FN (i.e., FRN to losses – FRN to gains) compared to children of parents without a history of SA. These studies, however, did not investigate the impact of depression on ΔFN. It is observed in depressive patients that FRN is more sensitive to negativity, i.e., in negative feedback, depressed patients show a stronger response (Fan et al., 2021). As for P300, song et al. used an affective stimulus delay task and found that there was no significant difference in the feedback P300 amplitude between MDD patients with suicide attempts and healthy controls (Song et al., 2019). In contrast to this conclusion, prior studies have indicated that individuals with depression who have attempted suicide exhibit a lower P300 amplitude in comparison to those who have not attempted suicide (Jandl et al., 2010). Nonetheless, in light of the contradictory conclusions of aforementioned studies and the insufficient evidence in IGT, further exploration is required.

In summary, the primary objective of this study was to examine whether there are differences in the behavioral and neural correlates of decision-making (i.e., FRN and P300) during IGT between depressed patients with and without a history of suicide attempt. We hypothesized that depressed patients with suicide attempts would perform the worst on the IGT in terms of behavioral data. Regarding the electrophysiological data, we hypothesized that depressed patients with suicide attempt (SA group) would present most blunted ΔFN. In addition, we also hypothesized that the SA group will have a delayed and smaller P300 amplitude.

2 Methods

2.1 Participants

MDD patients (n=79) were recruited from the depression specialty clinic of Nanjing Brain Hospital. Each patient was asked about their current medical conditions and whether they had taken any depression-related medications. In addition, two trained psychiatrists conducted the Mini International Neuropsychiatric Interview with MDD patients and assessed for depressive symptoms. Patients who met the inclusion criteria and did not meet any exclusion criteria were invited to complete demographic, self-rating scales, and EEG tests. After completing all tests, we would prescribe medication or psychotherapy based on the patient's condition. If the patient's condition was severe and there was a risk of self-harm or harm to others, we would recommend immediate hospitalization. At the same time, healthy controls (HCs, n=40) were recruited from local communities and universities. Prior to the study, informed consent was obtained from all participants. Additionally, the research protocol received approval from the Ethics Committee of Nanjing Brain Hospital.

We enrolled MDD patients from August 2023 to January 2024. The patient inclusion criteria were as follows: (1) diagnosis of MDD according to the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998); (2) never take medication, or take medication for less than a week; (3) Han nationality patients aged 16–25 with a junior high school education or above; (4) 24-item Hamilton Depression Rating Scale (HDRS-24) (Hamilton, 1960) score > 20 points; (5) Youth Mania Rating Scale (YMRS) (Pavuluri et al., 2006) score < 5; (6) no structured psychotherapy within 6 months. Patients were categorized into two groups based on their history of suicide attempts: non-suicide attempters (n = 40, NSAs) and suicide attempters (n = 39, SAs).

Exclusion criteria for patients were as follows: (1) individuals with less than nine years of education, (2) those with a previous or present history of other mental disorders such as schizophrenia or bipolar disorder, (3) individuals with medical illness, impaired vision, or a history of brain trauma or other neurological diseases, (4) those with a history of psychoactive substances abuse, and (5) individuals with any personality disorders.

We also included 40 healthy controls (HCs) who were matched to the individuals with mental disorders in terms of age and sex. All participants underwent the MINI by trained psychiatrists to confirm that patients met the criteria for MDD and that healthy controls were free of current or past mental illness. Additional exclusion criteria for the HC group were: (1) less than nine years of education, (2) a family history of neurological or psychiatric illness, and (3) history of psychoactive substance abuse.

2.2 Measures

2.2.1 Depression and anxiety

We used the 24-item Hamilton Depression Rating Scale (HDRS-24) (Hamilton, 1960) and the Hamilton Anxiety Rating Scale (HAMA) to evaluate each patient's depression and anxiety symptoms in the past week, respectively. The Chinese version of HDRS-24 and HAMA both have good reliability and validity (Wang et al., 2022, Zhou et al., 2020).

2.2.2 Suicidal attempt

A history of suicide attempts was evaluated using the Chinese version of the MINI structured interview (Si et al., 2009). Within this module, the assessment of suicide attempts primarily consists of two questions: C5 “Have you attempted suicide in the past month?” and C6 “In your lifetime, have you ever attempted suicide?”. If the answer to either C5 or C6 was “yes”, the patients were categorized as suicide attempters and the others were considered as non-suicide attempters.

2.2.3 Impulsivity

The Chinese version of Barratt Impulsiveness Scale-11 (BIS-11-CH) (Huang et al., 2013) is utilized for the assessment of impulsivity. The 30-item BIS-11 was modified by removing five items with poor item-total correlations, resulting in a 25-item self-report scale. Each item is rated on a four-point Likert scale. The internal consistency of the BIS-11-CH, calculated among the 25 items, was found to be satisfactory at 0.83 (Li and Chen, 2007).

2.3 Behavioral task: Iowa gambling task (IGT)

Participants completed a modified computerized version of the IGT (Bechara et al., 1994) for ERP recordings (Fig. 1). All participants were told that they would perform a gambling task, starting with 2,000 yuan in virtual currency and choosing from four decks of cards A, B, C, and D. Additionally, there was no time limit for the task and participants were free to choose any deck they wanted. However, each selection could result in a lost or reward, with cards being selected by pressing the left mouse button. After a 1000 ms interval, the participants were presented with the outcome associated with their card selection for a duration of 2000 ms.Fig. 1 Experimental flow of the adapted version of the Iowa gambling task (IGT) for event-related potentials (ERPs).

Participants were instructed to maximize their virtual earnings, with the understanding that certain decks presented greater challenges than others. However, they were also informed that it was still possible to achieve success by avoiding the most unfavorable decks (Bechara et al., 1994). Decks A and B were associated with high-magnitude outcomes, whereas decks C and D were linked to low-magnitude outcomes. Over the long term, decks A and B proved to be disadvantageous (referred to as 'risky') as they resulted in a net loss over time. On the other hand, decks C and D were advantageous (referred to as 'safe') as they led to net gains throughout the task. To adapt IGT to EEG, the number of trials was increased from 100 to 200. Additionally, to reduce eye artifacts, at the beginning of each trial, subjects were asked to focus on a cross. Before beginning the task, all participants were trained with a 5-trials short version of the game.

We divided the 200 trials into ten blocks of 20 trials each for behavioral data analysis. The IGT performance was measured using the IG index, calculated by [(C + D) − (A + B)]. A positive score indicates a favorable strategy (Balconi et al., 2015).

2.4 EEG recording and analysis

The EEG signal was acquired using a 64-channel active electrode system (Compumedics Neuvo) at a sampling rate of 1,000 Hz, referenced to a common average reference. EEG signals were filtered between 1 and 40 Hz, with a notch filter at 49–51 Hz. The electrode impedance was maintained at a level below 10kΩ throughout the EEG recording process. The feedback stage is displayed at 1200ms, with 200ms before the feedback presentation (adjusted to baseline) and 1000ms after the feedback presentation. Independent Component Analysis (ICA) was performed using EEGLAB to eliminate artifacts caused by eye movements and blinks (Makeig et al., 2004). No more than 5 ICA components were removed from each dataset.

Based on the topographic map of EEG components in this study and the literature (Guy et al., 2021, Tsypes et al., 2018), we selected the Fz, FCz, FC1, and FC2 electrodes in the 325∼375ms time window for FRN analysis. Additionally, we chose the Cz, CPz, C1, and C2 electrodes in the 300∼500ms time window for P300 analysis.

2.5 Statistical analyses

The data were analyzed using SPSS 26.0 (IBM, Armonk, NY, USA). Differences in linear demographic variables among groups were examined by conducting a one-way ANOVA, and sex matching among groups was assessed using a Pearson's chi-square test.

The one-way ANOVA was utilized to compare the three groups based on their IGT index. As the IGT consists of ten consecutive blocks, we conducted a repeated-measures analysis of variance with time as a within-subjects factor and group as a between-subjects factor to account for potential learning effects. Additionally, we utilized a repeated-measures analysis of variance (ANOVA) with groups (SA, NSA, and HC) as the between-subjects factor and condition (loss and gain) as the within-subjects factor to identify significant changes in ERP data. We employed the Kruskal-Wallis test to analyze the differences in ΔFN among the three groups. All analyses were considered significant at p < 0.05 with Greenhouse-Geisser corrections, and LSD’s correction was conducted for multiple comparisons.

Pearson correlation analyses were conducted to investigate the associations between self-reported psychometric scales and the amplitudes and latencies of ERP components that showed significant differences among the three groups according to post hoc analysis. Statistical significance was determined for differences with p-values <0.05.

3 Results

3.1 Demographic and clinical variables

The demographic data and clinical characteristics of participants are shown in Table 1. There were no inter-group differences in sex, age and education level. The two MDD groups (SAs and NSAs) are also comparable in terms of anxiety and depression levels. Significant differences among the three groups (SAs, NSAs, and HCs) were found in the BIS-11-CH (F (2,116) =59.27, p < 0.001). Post-hoc analyses showed that impulsivity scale scores were significantly higher in the SAs and NSAs than the HCs, with the SA group having the highest impulsivity scores.Table 1 Demographics and Clinical characteristics (N=119).

	SA	NSA	HC	Statistics F/ χ2	p value	Post hoc	
Demographics: mean(SD)							
Sample size	39	40	40				
Age(years)	21.23 ± 3.80	21.02 ± 2.87	22.23 ± 2.29	1.77	0.174	−	
Education(years)	13.84 ± 2.20	13.95 ± 2.43	14.75 ± 1.97	1.99	0.141	−	
Sex(male/female)	18/21	22/18	13/27	4.16	0.125	−	
Clinical scales: mean (SD)							
HDRS	25.05 ± 4.57	24.12 ± 3.37	−	1.49	0.308	−	
HAMA	20.48 ± 7.98	18.83 ± 4.73	−	4.83	0.263	−	
BIS-11-CH	66.23 ± 7.55	61.12 ± 7.56	48.42 ± 7.37	59.27	0.000***	SA>NSA>HC	
Note:Data are presented as mean (SD). * P<0.05,** P<0.01,*** P<0.001.

SA:Major depressive disorder with suicide attempts; NSA: Major depressive disorder without suicide attempts; HC: Healthy Control; HDRS: 24-item Hamilton Depression Rating Scale; HAMA:Hamilton Anxiety Rating Scale; BIS-11-CH: Chinese version of Barratt Impulsiveness Scale-11.

3.2 Behavioral measure (IGT)

IGT scores are shown in Fig. 2. There is a significant difference in net scores between groups (F (2,116) =18.19, p < 0.001) (Fig. 2A). Repeated measures analysis of the ten IGT block scores revealed significant changes in difference scores across blocks (F (9,116) = 15.18p < 0.001, η2 =0.116), groups (F (2,116) = 18.19, p < 0.001, η2 = 0.239), but there was no group by block interaction (F (18,116) = 1.64, p = 0.086, η2 = 0.028). Fig. 2B shows the learning curves for this analysis. Indeed, post-hoc paired comparisons revealed more negative IGT values (more disadvantageous than advantageous choices) for SAs than NSAs (p = 0.019) and HCs (p < 0.001). Similarly significant differences were found between NSAs and HCs (p < 0.001), with more negative IGT values for NSAs.Fig. 2 Behavioral performance on the IGT. (A) IGT index for all groups after 200 trials. (B)Iowa gambling task results over ten blocks. * P<0.05, ** P<0.01, *** P<0.001.

3.3 ERPs

Fig. 3 depicts the grand-average ERP waveforms for loss (orange line), and gain (blue line) among three groups at FRN and P300 different electrodes.Fig. 3 FRN and P300 waveforms evoked by loss and Gain feedback in the IGT.

3.3.1 FRN

The Kruskal-Wallis test found that a significant group difference in ΔFN (H = 11.90, p = 0.003), with HC group exhibiting significantly more negative ΔFN than MDD group (SAs and NSAs) (see Table.2). In addition, a repeated measures ANOVA showed main effect of group (F (2,116) = 7.40, p = 0.001, η2 = 0.113) and feedback (F (1,116) = 14.37, p < 0.001, η2 = 0.110) on FRN mean amplitude. There was a significant feedback by group interaction in the FRN mean amplitude data (F (2,116) = 7.51, p = 0.001, η2 = 0.115). Further post-hoc comparisons revealed a significant group difference in FRN mean amplitudes in the loss (p = 0.041) and gain feedback (p < 0.001). In the loss condition, SA participants showed more negative FRN wave compared to the NSA and HC group (p-values are 0.087 and 0.014, respectively). Under the feedback of gains, there were significant differences in average amplitude among the three groups (all p < 0.05), with the SA group having the smallest amplitude and the HC group having the largest amplitude. For FRN latency, neither the main effect of group (F (2,116) = 0.26, p = 0.770, η2 = 0.004) nor the main effect of feedback (F (1,116) = 0.008, p = 0.930, η2 = 0.001) was significant. The interaction between group and feedback also failed to be significant (F (2,116) =0.26, p = 0.773, η2 = 0.004) (see Table.3).Table 2 Kruskal-Wallis test results for ΔFN.

	SA	NSA	HC	Statistics H	p value	Post hoc	
Sample size	39	40	40				
ΔFN	0.07(−0.98,0.79)	−0.17(−0.62,0.22)	−0.73(−1.53,-0.12)	11.90	0.003**	HC<SA, NSA	
Note: Data are presented as median (P25, P75). * P<0.05, ** P<0.01, *** P<0.001.

SA:Major depressive disorder with suicide attempts; NSA: Major depressive disorder without suicide attempts; HC: Healthy Control; ΔFN: FRN-loss minus FRN-gain.

Table 3 Repeated measurement ANOVA results for amplitude and latency of target ERP components induced by IGT.

Variables	SA	NSA	HC	Main effect, F(p)	Interaction effect, F(p)	
				Group	Feedback	Group × Feedback	
Sample size	39	40	40				
FRN amplitude				14.37(0.000***)	7.40(0.001**)	7.51(0.001**)	
Loss	−0.083 ± 1.88	0.58 ± 1.42	0.89 ± 1.85				
Gain	−0.11 ± 1.74	0.86 ± 1.40	1.82 ± 2.21				
FRN latency				0.26(0.770)	0.008(0.930)	0.26(0.773)	
Loss	351.76 ± 13.96	351.10 ± 12.86	349.40 ± 13.94				
Gain	350.13 ± 13.03	351.80 ± 13.52	349.95 ± 15.01				
P300 amplitude				7.30(0.001**)	7.72(0.006**)	1.44(0.241)	
Loss	1.26 ± 1.55	1.35 ± 1.42	2.34 ± 1.92				
Gain	1.31 ± 1.08	1.76 ± 1.23	2.61 ± 1.81				
P300 latency				2.07(0.131)	2.82(0.096)	2.48(0.088)	
Loss	395.49 ± 60.25	404.90 ± 62.27	429.50 ± 57.79				
Gain	402.46 ± 54.31	393.40 ± 52.29	404.20 ± 43.68				
Note: Data are presented as mean (SD).* P<0.05,** P<0.01,*** P<0.001.

SA:Major depressive disorder with suicide attempts; NSA: Major depressive disorder without suicide attempts; HC: Healthy Control.

3.3.2 P300

P300 mean amplitudes were not comparable across groups (F (2,116) = 7.30, p = 0.001, η2 = 0.112). Meanwhile, there was a significant main effect of feedback (F (1,116) = 7.72, p = 0.006, η2 = 0.062). The two MDD group P300s had significantly smaller mean amplitudes than the HC P300s. And the P300 mean amplitudes were significantly higher for the gain than loss condition (p=0.006) among all groups. None of the interactions involving the group and feedback reached significance (F (2,116) = 1.44, p = 0.241, η2 = 0.024). On P300 latency, there was no significant effects of feedback and group, or any interactions were found (all p > 0.05) (see Table. 3).

3.4 Associations between ERP components and clinical characteristics

To control for the direction of correlation coefficients, ΔFN values were multiplied by −1. Pearson correlation analyses involving all participants (all groups) revealed that ΔFN was positively correlated with IGT index (r = 0.208, p = 0.023), and inversely correlated with impulsivity (BIS-11-CH scores) (r = −0.274, p = 0.003). Additionally, a significant negative correlation between impulsivity and IGT index was observed (r = −0.360, p<0.001), indicating that higher levels of impulsivity were associated with lower IGT net scores (Fig. 4).Fig. 4 Correlation between ΔFN, IGT index and BIS-11-CH scores under feedback. BIS-11-CH: Chinese version of Barratt Impulsiveness Scale-11. * P<0.05, ** P<0.01, *** P<0.001.

4 Discussion

The present study investigated the electrophysiological mechanisms underlying of decision-making in individuals who have attempted suicide during the Iowa Gambling Task. We found that the total BIS-11-CH scores were higher in the SA group compared to the NSA group and healthy controls. Regarding task performance, SA patients exhibited the most risky decision-making deficits compared to NSA patients and healthy participants. Our ERP data analysis showed that suicide attempters exhibited smaller amplitudes in response to both loss and gain stimuli compared to other groups. However, not entirely consistent with the hypothesis, we observed a similar blunted ΔFN in patients in both the SA and NSA groups. In addition, compared to healthy controls, MDD individuals with MDD (SAs and NSAs) demonstrated diminished allocation of cognitive resources, as evidenced by reduced mean amplitudes of P300. The ΔFN was significantly associated with both the IGT index scores and BIS-11-CH scores.

Our findings of increased impulsivity in the SA group compared to the NSA and HC group are in line with clinical observations and previous research findings (Park et al., 2020, Wang et al., 2014). Impulsivity can be broadly defined as behaviors or actions that are inappropriate, premature, unduly thought out, and risky or hasty, leading to unfavorable outcomes (Evenden, 1999), and it is thought to play an important role in several models of suicide (Baumeister, 1990, Beck et al., 1990, O'Connor and Kirtley, 2018). In addition to impulsive traits, risky decision-making (known as stateful impulsivity) has been found to be closely associated with suicidal behaviour (McHugh et al., 2019). Previous literature has indicated severe impairments in decision-making among individuals with mood disorders and a history of suicidal behavior (Bridge et al., 2012, Jollant et al., 2005, Perrain et al., 2021). Our study mirrored results showing more risky decision-making deficits in MDD patients with suicide attempts. Furthermore, NSA group also had decision-making impairments, which is also consistent with previous studies reporting impaired IGT in mental disorder groups without suicidal behavior (Bao et al., 2021, Cella et al., 2010).

For electrophysiological measurements, feedback on loss elicited a greater negative FRN in the SA group than in the other two groups, indicating that the SA group was more sensitive to loss. This conclusion is consistent with previous findings among children of parents with a history of suicide attempts (Tsypes et al., 2017). This finding may be associated with evidence suggesting that individuals who have attempted suicide may overestimate their negative emotional states (Dombrovski and Hallquist, 2017). Additionally, it has been observed that suicide attempters are at risk for experiencing multiple adverse outcomes, not limited to future suicidal behavior (Tsypes et al., 2021). The FRN results of gain feedback indicated that compared with the depression group (SAs and NSAs), the HC group showed a more positive FRN. This result aligns with prior research indicating that patients with depression are less sensitive to rewards (Foti et al., 2014, Foti et al., 2011). Importantly, our study found that the SA group had a smaller FRN mean amplitude than the NSA group during gain feedback. These findings seem to suggest a pattern of blunted neural responses in SA patients.

However, we found no differences in ΔFN between the SA group and the NSA group. Although somewhat unexpected, the results of prior studies that ΔFN in suicidal behavior have also been mixed. Prior research has indicated a stronger ΔFN in individuals attempting suicide (Pegg et al., 2020, Tsypes et al., 2021), while other studies have found a blunted ΔFN in children with suicidal ideation (Tsypes et al., 2019). These abnormal ΔFN changes are thought to be driven by a response to loss (Tsypes et al., 2019, Tsypes et al., 2021). Recent studies on children and adolescents are consistent with our results (Gallyer et al., 2023), indicating that no difference in gain or loss management between those with or without suicidal ideation. Based on our findings, the abnormal decision-making processing displayed by depressed patients with suicide attempts appears to be driven by a combination of neural responses to loss and gain trials. Therefore, this finding should be verified in future studies with larger sample sizes. Although our study did not find any differences between our two MDD group it demonstrated the presence of blunted ΔFN in both compared to healthy controls. This is consistent with previous studies showing that the depression group exhibited a smaller FRN differential wave (smaller ΔFN) (Holmes and Pizzagalli, 2010, Klawohn et al., 2021). Relatedly, depression is a disease characterized by anhedonia, and ΔFN can serve as an indicator of reward sensitivity. Therefore, our findings offer further evidence for the blunted ΔFN in depression.

Regarding the mean amplitude of P300, we observed a main effect of feedback outcome, with gain feedback eliciting a larger P300 relative to loss, consistent with previous results (Gu et al., 2010, Landes et al., 2018, Santopetro et al., 2021). Additionally, healthy controls exhibited a larger P300 than participants with MDD (SAs and NSAs). In line with previous P300 research, individuals with current depressive disorders or heightened depressive symptoms show reduced P300 amplitudes (Klawohn et al., 2020, Santopetro et al., 2021). Many previous studies have suggested that P300 represents cognitive or attentional resource allocation (Polich, 2012), and the amplitude of P300 is considered to be related to the invested cognitive resources (Polich, 2007). Thus, our research findings further support cognitive impairment in MDD patients in the field of neurophysiology.

In the present study, attenuated ΔFN was associated with impaired IGT performance and increased impulsivity. These associations indicate that SA patients are more impulsive and may not be able to learn from feedback, which is reflected in their inability to form preferences for favorable decks. Besides, decision-making deficits are linked to higher impulsivity, suggesting that decision barriers may serve as neurobiological indicators of weakened impulse control (Hollander and Rosen, 2000). In our study, correlation analysis further supports the association between decision-making deficits and heightened impulsivity.

The present study has several strengths and represents an important advancement in gaining a more precise understanding of decision-making processes in SAs. Our subjects are patients with depression who have never received medication or have received medication for less than a week, minimizing the impact of medication on our research. Furthermore, event related potentials offer high temporal resolution and the mile-second level resolution helps in separate the time course of different cognitive processes (e.g., attention and decision) in task-related activations over brain.

However, there are some limitations in this study. Firstly, this study has a small sample size and is cross-sectional. Future research should replicate our findings in larger samples and use longitudinal designs to determine if reward-related injuries are a qualitative change in SA patients. Secondly, combining EEG with high-resolution fMRI methods can pinpoint brain activity differences in SA patients during risky decision-making due to the limited spatial resolution of ERPs. Finally, as this study mainly focused on the feedback phase of decision-making, it will be crucial for future research to further differentiate the neural activity elicited by various phases of decision-making that are associated with suicide attempts.

5 Conclusion

In sum, MDD patients with suicide attempts exhibit severe decision-making defects. These impairments are not only evident at a behavioral level but also manifest as blunted ΔFN neural responses during feedback processing related to decision-making. Specifically, depressed patients who attempt suicide do not learn to avoid adverse choices and continue certain behaviors despite a range of negative consequences (e.g., suicide, self-harm). The results of this study contribute to a better understanding of the occurrence and development mechanism of MDD patients with SA, and the enhanced response to reward may provide guidance for the development of effective treatments.

Funding

This work was supported by the National Natural Science Foundation of China (82151315, 82271568, 82101573, 82301718); the Jiangsu Psychiatric Medical Innovation Center (CXZX202226); the Jiangsu Provincial Key Research and Development Program (BE2019675); the Key Project of Science and Technology Innovation for Social Development in Suzhou (2022SS04); the Jiangsu Provincial Natural Science Youth Fund (BK20230154).

CRediT authorship contribution statement

Ciqing Bao: Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation. Qiaoyang Zhang: Writing – original draft, Formal analysis, Data curation. Chen He: Formal analysis, Data curation. Haowen Zou: Supervision, Investigation. Yi Xia: Investigation. Rui Yan: Investigation. Lingling Hua: Investigation. Xiaoqin Wang: Investigation. Qing Lu: Writing – review & editing, Supervision, Conceptualization. Zhijian Yao: Writing – review & editing, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The authors do not have permission to share data.

Acknowledgements

We wish to thank all the participants and medical staff of the Affiliated Brain Hospital of Nanjing Medical University who supported our research. We want to express special gratitude to the participants and their families for their cooperation across this study.
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