Mood computational mechanisms underlying increased risk behavior in adolescent suicidal patients

Zhihao Wang; Tian Nan; Fengmei Lu; Yue Yu; Xiao Cai; Zongling He; Yuejia Luo; Ting Wang; Bastien Blain

doi:10.7554/eLife.108002.1

eLife Assessment

This valuable study combined careful computational modeling, a large patient sample, and replication in an independent general population sample to provide a computational account of a difference in risk-taking between people who have attempted suicide and those who have not. It is proposed that this difference reflects a general change in the approach to risky (high-reward) options and a lower emotional response to certain rewards. Evidence for the specificity of the effect to suicide, however, is incomplete, which would require additional analyses.

https://doi.org/10.7554/eLife.108002.1.sa4

Significance of findings

valuable: Findings that have theoretical or practical implications for a subfield

landmark
fundamental
important
valuable
useful

Strength of evidence

incomplete: Main claims are only partially supported

exceptional
compelling
convincing
solid
incomplete
inadequate

During the peer-review process the editor and reviewers write an eLife assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) and the strength of the evidence (on a scale ranging from exceptional to inadequate). Learn more about eLife assessments

Abstract

Suicidal thoughts and behaviors (STB) rank among the foremost causes of death globally. While literature consistently highlights increased risk behavior in individuals with STB and identifies mood issues as central to STB, the precise cognitive and affective computational mechanisms driving this increased risky behavior remain elusive. Here, we asked 83 adolescent inpatients with affective disorders, where 58 patients with STB (S⁺) and 25 without STB (S⁻), and 118 gender/age-matched healthy control (HC) to make decisions between certain vs. gamble option with momentary mood ratings. Choice data analyses revealed more risk behavior in S⁺ compared to S⁻ and HC. Using a prospect theory model enhanced with approach-avoidance parameters revealed that this rise in risky behavior resulted only from a heightened approach parameter in S⁺. Furthermore, approach strength mediated the rise in gambling choices with STB severity. Altogether, model-based choice data analysis indicated dysfunction in the approach system in S⁺ individuals, leading to greater propensity for gambling in favorable outcomes regardless the lotteries expected value. Additionally, mood model-based analyses revealed reduced sensitivity to certain rewards in S⁺ compared to S⁻ and HC. Importantly, these computational markers generalized to healthy population (n = 747). In S⁺, mood sensitivity to certain reward was negatively correlated with gambling, offering a mood computational account for increased risk behavior in STB. These findings remained significant even after adjusting for demographic, clinical, and medication-related variables. Overall, our study uncovers the cognitive and affective mechanisms contributing to increased risk behavior in STB, with significant implications for suicide prevention.

Introduction

Every 40 seconds, a life is lost due to suicide(1). Suicidal thoughts and behaviors (STB) are one of leading causes of death worldwide that have devastating impacts on individuals, families, and societies. STB occurs from adolescence(2,3), especially in the context of mood disorders, e.g., major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD)(4). Despite the progress made during the last 50 years for identifying risk factors (5) and developing preventing strategies(6), death rate from STB has not declined(7). The limited comprehension of cognitive and affective mechanisms creates a substantial gap in pinpointing targets for early prediction, screening, detection, and intervention in cases of suicidal thoughts. A recent and extensive literature has consistently reported increased risky decision making in patients with STB across different risk domains (for a short summary, see Table S1)(9–12). Yet, it remains whether this apparent increase in risk taking indeed corresponds to an increase in risk attitude, or a decrease in loss attitude or in value-independent choice biases, which can each result in a higher proportion of risky decisions. Understanding what is impaired in STB patients’ decision process would be key to prevent STB, for example through cognitive behavioral therapy (13,14). However, why patients with STB adopt a riskier behavior and how decisions relate to mood dynamics remain unclear.

Although meta-analyses have shown increased risk behavior in patients with STB(9,11,12), the underlying cognitive computational mechanism is still unknown. Specifically, some studies found heightened loss aversion in STB in the context of the balloon analog risk task and the gambling task (15,16), while others observed the opposite pattern using the Iowa gambling task (17). Although all these results aligned with their hypotheses (for a short summary, see Table S1), this contradictory evidence may original from the use of underspecified models. A growing literature indeed shows that behavior can be far better explained after adding approach and avoidance components to prospect theory(18,19), that is by including a decision bias in favor of the highest gain (approach) and another decision bias against the lowest loss (avoidance), above and beyond options value difference. This class of models highlights the important role of motivational components in decision making in addition to traditional (expected) reward sensitivity (e.g., loss/risk aversion)(20). Importantly, STB has been proposed in theoretical work to result from abnormal motivational system(21–24), but no direct evidence support such proposals. Therefore, investigating motivational components may facilitate understanding why STB is associated with increased risk-taking behavior. We therefore hypothesized that heightened approach motivation, or weakened avoidance motivation, would account for increased risk behavior in STB.

While suicide is a decision process per se, atypical mood dynamics have been thought to be at the core of STB(3). Various suicidal-related theories, including the interpersonal theory(25), integrated motivational-volitional model(26), and three-step theory(27,28; see Figure S1 for a summary), have proposed that STB is initially caused by low mood experience. Some official organizations, e.g., National Institute of Mental Health, have also listed mood problems as warning signals(8). Interestingly, within the framework of decision making under uncertainty, gambling on lotteries with a revealed outcome has been found to induce high mood variance(29), providing an opportunity to assess the relationship between deficient mood and increased gambling decisions in STB. Specifically, in a gambling task with momentary mood ratings (also referred to happiness or subjective well-being), where participants were asked to make decisions between certain vs. gamble options (2 possible outcomes, 50% probability for each), Rutledge et.al., (2014) found that mood was sensitive to certain reward (CR), reward expectation (EV), and reward prediction error (RPE; the difference between experienced and expected outcome)(29). Although mood is thought to persist for hours, days, or even weeks(30–33), momentary mood, measured over the timescale in the laboratory setting, represents the accumulation of the impact of multiple events at the scale of minutes (30,32,34–38). Momentary mood external validity is demonstrated e.g., through its association with depression symptoms (37). By definition, mood is different from emotions, which reflect immediate affective reactivity and is more transient (e.g. from surprise to fear) (31–33,39). Here, we investigated which mood computational component (among CR, EV and RPE) is associated with STB. We expect the mood response to gamble related quantities (EV and RPE) to be lower in STB compared to the control groups. In contrast, riskier decision may result from aversion to certain reward in STB. Therefore, another possibility is that lower mood sensitivity to CR would relate to increased risk behavior in STB.

To summarize, the aim of this study is to examine cognitive and affective computational mechanisms underlying increased risk behavior in adolescent patients with STB, as adolescent period might provide a developmental window for opportunities for early intervention(2). This study aligns with the principles of Computational Psychiatry(40), which assumes that psychiatric symptoms arise from alterations in cognitive and affective computations. The ultimate aim for this field is to uncover ‘‘computational phenotypes’’— distinct patterns of computational dysfunctions—potentially enabling targeted treatments, improved outcome predictions, and more precise diagnostic frameworks. Specifically, we employed a gambling task with momentary mood ratings to assess risk preferences and track mood fluctuations in response to various events. We applied computational models of risky decision making and momentary mood to dissect the cognitive and affective processes contributing to heightened risky behavior in patients with STB. Regarding choices, we hypothesized heightened approach motivation, or weakened avoidance motivation, in STB, which would account for increased risk behavior. Regarding mood dynamics, we hypothesized that greater mood sensitivity to gamble related variables (i.e., RPE and EV), or reduced mood sensitivity to CR, would explain increased risk behavior in STB.

Methods and materials

Participants

We recruited 95 adolescent patients with mood disorder from the Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China (The Mental Health Center of Chengdu, Sichuan, China). According to medical records and information from family and friends by the researcher (T.N) and psychiatrists (F.L, Y.Y, X.C, & Z.H), patients with suicidal thoughts and behaviors were categorized as suicidal group (S⁺), while patients without suicidal thoughts and behaviors were identified as control group (S⁻). The definition for suicidal thoughts in this study was active thoughts of suicide, i.e., wishing to die and having some intention to do so (see Supplementary Note 1 for details). This grouping operation was consistent with previous suicidal-related literature (41–46), reflecting the general tendency for suicidal risks, with important implications for suicidal prevention, especially among adolescence. As baseline control, we also recruited 124 gender- and age-matched healthy adolescents (HC). We assert that all procedures contributing to this work comply with the ethical standards of the ethical committee of The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China (number: 2022(33)) on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All procedures involving human subjects/patients were approved by the ethical committee of The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China (number: 2022(33)). Informed written consent was obtained. Patients were included if they met the following criteria: 1) both the researcher and psychiatrists agreed on their group classification; 2) they had a current diagnosis of major depressive disorder (MDD; unipolar depression), generalized anxiety disorder (GAD), or bipolar disorder with depressive episodes (BD), confirmed by two experienced psychiatrists using the Structured Clinical Interview for DSM-IV-TR-Patient Edition (SCID-P, 2/2001 revision; see Supplementary Note 1 for details);3) they were between 10 and 19 years of age; 4) they had experienced suicidal thoughts and behaviors during adolescence (ages 10-19); 5) they had no organic brain disorders, intellectual disability, or head trauma; 6) they had no history of substance abuse; 7) they had no experience of electroconvulsive therapy. In addition, participants were excluded if they failed more than 1/4 of the catch trials. The final sample consisted of 25 patients for S⁻, 58 patients for S⁺, and 118 HC participants. Note that the sample size of 25 in the control group was similar to previous suicidal- and risk decision-making-related research (47,49,51). See Table 1 and Table S2 for demographic, clinical and psychological information. The validation dataset was from our previous online study, with 747 healthy participants completing the same task and numerous anxiety/depression-related questionnaires for different purposes. See (48) for demographic and psychological details.

Demographics, clinical, psychological characteristics of patients with and without suicidal thoughts and behaviors.

Self-reported questionnaires

Participants completed a set of Chinese-version suicidal-, emotion regulation-, and depression/anxiety-related questionnaires. These measurements included the Beck Scale for Suicidal Ideation at the current time (BSI-C, 19 items) and at the worst time (BSI-W, 19 items)(50), the Childhood Trauma Questionnaire (CTQ, 28 items)(52), Emotion Regulation Questionnaire-Reappraisal (ERQ-R, 6 items) and Suppression (ERQ-S, 4 items)(53). In addition, as patients were available only for a limited duration, anxiety/depression-related scales from only 50 participants in the S⁺ group and only 21 participants in the S⁻ group were collected. Specifically, patients filled the Trait subscale of the State-Trait Anxiety Inventory (TAI; 20 items)(54), the Penn State Worry Questionnaire (PSWQ; 16 items), the Beck Depression Inventory (BDI; 21 items)(55), and the Center for Epidemiologic Studies Depression Scale (CESD; 20 items)(56).

Experimental Procedure

Participants were asked to make a choice between a certain option and a gamble (50% probability for each outcome) to maximize their points and to rate their momentary moods(18,29). Before performing the task, participants were asked to rate their current happiness that we consider as their initial mood. At the beginning of the task, participants were endowed with 500 points. Each trial started with two options (a gamble option and a certain option) which were presented randomly on each side (Figure 1A). Upon response, the chosen option was highlighted in yellow for 0.5 s. Note that Rutledge et al., (2014) displayed the chosen option for about 6 s(29), a delay we shortened for the sake of time. Then the corresponding outcome at the screen center was presented for 1 s, followed by a fixation cross with a random duration (0.6∼1.4 s). If the gamble was chosen, participants had equal probability to obtain each outcome. The obtained outcome was added to their total score, which was presenting at the top-right corner. Every 2∼3 trials, participants rated their mood (“how happy are you at this moment?”) from 0 (very unhappy) to 100 (very happy) by moving a slider anchored at midpoint (i.e., 50). Upon identifying their current mood, a fixation cross was presented with a random duration (0.6∼1.4 s). This task consisted of 90 randomly presented trials, including 30 mixed trials, 30 gain trials, and 30 loss trials. In mixed trials, participants made a choice between a certain amount 0 and a gamble with a gain amount {40, 45, or 75} and a loss amount determined by a multiplier {0.2, 0.34, 0.5, 0.64, 0.77, 0.89, 1, 1.1, 1.35, or 2} on the gain amount. For example, with a gain amount of 40 and a multiplier of 2 for the loss (2 times 40 = 80), participants chose between a certain option of 0 and a gamble option, which offered a 50% chance to win 40 and a 50% chance to loss 80. These trials are therefore particularly suited to measure loss aversion. In gain trials, there was a certain gain amount {35, 45, or 55} and a gamble with 0 and a gain amount determined by a multiplier {1.68, 1.82, 2, 2.22, 2.48, 2.8, 3.16, 3.6, 4.2, or 5} on the certain gain amount. In loss trials, there were a certain loss amount {−35, −45, or −55} and a gamble with 0 and a loss amount determined by a multiplier {1.68, 1.82, 2, 2.22, 2.48, 2.8, 3.16, 3.6, 4.2, or 5} on the certain loss amount. Many amounts and multipliers were used to provide a wide range of risk and loss sensitivity, as in previous literature. We also added an extra 4 trials in the entire task for attentional checks. For example, participants were asked to make a choice between a certain gain 20 and a gamble 35/55, where the correct response for this trial was the gamble choice (as the worst lottery outcome was higher than the certain reward). All experimental procedures were programmed using Psychopy3 (2021.2.3).

Task design, outcome and time effects on mood, and group differences in mood.

A) Gambling task with mood ratings. On each trial, participants were asked to choose between a certain option and a gambling option (self-paced). Once selected, the chosen option was highlighted in yellow for 500 ms. Then the corresponding outcome was displayed in the center of the screen for 1000 ms. The cumulated score was always shown in the right-upper corner. Every 2 to 3 trials, participants were asked to complete a self-paced rating of their happiness, answering the question “How happy are you at the moment” on a slider from 0 (very unhappy) to 100 (very happy). B) Patients and healthy controls felt happier after winning than losing. C) Mood drifted over time. D) Group difference in mood before the task show weakened mood in S+. E) Group difference in average mood displays lower mood experience in S⁺. The grey dots represent the winning model predictions. F) Mood variance was similar for all the three groups, as indexed by standard deviation of happiness ratings across the task. G) Each group earned about the same amount of point by the end of the task. Abbreviations: HC, healthy control; S⁻, patients without suicidal thoughts and behavior; S⁺, patients with suicidal thoughts and behavior; *p<0.05. Error bars correspond to the standard error.

Choice computational models

In line with previous studies(18,19), our choice model space included expected value model (cM1), prospect theory model (cM2)(57), and approach-avoidance prospect theory model (cM3)(18). For cM2 (Equations 1-4), there were 3 parameters, including risk aversion (α, range: 0.3-1.3), loss aversion (λ: 0.5-5), and inverse temperature (μ : 0-10).

where V_gain and V_loss are the objective gain and loss from a gamble, respectively. V_certain is the objective value for the certain option. U_gamble and U_certain are subjective utilities of the gamble and the certain option, respectively. Choice probability for gamble (P_gamble) is determined by the softmax rule.

Based on cM2, cM3 additionally considered motivational components when making decisions (Equations 1-3 & 5-8). That is, choice probability for P_gamble in cM3 is jointly determined by the softmax rule and approach/avoidance parameters (β_gain: - 1-1, β_loss: - 1-1).

For gain trials,

For loss trials,

Mood computational models

To quantify how different events impacted participants’ momentary mood during the gambling task, we conducted a stage-wise model construction procedure(58). That is, we added or removed each component to the model progressively, based on the best model from the previous stage. In Stage 1, we fit the classic model assuming that momentary mood depends on the recency-weighted average of the chosen certain reward (CR), expected value of the chosen gamble (EV), and reward prediction error (RPE; mM1; Equation 9). RPE was defined as the difference between the obtained and expected value.

Here, t and j are trial numbers, β₀ is a baseline mood parameter, other weights β capture the influence of different event types, γ ∈ [0,1] is a decay parameter representing how many previous trials influence happiness. CR_j is the CR if the certain option was chosen on trial j; otherwise, CR_j is 0. EVj is the EV and RPEj is the RPE on trial j if the gamble was chosen. If the certain option was chosen, then EV_j = 0 and RPEj =0.

To check that mood ratings are best explained by a shared forgetting factor (i.e., the recency-weighted history of different event types), we compared a model with a single decay parameter to an alternative model, including a forgetting factors for each event type, e.g., different decay parameters for CR, EV, and RPE (mM2; Equation 10).

Although mM1 has been shown to accurately predict mood data(29), we also fit alternative mood models. Firstly, we fit an alternative model in which mood ratings are explained by the recency-weighted average of the certain reward (CR) and the gamble reward (GR; mM3; Equation 11), a simple model providing a mood sensitivity parameter for certain rewards and gamble rewards. Secondly, we also fit a model with two forgetting factors, one for CR and one for GR (mM4; Equation 12).

In Stage 2, to identify whether mood can be better explained by different responses to better and worse gamble outcomes, we fit a model splitting GR into better and worse GR terms (mM5). We also fit a model with different decay parameters for each event based on mM5 (mM6).

In Stage 3, to check whether mood data can be better explained by a single event (CR or GR), we compared a CR-mood model (mM7) and a GR-mood model (mM8).

Model fitting and comparison

We fit model parameters by using the method of maximum likelihood estimation (MLE) with fmincon function of MATLAB (version R2015a) at the individual level. To avoid local minimum, we ran this optimization function with random starting locations 50 times. Bayesian information criteria (BIC) were used to compare model fits.

Replication of suicidal-related results in an independent dataset (n = 747)

We next verified our results in an independent dataset, including the same task and BDI questionnaire in 747 healthy participants (500 females; age: 20.90±2.41)(48). In particular, one item in BDI involves the measurement of STB. In item 9 of BDI, participants chose one option that describes them best: Option 1, “I don’t have any thoughts of killing myself.”; Option 2, “I have thoughts of killing myself, but I would not carry them out.”; Option 3, “I would like to kill myself.”; Option 4, “I would kill myself if I had the chance.”. We identified S⁺ group as choosing Option 2, 3, or 4, while participants selecting Option 1 were categorized as S⁻ group. Therefore, there were 129 participants in S⁺ and 618 participants in S⁻. We did not find significant group difference in gender and age (ps > 0.075). To make it comparable, we fit the winning choice and mood models from the clinical study.

Statistical analysis

We performed chi-square, independent-sample t-test or repeated measure ANOVA to test group-related differences. Spearman correlations were used to check correlations among suicidal-related questionnaires, choice data, and mood data. Generalized linear model was conducted for control analysis using Matlab R2015a. Mediation analyzes were conducted using R (4.1.0) and the R package ‘mediation’. All reported tests are two-tailed in addition to the replication of previous findings in the validation dataset. We set the significance level at p = 0.05. Multiple comparisons were corrected using Benjamini-Hochberg false discovery rate (FDR) correction.

Results

Demographic and clinical characteristics

Overall, gender and age were comparable among S⁺, S⁻, and HC groups (ps > 0.157), though S⁺ was significantly younger than S⁻ (t = 1.997, p = 0.049). As expected, S⁺ scored significantly higher than S⁻ and HC in suicidal-related scales (e.g., BSI-C; ps < 0.001), further validating our group manipulation. There was no significant difference between S⁺ and S⁻ in illness duration, family history, diagnosis, and various medications use (ps > 0.07), except other anxiolytics (χ²=5.434, p = 0.020). See Table 1 and Table S2 for details. For subsequent control analysis for S⁺ vs. S⁻ contrast, we included gender, illness duration, family history, diagnosis, and various medications use (except other anxiolytics) as covariates, whereas we used median split to check potential confounds of age and the use of other anxiolytics.

Sanity checks

To ensure engagement and task validation, we performed sanity checks. As expected, we found significant group differences in psychological measurements (ps < 0.001), including childhood trauma, emotion regulation, and anxiety/depression (Table 1 and Table S2). In addition, we replicated the classic mood-related effects(59,60): 1) subjects were happier after winning than losing (t = 11.001, p < 0.001; Figure 1B) and 2) mood drifted over time (t = −3.254, p = 0.001; Figure 1C). As grouping checks, we found a hierarchical pattern of mood level both before the task and across the task (S⁺ < S⁻ < HC; for initial mood, F = 53.415, p < 0.001; S⁺ vs. S⁻: t = −4.525, p < 0.001; S⁺ vs. HC: t = −10.427, p < 0.001; S⁻ vs. HC: t = −2.634, p = 0.009; Figure 1D; for mean mood, F = 28.018, p < 0.001; S⁺ vs. S⁻: t = −3.773, p < 0.001; S⁺ vs. HC: t = −7.292, p < 0.001; S⁻ vs. HC: t = −1.458, p = 0.147; Figure 1E). No significant group difference in mood variation were found (F = 1.270, p = 0.284; Figure 1F) which suggests that any parameter difference between groups is unlikely to be explained by mood variance. Moreover, there was no group difference in terms of mood drift effect, or earnings (Figure 1G; ps > 0.276).

Choice results

To replicate previous findings of increased risk behavior in suicidal populations, we conducted a two-way ANOVA on gambling rate with group (S⁺/S⁻/HC) as a between-subject factor and trial type (mix/gain/loss) as a within-subject factor. We found a significant main effect of group (F = 3.655, p = 0.028, partial η² = 0.036; Figure 2A), with more gambling behavior for S⁺ than S⁻ (two-sample t-test, t = 2.145, p = 0.035) and HC (t = 2.465, p = 0.015) and comparable gambling behavior between S- and HC (t = −0.439, p = 0.661) across the task. We also observed the main effect of trial type (F = 51.225, p < 0.001, partial η² = 0.206; gain > mix > loss). We did not observe any significant interaction effect between group and trial type (F =0.270, partial η² = 0.003). Within patients, this group effect on gambling rate remained significant after controlling for gender, illness duration, family history, diagnosis, and various medications use (ps < 0.05). There was also no significant age/other anxiolytics use difference in gambling behavior (ps > 0.109; Figure S9). In addition, there was significant correlations between gambling rate and Suicidal Ideation score at current time (BSI-C, rho = 0.233, p = 0.034; Figure 2B) and Suicidal Ideation score at worst time (BSI-W, rho = 0.219, p = 0.046) among patients.

We next performed a model comparison to select the model that best explain choice data. This analysis revealed that the winning model to formally quantify mechanisms for gambling behavior is the approach-avoidance prospect theory model (cM3; mean R² = 0.37; Table 2). Parameter and model recovery analyses showed that each model and parameter can be identified (rs > 0.91, ps< 0.001; see Supplementary Note 5; Figure S2 & S3). As predicted, we found a (marginally) significant group effect in approach parameter (F = 2.989, p = 0.053; Figure 2C), with a significant stronger approach motivation for S⁺ than S⁻ (t = 2.217, p = 0.029) and HC (t = 2.091, p = 0.038), and comparable between S- and HC (t = −0.737, p = 0.463). No other significant group difference in these parameters was found (ps > 0.135). Within patients, this group effect on the approach parameter remained significant after controlling for gender, illness duration, family history, diagnosis, and various medications use (ps < 0.05). There was also no significant age/other anxiolytics use difference in gambling behavior (ps > 0.223; Figure S9). In addition, we observed significant positive correlations of approach parameter with BSI-C (rho = 0.286, p = 0.009) and BSI-W (rho = 0.222, p = 0.044) among patients, suggesting more gambling in gain (regardless of risk attitude) for patients with high STB severity. Given significant correlations between BSI-C, approach parameter, and gambling rate (ps < 0.034), we further conducted a mediation analysis with the assumption of the mediating effect of approach motivation of suicidality on the risk behavior. Results supported our hypothesis (a×b = 0.233, 95% CI = [0.074, 0.40], p < 0.001; Figure 2D). Taken together, these choice results suggest that suicidal thoughts and behavior increase risk behavior through stronger approach motivation.

Mood results

Next, we turned to mood model comparison. We observed inconsistent mood winning models for different groups (Table 3), suggesting an effect of STB on mood dynamics. Given that the focus of the current study was STB effect, especially for the S⁺ group, with the baseline control of S⁻ and HC groups, we specially focused on the winning model from the S⁺ group. Parameter and model recovery analyses showed that each model and parameter can be identified (rs > 0.90, ps < 0.001; see Supplementary Note 5; Figure S2 & S3). The winning mood model from S⁺ assumed that momentary mood fluctuations were explained by the recency-weighted average of certain reward (CR) and the gamble reward (GR; mM3; mean R² = 0.42; Table 3). Overall, both CR and GR weights were significantly higher than 0 (CR: t = 8.033, p < 0.001; GR: t = 9.853, p < 0.001). The baseline parameter β₀ was significant correlated with the initial mood (rho = 0.580, p < 0.001), validating this model. We also replicated previous depression-related findings (61): depression symptom measured by Beck Depression Inventory (BDI) was negatively correlated with the baseline mood parameter β₀ (rho = −0.530, p < 0.001; Figure S5). We found significantly lower β₀ in S⁺ than S⁻ (F = 22.861, p <0.001; t = −3.513, p < 0.001) and HC (t = −6.606, p < 0.001), which mirrors the lower initial mood pattern. Importantly, a two-way ANOVA on mood parameters with group (S⁺/S⁻/HC) as a between-subject factor, event type (CR/GR) as a within-subject factor showed a significant main effect of group (F = 3.835, p = 0.023, partial η² = 0.037), with lower mood sensitivity for S⁺ than S⁻ (t = −2.080, p = 0.041) and HC (t = −2.758, p = 0.006) and comparable between S- and HC (t = −0.110, p = 0.913). We also observed a significant interaction effect between group and event type (F = 4.283, p = 0.015, partial η² = 0.041; Figure 3B). Simple effect analysis revealed that S⁺ group exhibited significant lower mood sensitivity to CR as compared to GR (F = 4.823, p = 0.029, partial η² = 0.024), while there was no significant CR-GR difference in S⁻ (although trendy; F = 2.783, p = 0.097, partial η ² = 0.014) and HC (F = 0.989, p = 0.321, partial η ² = 0.005). This interaction was driven by the group difference in CR (F = 6.085, p = 0.003, partial η ² = 0.058) rather than in GR (F = 0.801, p = 0.450, partial η ² = 0.008). Specifically, S⁺ showed lower mood sensitivity to CR than S⁻ (t = −2.661, p = 0.009) and HC (t = −3.381, p <0.001), while S⁻ and HC were comparable (t = 0.450, p = 0.679), suggesting S⁺ was specifically more insensitive to certain outcome than gamble outcome. No significant main event type (CR vs. GR) effect was found (F = 0.285, p = 0.594, partial η ² = 0.001). Within patients, this group effect on β_CR remained significant after controlling for gambling rate, earnings, mood-related outcome effect, mood drift effect, gender, illness duration, family history, diagnosis, and various medications use (ps < 0.032). There was also no significant age/other anxiolytics use difference in gambling behavior (ps > 0.582; Figure S9). In addition, we observed significant negative correlation between BSI-C and β_CR among patients (rho = −0.243, p = 0.027). These results indicate decreased mood sensitivity for certain reward in suicidal populations.

Effect of Suicidal thoughts and behavior on mood dynamics.

A) Group difference in mood baseline, β₀. B) Group differences in mood sensitivity to certain reward (CR) and gamble reward (GR). C) Correlation between Suicidal Ideation score at current time BSI-C and mood sensitivity to CR. D) Correlational difference in S- and S+ between mood sensitivity to CR and gambling behavior. Abbreviations: CR, certain reward; GR, gamble reward; HC, healthy control; S⁻, patients without suicidal thoughts and behavior; S⁺, patients with suicidal thoughts and behavior; BSI-C, Beck Scale for Suicidal Ideation at the current time; *p<0.05.

In addition to the winning model (mM3) from S⁺ group, we also checked results from the classic mood model (mM1). Overall, we replicated previous findings (Figure S6): 1) mood sensitivity to CR, EV, and RPE were all significantly higher than 0 (ps < 0.001); 2) higher weight for RPE than EV (t = 5.760, p < 0.001). Although no significant group difference between S⁺ and S⁻ was found in each parameter (ps > 0.115), we replicated significant correlation between BSI-C and β_CR (rho = −0.239, p = 0.030). To explore why the classic mood model (mM1) did not outperform the CR-GR model, we examined expectation effect on mood, as previous literature showed impaired value expectation in patients with STB(62). Our data suggests a lower mood sensitivity to RPE relative to EV in S⁺ than HC (Figure S6; significant interaction between group and EV/RPE: F = 3.422, p = 0.035; with stronger mood sensitivity to RPE than EV in HC (F = 36.658, p < 0.001), while no such significant difference in S⁺ (F = 1.161, p = 0.283) and S⁻ (F = 3.009, p = 0.084)). Equal weights on EV and RPE suggests that expectations cancel out as RPE is the difference between the outcome and EV, resulting in outcome only. Then, we additionally fit a mood model with CR, GR, and EV components (Figure S7). We expect less negative mood sensitivity to EV in S⁺ than HC. As expected, in addition to replication of our main results (ps < 0.045; R² for this model: 0.487), we observed a less negative mood sensitivity to EV in S⁺ than HC (t = 2.302, p = 0.023), which explains why the winning model shifts to mM3. Given that mM5 (splitting GR into better and worse terms) performed better than our winning model (mM3) in the S⁻ and HC groups, we also checked results from this model (Figure S8). Again, we found that S⁺ had significant lower β_CR than S⁻ and HC (for group effect: F = 44.660, p = 0.011; S⁺ vs. S⁻: t = −2.659, p = 0.009; S⁺ vs. HC: t = −2.589, p = 0.010; S⁻ vs. HC: t = 1.059, p = 0.292) and significant correlation between BSI-C and β_CR, (rho = −0.297, p = 0.006) among patients, suggesting the robustness of mood sensitivity to certain reward in suicidal people. The marginally significant group effect in approach parameter (p = 0.053) remain marginally significant after correction (p=0.068). In addition to this, all results of interest, including gambling chosen, approach parameter, and mood sensitivity to CR, remained significant with FDR correction (ps <=0.05).

Associations between choice and mood

To examine the association between risk behavior and atypical mood dynamics in suicidal patients, we then tested the correlation between participants’ gambling rate and mood sensitivity to certain reward (β_CR) in S⁺. We found significant negative correlation between gambling rate and β_CR in S⁺ (rho = −0.274, p = 0.037), suggesting the lower mood sensitivity to certain reward, the more gambling behavior suicidal patients made. We did not observe such a significant correlation in S⁻ (rho = 0.246, p = 0.237) and there was significant correlational difference between S⁺ and S⁻ (Z = −2.109, p = 0.017; 42)), suggesting the suicidal-specific association of mood and choice.

Replication of suicidal-related results in an independent dataset (n = 747)

Next, we collected online data on healthy volunteers in an attempt to replicate our findings. In this large online dataset, we found lower mood experience in healthy volunteers who replied non-negatively to the Suicidal item of the BDI (S⁺). Regarding the initial mood rating (before the task), S⁺ exhibited significantly lower mood than S⁻ (t = −6.077, p < 0.001; Figure 4D). There was a trend for lower mood experience across time in S⁺ than S⁻ (t = −1.600, p = 0.055; Figure 4E). Critically, we identified a significantly increased gambling behavior in S⁺ than S⁻, especially in the gain domain (t = 1.668, p = 0.048; Figure 4F). Approach-avoidance prospect theory model (mean pseudoR² = 0.479) revealed a significantly heightened approach parameter in in S⁺ than S⁻ (t = 1.762, p = 0.039; Figure 4B), but not any other choice parameters (ps > 0.172). We also replicated the previous mediation result that STB increase risk behavior through stronger approach motivation (a×b = 0.143, 95% CI = [0.016, 0.288], p = 0.031; Figure 4C). Regarding CR-GR mood model (mean R² = 0.588), we observed significantly lower β₀ in S⁺ than S⁻ (t = −2.018, p = 0.022; Figure 4F). Mood sensitivity to CR (t = −2.237, p = 0.013; Figure 4G), but not GR (t = −0. 187, p = 0.473; Figure 4G), was significantly reduced in S⁺ than S⁻. These validation results suggest that our computational markers can generalize to healthy population. However, we did not observe any significant correlation between mood sensitivity to CR and gambling behavior (ps > 0.389), which suggests that the link between mood sensitivity to CR and gambling behavior may be specifically observable in suicidal patients.

Validation of suicidal-related results in an independent dataset of healthy populations (n = 747).
A) Group difference in gambling behavior in the gain domain. B) The estimated parameters from the winning choice model (pseudo R² = 0.479) differed across groups, with higher approach behavior for S⁺. C) The mediation model among the group, β _gain, and gambling behavior in the gain condition. The approach parameter mediated the group effect on increased gambling behavior in the gain condition. D) Group difference in mood before the task show weakened mood in S⁺. E) Group difference in average mood displays lower mood experience in S⁺. FG) The estimated parameters from CR-GR mood model (mean R² = 0.588). F) Group difference in mood baseline, β₀. G) Group differences in mood sensitivity to certain reward (CR) and gamble reward (GR). Abbreviations: S⁻, healthy participants without suicidal thoughts and behavior; S⁺, healthy participants with suicidal thoughts and behavior; *p<0.05, ⁺ p<0.1.

Discussion

The current study tested cognitive and affective computational mechanisms for increased risk behavior in adolescent patients with suicidal thoughts and behaviors (STB), with a control group including adolescent patients without STB and gender/age-matched healthy control (HC). Firstly, we observed an increased gambling behavior and a lower overall mood in STB patients (S⁺), as compared to non-STB patients (S⁻) and HC, replicating previous findings(9–12). Secondly, using an approach-avoidance prospect theory model, we found heightened approach motivation in S⁺ than S⁻ and HC, which explained increased gambling choices for STB, suggesting an over-reactivity of the approach system to approach risky options. Thirdly, using a momentary mood model, we showed that lower mood sensitivity to certain outcomes in S⁺ compared to S⁻ and HC, which was driven by lower mood sensitivity to certain outcome in S⁺ than S⁻ and HC. These computational markers generalized to healthy population (n = 747). Importantly, mood hyposensitivity to certain reward specifically correlated to more gambling behavior in S⁺, offering a mood computational account for increased risk behavior in STB.

Our results suggest a unique reason for the twofold observations that STB patients display an increase in both risk taking and impulsivity, defined as a tendency to act quickly without planning while failing to inhibit a behavior that is likely to result in negative consequences(64–68). Indeed, we did not observe a difference in risk attitude per se between STB and controls but instead a higher approach behavior towards largest rewards (i.e., the lotteries) in STB patients. This would result from the value-independent term in the model that represent forms of approach in the face of gains(18,69,70). Such approach actions are elicited without regard to their actual contingent benefits and therefore corresponds to an impulsive behavior. A substantial body of research has shown that impulsivity, as assessed either through questionnaires or clinical observations, is a key predictor for STB (for a review, see Franklin et.al (2016)). Our study employed computational modeling to quantitively elucidate the altered approach-system processing for increased risky behavior in STB, offering enhanced predictive power and generalizability (40). On the other word, contrary to the proposal of atypical avoidance system(21,24), we did not observe significant group difference in avoidance, which may be attributed to the different involvement of the motivational system in learning and non-leaning contexts(19,71). In our model specification, motivational systems work in a value independent way in the non-learning context. Consistent with the view that suicide is an escape from intolerable affective states(3), risky behavior in suicidal individuals may be rewarding. In clinical practices, understanding the distortion of the approach system in STB may encourage mental health professionals to closely monitor patients who exhibit heightened approach tendencies. Such vigilance may enable early detection of risk-related behaviors, thus facilitating timely intervention strategies tailored to mitigate impulsivity-driven actions that may elevate the likelihood of STB.

Consistent with suicidal-related theories(3,72,73) and as summarized by Millner et.al (2020), we observed lower mood levels in patients with STB, regarding both initial happiness and mood baseline (the latter corresponding to the steady state mood converges to). More importantly, STB patients’ mood was less sensitive to certain outcomes than control without STB, which would lead them to take more risk regardless the gain at stake and therefore to potentially experience more suboptimal outcomes than controls(9). Although no direct causal link was established between STB and happiness ratings in response to wins or losses, recent literature has documented associations between STB and anhedonia symptoms (albeit with mixed evidence; for a review, see (73)), where anhedonia can be assessed through affective reactivity to wins versus losses ((74)). Our findings thus provide support for the presence of anhedonia in STB, particularly in response to certain outcomes. Surprisingly, mood model-based analysis did not support the effect of expectations and prediction errors on mood in healthy people (the “CR-EV-RPE model”(18,29,75)), but suggest instead a dissociation between certain outcomes and lottery outcomes (the “CR-GR model”). These two models differed with respect to the inclusion of reward expectation terms, the former including it unlike the latter. This difference can be explained by the lower expected value signal in patients with STB(62), resulting in insufficient expectation representations of the gamble option to influence mood dynamics. An alternative explanation could be the duration of the chosen option display which was considerably lower in our design than in other mood studies (e.g., 0.5 s in our study versus 6 s in (29)), which would not let enough time for expectation to be built. Within the winning CR-GR model, we observed that S⁺ specifically exhibited lower mood sensitivity to CR than GR, which was driven by mood hyposensitivity to CR in S⁺ than S⁻ and HC. This mood insensitivity was associated with STB severity, which was replicated when using the CR-EV-RPE model. Importantly, we found that mood hyposensitivity to certain reward was specifically correlated to gambling behavior in patients with STB, suggesting the potential mood computational mechanism for increased risk behavior in STB. As for clinical practices, CR-based anhedonia linked to CR (computational reactivity) in STB may prompt mental health professionals to closely monitor patients who exhibit mood insensitivity to certain daily events. This proactive monitoring could aid in identifying and addressing risk-related behaviors early on.

With replication in an independent dataset with large sample size (n = 747), this study provides robust evidence of the affective and cognitive computational mechanisms underlying heightened risky behavior in adolescents with STB. In addition, these results remained significant after controlling for demographics, social and clinical variables, medication factors, and the timing of suicidal events (Supplementary Note 3 & 4). However, this study could not differentiate between suicidal thoughts and suicidal behaviors. Although it has been shown that they represented different decision-making processes with different neural underpinnings (76–78), our data did not reveal significant differences between them (see Supplementary Note 2). Future research would benefit from examining these distinctions at the neural level. Nonetheless, by combining the suicidal ideation and suicidal attempt groups into a single STB group (41–46), our findings highlight why adolescents with suicidality exhibit a preference for risky behavior, providing computational markers for general suicidal tendency among adolescents. These findings carry important clinical implications for early prevention of adolescent suicidality. Notably, this study, like many traditional studies on suicidality (41–43,79,80), does not seek to elucidate the affective and cognitive mechanisms underlying fluctuations in suicidal thoughts. Given the inherently variable nature of suicidal ideation, recent research has increasingly adopted ecological momentary assessments to capture real-time variations in suicidal ideations(46,81,82). While such methods can help predict when suicidal ideation may arise, they fall short of explaining the underlying mechanisms driving these thoughts. In contrast, our approach, consistent with traditional literature (41–45,79), is directed at understanding why individuals with STB are more inclined toward risky behavior. We acknowledge the interaction between environmental stressor and the occurrence of STB, noting that suicidal severity often diminishes once the stressor is removed (45,83). This is a crucially important issue in current psychiatric research. For instance, patients with MDD sometimes experience a depressive episode, particularly in response to stressful events. However, collecting data during STB is both impractical and ethically challenging. Our grouping approach is based on the assumption of trait-driven STB: individuals with a history of STB, despite not during the experiment, represent a cluster of suicidal-related traits (83,84). Sensitivity analyses for STB timeframe support this assumption (see Supplementary Note 3). We also recognize that these affective and cognitive impairments may worsen under stress(45,83). Future studies would benefit from investigating how acute stress influences the propensity for risky behavior in individuals with STB.

Given that STB is a challenging multifactorial phenomenon, the development of a formal theory to quantify suicide seems necessary(21,22,85). Our cognitive and affective computational insights may pave the way for such a formal theory. Although previous literature has shown various cognitive impairments(12), e.g., executive function, in STB(86), our work is the first to quantify mood dynamics impairment and their behavioral consequences, providing insight into potential target to prevent and intervene STB. Our results indeed provide a computational mechanism for the main theories of suicide, linking low mood to suicidal behaviors. Suicide behavior is conceived to result from an intention shaped by various motivational factors (e.g., feeling of entrapment, belongness, burdensomeness(87)). The suicidal intent may then progress to suicidal behavior, which is thought to be moderated by impulsive decisions (e.g., (88)). A possibility is that the approach component becomes excessive as the suicidal intent emerges. These findings provide new insights to the putative dynamics underpinning STB, and offer potential markers for the early prediction, screening, detection, and prevention of suicidal behavior. These results would explain the observed increase in risk-taking behavior in STB such as substance use, early onset of sexual intercourse and physical fighting independent of psychiatric diagnosis.

Several limitations are worth mentioning. First, although we found that aberrant mood sensitivity explained increased risk behavior in STB, the mutual relationship between mood and risk behavior remains to be tested. For example, does mood really influence risk behavior, does risk behavior influence mood, or is there a loop between them? Second, our cross-section findings are of correlational nature. Causal relationships remain to be tested in a longitudinal study.

To conclude, this study examined cognitive and affective computational mechanisms underlying increased risk behavior in adolescent patients with suicidal thoughts and behaviors. Given very limited predictive abilities of suicide from previous risk-factor investigations(8), our study offers a potential new perspective of mood, at the core of STB, and reveals a relationship between low mood sensitivity to certain reward and an increased risk behavior in STB and possibly suggesting dysfunctional dopaminergic and serotoninergic systems. Our work has important implications for prevention of suicide, especially for clinical populations.

Acknowledgements

This study was funded by the National Natural Science Foundation of China (31920103009,62173069,62006038), the Major Project of National Social Science Foundation (20&ZD153), Shenzhen-Hong Kong Institute of Brain Science – Shenzhen Fundamental Research Institutions (2019SHIBS0003),Science and Technology Bureau of Chengdu Program (2022-YF09-00023-SN),Sichuan Province Science and Technology Support Program (2022YFS0180).

Additional information

Data Availability

All data produced in the present study are available upon reasonable request to the authors

Funding Statement

National Natural Science Foundation of China

Additional files

Supplementary materials.

References

1.
1. World Health Organization
2014Preventing suicide: A global imperativeWorld Health Organization Google Scholar
2.
1. Hawton K
2. Saunders KE
3. O’Connor RC
2012Self-harm and suicide in adolescentsThe Lancet 379:2373–2382Google Scholar
3.
1. O’Connor RC
2. Nock MK
2014The psychology of suicidal behaviourLancet Psychiatry 1:73–85Google Scholar
4.
1. Guze SB
2. Robins E
1970Suicide and Primary Affective DisordersBritish Journal of Psychiatry 117:437–438Google Scholar
5.
1. Franklin JC
2. Ribeiro JD
3. Fox KR
4. Bentley KH
5. Kleiman EM
6. Huang X
7. et al.
2017Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of researchPsychol Bull 143:187–232Google Scholar
6.
1. King CA
2. Arango A
3. Ewell Foster C
2018Emerging trends in adolescent suicide prevention researchCurr Opin Psychol 22:89–94Google Scholar
7.
1. Schmaal L
2. A-L van Harmelen
3. Chatzi V
4. Lippard ETC
5. Toenders YJ
6. Averill LA
7. et al.
2020Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studiesMol Psychiatry 25:408–427Google Scholar
8.
1. Franklin JC
2. Ribeiro JD
3. Fox KR
4. Bentley KH
5. Kleiman EM
6. Huang X
7. et al.
2017Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of researchPsychol Bull 143:187–232Google Scholar
9.
1. Sastre-Buades A
2. Alacreu-Crespo A
3. Courtet P
4. Baca-Garcia E
5. Barrigon ML
2021Decision-making in suicidal behavior: A systematic review and meta-analysisNeurosci Biobehav Rev 131:642–662Google Scholar
10.
1. Jollant F
2. Bellivier F
3. Leboyer M
4. Astruc B
5. Torres S
6. Verdier R
7. et al.
2005Impaired Decision Making in Suicide AttemptersAmerican Journal of Psychiatry 162:304–310Google Scholar
11.
1. Perrain R
2. Dardennes R
3. Jollant F
2021Risky decision-making in suicide attempters, and the choice of a violent suicidal means: an updated meta-analysisJ Affect Disord 280:241–249Google Scholar
12.
1. Richard-Devantoy S
2. Berlim MT
3. Jollant F
2014A meta-analysis of neuropsychological markers of vulnerability to suicidal behavior in mood disordersPsychol Med 44:1663–1673Google Scholar
13.
1. da Silva AG
2. Malloy-Diniz LF
3. Garcia MS
4. Figueiredo CGS
5. Figueiredo RN
6. Diaz AP
7. Palha AP
2018Cognition As a Therapeutic Target in the Suicidal Patient ApproachFront Psychiatry 9https://doi.org/10.3389/fpsyt.2018.00031 Google Scholar
14.
1. Oldershaw A
2. Simic M
3. Grima E
4. Jollant F
5. Richards C
6. Taylor L
7. Schmidt U
2012The Effect of Cognitive Behavior Therapy on Decision Making in Adolescents who Self-Harm: A Pilot StudySuicide Life Threat Behav 42:255–265Google Scholar
15.
1. Liu Q
2. Zhong R
3. Ji X
4. Law S
5. Xiao F
6. Wei Y
7. et al.
2022Decision-making biases in suicide attempters with major depressive disorder: A computational modeling study using the balloon analog risk task (BART)Depress Anxiety 39:845–857Google Scholar
16.
1. Baek K
2. Kwon J
3. Chae J-H
4. Chung YA
5. Kralik JD
6. Min J-A
7. et al.
2017Heightened aversion to risk and loss in depressed patients with a suicide attempt historySci Rep 7:11228Google Scholar
17.
1. Alacreu-Crespo A
2. Guillaume S
3. Sénèque M
4. Olié E
5. Courtet P
2020Cognitive modelling to assess decision-making impairments in patients with current depression and with/without suicide historyEuropean Neuropsychopharmacology 36:50–59Google Scholar
18.
1. Rutledge RB
2. Skandali N
3. Dayan P
4. Dolan RJ
2015Dopaminergic Modulation of Decision Making and Subjective Well-BeingJournal of Neuroscience 35:9811–9822Google Scholar
19.
1. Rutledge RB
2. Smittenaar P
3. Zeidman P
4. Brown HR
5. Adams RA
6. Lindenberger U
7. et al.
2016Risk Taking for Potential Reward Decreases across the LifespanCurrent Biology 26:1634–1639Google Scholar
20.
1. Corr PJ
2. McNaughton N
2012Neuroscience and approach/avoidance personality traits: A two stage (valuation–motivation) approachNeurosci Biobehav Rev 36:2339–2354Google Scholar
21.
1. Dombrovski AY
2. Hallquist MN
2022Search for solutions, learning, simulation, and choice processes in suicidal behaviorWIREs Cognitive Science 13https://doi.org/10.1002/wcs.1561 Google Scholar
22.
1. Karvelis P
2. Diaconescu AO
2022A Computational Model of Hopelessness and Active-Escape Bias in SuicidalityComputational Psychiatry 6:34Google Scholar
23.
1. Millner AJ
2. den Ouden HEM
3. Gershman SJ
4. Glenn CR
5. Kearns JC
6. Bornstein AM
7. et al.
2019Suicidal thoughts and behaviors are associated with an increased decision-making bias for active responses to escape aversive statesJ Abnorm Psychol 128:106–118Google Scholar
24.
1. Dombrovski AY
2. Hallquist MN
2017The decision neuroscience perspective on suicidal behaviorCurr Opin Psychiatry 30:7–14Google Scholar
25.
1. Van Orden KA
2. Witte TK
3. Cukrowicz KC
4. Braithwaite SR
5. Selby EA
6. Joiner TE
2010The interpersonal theory of suicidePsychol Rev 117:575–600Google Scholar
26.
1. O’Connor RC
2. Cleare S
3. Eschle S
4. Wetherall K
5. Kirtley OJ
2016The Integrated Motivational-Volitional Model of Suicidal Behavior
In:
1. O’Connor RC
2. Pirkis J
, editors. The International Handbook of Suicide Prevention Chichester, UK: John Wiley & Sons, Ltd pp. 220–240
Google Scholar
27.
1. Klonsky ED
2. May AM
2015The Three-Step Theory (3ST): A New Theory of Suicide Rooted in the “Ideation-to-Action” FrameworkInt J Cogn Ther 8:114–129Google Scholar
28.
1. Millner AJ
2. Robinaugh DJ
3. Nock MK
2020Advancing the Understanding of Suicide: The Need for Formal Theory and Rigorous Descriptive ResearchTrends Cogn Sci 24:704–716Google Scholar
29.
1. Rutledge RB
2. Skandali N
3. Dayan P
4. Dolan RJ
2014A computational and neural model of momentary subjective well-beingProceedings of the National Academy of Sciences 111:12252–12257Google Scholar
30.
1. Kao C-H
2. Feng GW
3. Hur JK
4. Jarvis H
5. Rutledge RB
2023Computational models of subjective feelings in psychiatryNeurosci Biobehav Rev 145:105008Google Scholar
31.
1. Emanuel A
2. Eldar E
2023Emotions as computationsNeurosci Biobehav Rev 144:104977Google Scholar
32.
1. Eldar E
2. Rutledge RB
3. Dolan RJ
4. Niv Y
2016Mood as Representation of MomentumTrends Cogn Sci 20:15–24Google Scholar
33.
1. Schiller D
2. Yu ANC
3. Alia-Klein N
4. Becker S
5. Cromwell HC
6. Dolcos F
7. et al.
2024The Human AffectomeNeurosci Biobehav Rev 158:105450Google Scholar
34.
1. Rutledge RB
2. de Berker AO
3. Espenhahn S
4. Dayan P
5. Dolan RJ
2016The social contingency of momentary subjective well-beingNat Commun 7:11825Google Scholar
35.
1. Blain B
2. Rutledge RB
2020Momentary subjective well-being depends on learning and not rewardeLife 9https://doi.org/10.7554/eLife.57977 Google Scholar
36.
1. Rutledge RB
2. Skandali N
3. Dayan P
4. Dolan RJ
2014A computational and neural model of momentary subjective well-beingProceedings of the National Academy of Sciences 111:12252–12257Google Scholar
37.
1. Rutledge RB
2. Moutoussis M
3. Smittenaar P
4. Zeidman P
5. Taylor T
6. Hrynkiewicz L
7. et al.
2017Association of Neural and Emotional Impacts of Reward Prediction Errors With Major DepressionJAMA Psychiatry 74:790Google Scholar
38.
1. Csukly G
2. Farkas K
3. Fodor T
4. Unoka Z
5. Polner B
2023Stronger coupling of emotional instability with reward processing in borderline personality disorder is predicted by schema modesPsychol Med 53:6714–6723Google Scholar
39.
1. Wang Z
2. Nan T
3. Goerlich KS
4. Li Y
5. Aleman A
6. Luo Y
7. Xu P
2023Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environmentsPLoS Biol 21:e3001724Google Scholar
40.
1. Huys QJM
2. Maia T V
3. Frank MJ
2016Computational psychiatry as a bridge from neuroscience to clinical applicationsNat Neurosci 19:404–413Google Scholar
41.
1. Glenn CR
2. Millner AJ
3. Esposito EC
4. Porter AC
5. Nock MK
2019Implicit Identification with Death Predicts Suicidal Thoughts and Behaviors in AdolescentsJournal of Clinical Child & Adolescent Psychology 48:263–272Google Scholar
42.
1. Glenn JJ
2. Werntz AJ
3. Slama SJK
4. Steinman SA
5. Teachman BA
6. Nock MK
2017Suicide and self-injury-related implicit cognition: A large-scale examination and replicationJ Abnorm Psychol 126:199–211Google Scholar
43.
1. Millner AJ
2. den Ouden HEM
3. Gershman SJ
4. Glenn CR
5. Kearns JC
6. Bornstein AM
7. et al.
2019Suicidal thoughts and behaviors are associated with an increased decision-making bias for active responses to escape aversive statesJ Abnorm Psychol 128:106–118Google Scholar
44.
1. Miller AB
2. Jenness JL
3. Elton AL
4. Pelletier-Baldelli A
5. Patel K
6. Bonar A
7. et al.
2024Neural Markers of Emotion Reactivity and Regulation Before and After a Targeted Social Rejection: Differences Among Girls With and Without Suicidal Ideation and Behavior HistoriesBiol Psychiatry 95:1100–1109Google Scholar
45.
1. Eisenlohr-Moul TA
2. Miller AB
3. Giletta M
4. Hastings PD
5. Rudolph KD
6. Nock MK
7. Prinstein MJ
2018HPA axis response and psychosocial stress as interactive predictors of suicidal ideation and behavior in adolescent females: a multilevel diathesis-stress frameworkNeuropsychopharmacology 43:2564–2571Google Scholar
46.
1. Miller AB
2. Eisenlohr-Moul T
3. Giletta M
4. Hastings PD
5. Rudolph KD
6. Nock MK
7. Prinstein MJ
2017A within-person approach to risk for suicidal ideation and suicidal behavior: Examining the roles of depression, stress, and abuse exposureJ Consult Clin Psychol 85:712–722Google Scholar
47.
1. Adan A
2. del M Capella M
3. Prat G
4. Forero DA
5. López-Vera S
6. Navarro JF
2017Executive Functioning in Men with Schizophrenia and Substance Use Disorders. Influence of Lifetime Suicide AttemptsPLoS One 12:e0169943Google Scholar
48.
1. Xu P
2. Wang Z
3. Wang T
4. Nan T
5. Xu J
6. Aleman A
7. Luo Y
8. Blain B
9. Liu Y
2023Opposed Mood Dynamics of Depression and Anxiety are related to Reward Prediction ErrorRes Sq Google Scholar
49.
1. Ponsoni A
2. Branco LD
3. Cotrena C
4. Shansis FM
5. Grassi-Oliveira R
6. Fonseca RP
2018Self-reported inhibition predicts history of suicide attempts in bipolar disorder and major depressionCompr Psychiatry 82:89–94Google Scholar
50.
1. Zhang J
2. Brown GK
2007Psychometric Properties of the Scale for Suicide Ideation in ChinaArchives of Suicide Research 11:203–210Google Scholar
51.
1. Jollant F
2. Bellivier F
3. Leboyer M
4. Astruc B
5. Torres S
6. Verdier R
7. et al.
2005Impaired Decision Making in Suicide AttemptersAmerican Journal of Psychiatry 162:304–310Google Scholar
52.
1. Zhao X
2. Zhang Y
3. Li L
4. Zhou Y
2005Evaluation on reliability and validity of Chinese version of childhood trauma questionnaireChinese Journal of Tissue Engineering Research :209–211Google Scholar
53.
1. Zhu X
2. Auerbach RP
3. Yao S
4. Abela JRZ
5. Xiao J
6. Tong X
2008Psychometric properties of the Cognitive Emotion Regulation Questionnaire: Chinese versionCogn Emot 22:288–307Google Scholar
54.
1. Shek DTL
1988Reliability and factorial structure of the Chinese version of the State-Trait Anxiety InventoryJ Psychopathol Behav Assess 10:303–317Google Scholar
55.
1. Shek DTL
1990Reliability and factorial structure of the chinese version of the Beck Depression InventoryJ Clin Psychol 46:35–43Google Scholar
56.
1. Jiang L
2. Wang Y
3. Zhang Y
4. Li R
5. Wu H
6. Li C
7. et al.
2019The Reliability and Validity of the Center for Epidemiologic Studies Depression Scale (CES-D) for Chinese University StudentsFront Psychiatry 10https://doi.org/10.3389/fpsyt.2019.00315 Google Scholar
57.
1. Kahneman L. DANIE
2. Tversky A
1979Prospect theory: An analysis of decision under riskEconometrica 47:363–391Google Scholar
58.
1. Wang Z
2. Nan T
3. Goerlich KS
4. Li Y
5. Aleman A
6. Luo Y
7. Xu P
2023Neurocomputational mechanisms underlying fear-biased adaptation learning in changing environmentsPLoS Biol 21:e3001724Google Scholar
59.
1. Blain B
2. Rutledge RB
2020Momentary subjective well-being depends on learning and not rewardeLife 9https://doi.org/10.7554/eLife.57977 Google Scholar
60.
1. Jangraw DC
2. Keren H
3. Sun H
4. Bedder RL
5. Rutledge RB
6. Pereira F
7. et al.
2023A highly replicable decline in mood during rest and simple tasksNat Hum Behav 7:596–610Google Scholar
61.
1. Rutledge RB
2. Moutoussis M
3. Smittenaar P
4. Zeidman P
5. Taylor T
6. Hrynkiewicz L
7. et al.
2017Association of Neural and Emotional Impacts of Reward Prediction Errors With Major DepressionJAMA Psychiatry 74:790Google Scholar
62.
1. Dombrovski AY
2. Szanto K
3. Clark L
4. Reynolds CF
5. Siegle GJ
2013Reward Signals, Attempted Suicide, and Impulsivity in Late-Life DepressionJAMA Psychiatry 70:1020Google Scholar
63.
1. Lenhard W
2. Lenhard A
2014Hypothesis Tests for Comparing Correlationshttps://Www.Psychometrica.de/Correlation.Html
64.
1. Ortin A
2. Lake AM
3. Kleinman M
4. Gould MS
2012Sensation seeking as risk factor for suicidal ideation and suicide attempts in adolescenceJ Affect Disord 143:214–222Google Scholar
65.
1. Horesh N
2. Gothelf D
3. Ofek H
4. Weizman T
5. Apter A
1999Impulsivity as a correlate of suicidal behavior in adolescent psychiatric inpatientsCrisis: The Journal of Crisis Intervention and Suicide Prevention 20:8–14Google Scholar
66.
1. Javdani S
2. Sadeh N
3. Verona E
2011Suicidality as a function of impulsivity, callous–unemotional traits, and depressive symptoms in youthJ Abnorm Psychol 120:400–413Google Scholar
67.
1. Kingsbury S
2. Hawton K
3. Steinhardt K
4. James A
1999Do Adolescents Who Take Overdoses Have Specific Psychological Characteristics? A Comparative Study With Psychiatric and Community ControlsJ Am Acad Child Adolesc Psychiatry 38:1125–1131Google Scholar
68.
1. Renaud J
2. Berlim MT
3. McGirr A
4. Tousignant M
5. Turecki G
2008Current psychiatric morbidity, aggression/impulsivity, and personality dimensions in child and adolescent suicide: A case-control studyJ Affect Disord 105:221–228Google Scholar
69.
1. Dayan P
2. Niv Y
3. Seymour B
4. D. Daw N
2006The misbehavior of value and the discipline of the willNeural Networks 19:1153–1160Google Scholar
70.
1. Bushong B
2. King LM
3. Camerer CF
4. Rangel A
2010Pavlovian Processes in Consumer Choice: The Physical Presence of a Good Increases Willingness-to-PayAmerican Economic Review 100:1556–1571Google Scholar
71.
1. Guitart-Masip M
2. Huys QJM
3. Fuentemilla L
4. Dayan P
5. Duzel E
6. Dolan RJ
2012Go and no-go learning in reward and punishment: Interactions between affect and effectNeuroimage 62:154–166Google Scholar
72.
1. Allen NB
2. Nelson BW
3. Brent D
4. Auerbach RP
2019Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough?J Affect Disord 250:163–169Google Scholar
73.
1. Stewart JG
2. Polanco-Roman L
3. Duarte CS
4. Auerbach RP
2019Neurocognitive Processes Implicated in Adolescent Suicidal Thoughts and Behaviors: Applying an RDoC Framework for Conceptualizing RiskCurr Behav Neurosci Rep 6:188–196Google Scholar
74.
1. Hall AF
2. Browning M
3. Huys QJM
2024The computational structure of consummatory anhedoniaTrends Cogn Sci 28:541–553Google Scholar
75.
1. Vanhasbroeck N
2. Devos L
3. Pessers S
4. Kuppens P
5. Vanpaemel W
6. Moors A
7. Tuerlinckx F
2021Testing a computational model of subjective well-being: a preregistered replication of Rutledge et al. (2014)Cogn Emot 35:822–835Google Scholar
76.
1. Wagner G
2. Li M
3. Sacchet MD
4. Richard-Devantoy S
5. Turecki G
6. Bär K-J
7. et al.
2021Functional network alterations differently associated with suicidal ideas and acts in depressed patients: an indirect support to the transition modelTransl Psychiatry 11:100Google Scholar
77.
1. Schmaal L
2. A-L van Harmelen
3. Chatzi V
4. Lippard ETC
5. Toenders YJ
6. Averill LA
7. et al.
2020Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studiesMol Psychiatry 25:408–427Google Scholar
78.
1. Saffer BY
2. Klonsky ED
2018Do neurocognitive abilities distinguish suicide attempters from suicide ideators? A systematic review of an emerging research areaClinical Psychology: Science and Practice 25:e12227Google Scholar
79.
1. Jollant F
2. Colle R
3. Nguyen TML
4. Corruble E
5. Gardier AM
6. Walter M
7. et al.
2023Ketamine and esketamine in suicidal thoughts and behaviors: a systematic reviewTher Adv Psychopharmacol 13https://doi.org/10.1177/20451253231151327 Google Scholar
80.
1. Tsypes A
2. Hallquist MN
3. Ianni A
4. Kaurin A
5. Wright AGC
6. Dombrovski AY
2024Exploration-Exploitation and Suicidal Behavior in Borderline Personality Disorder and DepressionJAMA Psychiatry 81:1010Google Scholar
81.
1. Kleiman EM
2. Turner BJ
3. Fedor S
4. Beale EE
5. Huffman JC
6. Nock MK
2017Examination of real-time fluctuations in suicidal ideation and its risk factors: Results from two ecological momentary assessment studiesJ Abnorm Psychol 126:726–738Google Scholar
82.
1. Wang SB
2. Van Genugten RDI
3. Yacoby Y
4. Pan W
5. Bentley KH
6. Bird SA
7. et al.
2024Building personalized machine learning models using real-time monitoring data to predict idiographic suicidal thoughtsNature Mental Health 2:1382–1391https://doi.org/10.1038/s44220-024-00335-w Google Scholar
83.
1. Stewart JG
2. Shields GS
3. Esposito EC
4. Cosby EA
5. Allen NB
6. Slavich GM
7. Auerbach RP
2019Life Stress and Suicide in AdolescentsJ Abnorm Child Psychol 47:1707–1722Google Scholar
84.
1. Dombrovski AY
2. Hallquist MN
2022Search for solutions, learning, simulation, and choice processes in suicidal behaviorWIREs Cognitive Science 13https://doi.org/10.1002/wcs.1561 Google Scholar
85.
1. Millner AJ
2. Robinaugh DJ
3. Nock MK
2020Advancing the Understanding of Suicide: The Need for Formal Theory and Rigorous Descriptive ResearchTrends Cogn Sci 24:704–716Google Scholar
86.
1. Bredemeier K
2. Miller IW
2015Executive function and suicidality: A systematic qualitative reviewClin Psychol Rev 40:170–183Google Scholar
87.
1. Klonsky ED
2. Saffer BY
3. Bryan CJ
2018Ideation-to-action theories of suicide: a conceptual and empirical updateCurr Opin Psychol 22:38–43Google Scholar
88.
1. O’Connor RC
2. Kirtley OJ
2018The integrated motivational–volitional model of suicidal behaviourPhilosophical Transactions of the Royal Society B: Biological Sciences 373:20170268Google Scholar

Article and author information

Author information

Zhihao Wang
Center for Neurocognition and Social Behavior, Institute of Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, China, CNRS - Centre d’Economie de la Sorbonne, Panthéon-Sorbonne University, Paris, France
ORCID iD: 0000-0001-6292-9307
- For correspondence: wangzhihao@suat-sz.edu.cn
- These authors contributed equally to this work.
Tian Nan
Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
- These authors contributed equally to this work.
Fengmei Lu
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
Yue Yu
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
Xiao Cai
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
Zongling He
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- For correspondence: hzl_811015@126.com
Yuejia Luo
Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
Ting Wang
Institute for brain research and rehabilitation, South China Normal University, Guangzhou, China
- For correspondence: wangting9312@gmail.com
Bastien Blain
CNRS - Centre d’Economie de la Sorbonne, Panthéon-Sorbonne University, Paris, France, Department of Experimental Psychology, University College London, London, United Kingdom

Author Notes

Competing interests: No competing interests declared

Version history

Preprint posted: June 10, 2025
Sent for peer review: June 19, 2025
Reviewed Preprint version 1: September 2, 2025

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.108002. This DOI represents all versions, and will always resolve to the latest one.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

views: 0
downloads: 0
citations: 0

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Significance of findings

Strength of evidence

Abstract

Introduction

Methods and materials

Participants

Demographics, clinical, psychological characteristics of patients with and without suicidal thoughts and behaviors.

Self-reported questionnaires

Experimental Procedure

Task design, outcome and time effects on mood, and group differences in mood.

Choice computational models

Mood computational models

Model fitting and comparison

Replication of suicidal-related results in an independent dataset (n = 747)

Statistical analysis

Results

Demographic and clinical characteristics

Sanity checks

Choice results

Choice results.

Choice model comparison.

Mood results

Mood model comparison.

Effect of Suicidal thoughts and behavior on mood dynamics.

Associations between choice and mood

Replication of suicidal-related results in an independent dataset (n = 747)

Validation of suicidal-related results in an independent dataset of healthy populations (n = 747).

Discussion

Acknowledgements

Additional information

Data Availability

Funding Statement

Additional files

References

Article and author information

Author information

Zhihao Wang*

Tian Nan*

Fengmei Lu

Yue Yu

Xiao Cai

Zongling He

Yuejia Luo

Ting Wang

Bastien Blain

Author Notes

Version history

Cite all versions

Copyright

Metrics

Zhihao Wang

Tian Nan