Neurocognition and the Suicidal Process

Rutter, Sarah; Cipriani, N; Smith, Emma; Ramjas, Elizabeth; Vaccaro, Daniel H.; Lopez, Maria Martin; Calabrese, William; Torres, Danielle; Campos-Abraham, P; Llaguno, Marian; Soto, Elia F.; Ghavami, Mona; Pérez-Rodríguez, María Mercedes

doi:10.1007/7854_2020_162

Cited by 6 publications

(6 citation statements)

References 189 publications

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“…In our study, in the most of these tests measuring episodic memory and learning, it was observed that, the number of correct responses in adolescents with depression was significantly lower than that of healthy controls, and they answered questions more slowly. Our findings are consistent with the broad literature suggesting a relationship between suicidal ideation and low cognitive function in adolescence and young adulthood (Naifeh et al, 2017;Rutter et al, 2020). When the relationship between neurocognitive impairments and suicide is examined, it has been reported that some executive functions, especially those related to emotion regulation and decision-making, constitute a permanent defect in information processing that may contribute to the risk of suicidal behavior (Allen et al, 2019).…”

Section: Discussionsupporting

confidence: 91%

A comparison of the social cognition and neurocognitive characteristics of adolescents with suicide behavior, adolescent with depression and healthy controls

Yavuz,

Turan,

Mutlu

et al. 2024

Suicide & Life Threat Behav

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IntroductionDeficits in neurocognition and social‐cognition have been suggested to be an endophenotype for suicidal behavior. We aimed to compare the social‐cognition and neurocognitive characteristics of adolescents diagnosed with depression with and without suicidal behavior and to investigate whether these functions predict suicide.MethodAdolescents diagnosed with Major Depressive Disorder (MDD) with suicidal behavior (n = 42); MDD without suicidal behavior (n = 44) and age‐ and sex‐matched controls (n = 43) were included. The University of Pennsylvania Computerized Neurobehavioral Test Battery and Autism Spectrum Screening Questionnaire (ASSQ) were used to evaluate social‐cognition and neurocognitive characteristics.ResultsSeveral neurocognitive domain values of MDD groups, were significantly different from the control group. Neutral emotion recognition task (p = 0.025) and ASSQ scores were found to be significantly impaired in the patient groups (p < 0.001). Logistic regression analysis showed that, only the increase in the Suicide Probability Scale score was found to be significant as a risk factor predicting suicide (p = 0.007, OR: 1.246).ConclusionWhile the neurocognitive and social‐cognitive performances of adolescents with MDD were significantly lower than the control group, these performances in the two depression groups were similar. When the predictors of suicidal behavior were examined, it was found that only the increase in suicidal ideation scores predicted suicide.

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Section: Discussionsupporting

confidence: 91%

A comparison of the social cognition and neurocognitive characteristics of adolescents with suicide behavior, adolescent with depression and healthy controls

Yavuz,

Turan,

Mutlu

et al. 2024

Suicide & Life Threat Behav

View full text Add to dashboard Cite

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“…We are interested in cognitive constraints because they potentially explain the inability of suicidal people to fully consider alternatives and consequences of suicide. The growing empirical literature linking suicidal behavior to cognitive deficits has been recently reviewed and subjected to meta‐analyses (K. J. D. Allen et al, 2019 ; Bredemeier & Miller, 2015 ; Cha et al, 2019 ; Jollant et al, 2011 ; Liu et al, 2017 ; Richard‐Devantoy, Berlim, & Jollant, 2014 ; Richard‐Devantoy et al, 2015 ; Rutter et al, 2020 ; Saffer & Klonsky, 2018 ). Two general patterns can be discerned.…”

Section: Suicide As a Cognitive Failurementioning

confidence: 99%

Search for solutions, learning, simulation, and choice processes in suicidal behavior

Dombrovski

Hallquist

2021

WIRES Cognitive Science

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Suicide may be viewed as an unfortunate outcome of failures in decision processes. Such failures occur when the demands of a crisis exceed a person's capacity to (i) search for options, (ii) learn and simulate possible futures, and (iii) make advantageous value-based choices. Can individual-level decision deficits and biases drive the progression of the suicidal crisis? Our overview of the evidence on this question is informed by clinical theory and grounded in reinforcement learning and behavioral economics. Cohort and case-control studies provide strong evidence that limited cognitive capacity and particularly impaired cognitive control are associated with suicidal behavior, imposing cognitive constraints on decision-making. We conceptualize suicidal ideation as an element of impoverished consideration sets resulting from a search for solutions under cognitive constraints and mood-congruent Pavlovian influences, a view supported by mostly indirect evidence. More compelling is the evidence of impaired learning in people with a history of suicidal behavior. We speculate that an inability to simulate alternative futures using one's model of the world may undermine alternative solutions in a suicidal crisis. The hypothesis supported by the strongest evidence is that the selection of suicide over alternatives is facilitated by a choice process undermined by randomness. Case-control studies using gambling tasks, armed bandits, and delay discounting support this claim. Future experimental studies will need to uncover real-time dynamics of choice processes in suicidal people. In summary, the decision process framework sheds light on neurocognitive mechanisms that facilitate the progression of the suicidal crisis.

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“…Cognitive deficits have been proposed as candidate "endophenotypes" for research on the genetics of suicide [ 5 ]. The cognitive deficits of suicidal individuals are characterized by "cognitive rigidity" [ 6 ]. In MDD with and without suicidal behavior, deficits in decision-making and processing speed have been observed in several studies [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

et al. 2022

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Background Previous studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD). Methods Participants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls (HC). All participants were required to complete a series of questionnaires, the Suicide Stroop Task (SST) and the Iowa Gambling Task (IGT). The performance in IGT was analyzed using repeated measures ANOVA. ML with extreme gradient boosting (XGBoost) classification algorithm and locally explanatory techniques assessed performance and relative importance of characteristics for predicting suicide attempts. Prediction performances were compared with the area under the curve (AUC), decision curve analysis (DCA), and net reclassification improvement (NRI). Results DSA and DNS preferred to select the card from disadvantageous decks (decks "A" + "B") under risky situation (p = 0.023) and showed a significantly poorer learning effect during the IGT (F = 2.331, p = 0.019) compared with HC. Performance of XGBoost model based on demographic and clinical characteristics was compared with that of the model created after adding cognition data (AUC, 0.779 vs. 0.819, p > 0.05). The net benefit of model was improved and cognition resulted in continuous reclassification improvement with NRI of 5.3%. Several clinical dimensions were significant predictors in the XGBoost classification algorithm. Limitations A limited sample size and failure to include sufficient suicide risk factors in the predictive model. Conclusion This study demonstrate that cognitive deficits may serve as an important risk factor to predict suicide attempts in patients with MDD. Combined with other demographic characteristics and attributes drawn from clinical questionnaires, cognitive function can improve the predictive effectiveness of the ML model. Additionally, explanatory ML models can help clinicians detect specific risk factors for each suicide attempter within MDD patients. These findings may be helpful for clinicians to detect those at high risk of suicide attempts quickly and accurately, and help them make proactive treatment decisions.

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Neurocognition and the Suicidal Process

Cited by 6 publications

References 189 publications

A comparison of the social cognition and neurocognitive characteristics of adolescents with suicide behavior, adolescent with depression and healthy controls

A comparison of the social cognition and neurocognitive characteristics of adolescents with suicide behavior, adolescent with depression and healthy controls

Search for solutions, learning, simulation, and choice processes in suicidal behavior

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

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