Hypersensitivity to negative feedback during dynamic risky-decision making in major depressive disorder: An event-related potential study

Fan, Lejia; Kong, Xinyuan; Zhang, Panwen; Lin, Pan; Zhao, Jiahui; Ji, Xinlei; Fang, Shulin; Wang, Xiaosheng; Yao, Shuqiao; Li, Huanhuan

doi:10.1016/j.jad.2021.09.019

Cited by 10 publications

(5 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Damasio proposed that making advantageous decisions can be learned through emotions generated by rewarding and punishing feedback based on previous decisions [ 32 ]. Previous studies demonstrated hyposensitivity to rewards and hypersensitivity to punishments in MDD patients (including patients with a history of suicide attempts) [ 33 ]. Thus, the intense negative emotional experience from punishing feedback may warn MDD away from choosing the disadvantageous decks repeatedly.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Concerning EEG studies, 32 were on sample healthy volunteers and 9 involved a sample suffering from alcohol abuse ( Fein and Chang, 2008 ; Fuentemilla et al, 2013 ; Lannoy et al, 2017 ; Sehrig et al, 2019 , 2020 ), anxiety ( Nash et al, 2021 ), substance abuse ( Euser et al, 2013 ; Zhong et al, 2020 ), and major depressive disorder ( Fan et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…In accordance with the initial design, the consequences of balloon explosion included a loss of reward associated with exploded balloons in all studies. The same loss was also subtracted from cumulative earnings in the permanent bank in 17 studies (9 fMRI, 7 EEG, 1 fNIRS) ( Rao et al, 2008 , 2010 ; Weber et al, 2014 ; Wei et al, 2016 ; Xu et al, 2016 , 2018 , 2019 , 2020a ; Tikàsz et al, 2019 ; Burnette et al, 2020 ; Li et al, 2020 ; Fan et al, 2021 ; Xu, 2021 ; Quan et al, 2022 ; Zhang et al, 2022b ; Zhao et al, 2023 ) and from the cumulative earnings given for research participation in 3 EEG experiments ( Xu et al, 2019 , 2020a ; Xu, 2021 ) as a penalty.…”

Section: Resultsmentioning

confidence: 99%

“…In 4 EEG studies, the mean number of adjusted pumps in trials following a positive and negative feedback was likewise calculated to show the impact of the valence of the outcomes ( Ba et al, 2016 ; Xu et al, 2020b ; Fan et al, 2021 ; Wang et al, 2021 ). In one study, the percentage to which the participants decided to inflate the balloon to a certain level (1–4) after they had made the decision to inflate the balloon to that particular level during preceding trials was calculated (e.g., participant decided to inflate the balloon to level 3, and the balloon exploded.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Adaptations of the balloon analog risk task for neuroimaging settings: a systematic review

Compagne,

Mayer,

Gabriel

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

IntroductionThe Balloon Analog Risk Task (BART), a computerized behavioral paradigm, is one of the most common tools used to assess the risk-taking propensity of an individual. Since its initial behavioral version, the BART has been adapted to neuroimaging technique to explore brain networks of risk-taking behavior. However, while there are a variety of paradigms adapted to neuroimaging to date, no consensus has been reached on the best paradigm with the appropriate parameters to study the brain during risk-taking assessed by the BART. In this review of the literature, we aimed to identify the most appropriate BART parameters to adapt the initial paradigm to neuroimaging and increase the reliability of this tool.MethodsA systematic review focused on the BART versions adapted to neuroimaging was performed in accordance with PRISMA guidelines.ResultsA total of 105 articles with 6,879 subjects identified from the PubMed database met the inclusion criteria. The BART was adapted in four neuroimaging techniques, mostly in functional magnetic resonance imaging or electroencephalography settings.DiscussionFirst, to adapt the BART to neuroimaging, a delay was included between each trial, the total number of inflations was reduced between 12 and 30 pumps, and the number of trials was increased between 80 and 100 balloons, enabling us to respect the recording constraints of neuroimaging. Second, explicit feedback about the balloon burst limited the decisions under ambiguity associated with the first trials. Third, employing an outcome index that provides more informative measures than the standard average pump score, along with a model incorporating an exponential monotonic increase in explosion probability and a maximum explosion probability between 50 and 75%, can yield a reliable estimation of risk profile. Additionally, enhancing participant motivation can be achieved by increasing the reward in line with the risk level and implementing payment based on their performance in the BART. Although there is no universal adaptation of the BART to neuroimaging, and depending on the objectives of a study, an adjustment of parameters optimizes its evaluation and clinical utility in assessing risk-taking.

show abstract

“…By 2030, MDD will be the leading cause of the burden of disease worldwide ( Collins et al, 2011 ). Plenty of studies have shown that patients with MDD have aberrant brain imaging ( Zhang et al, 2018 ; Liang et al, 2020 ; Yang et al, 2021 ), clinical ( Caldwell and Steffen, 2018 ; Kircanski et al, 2019 ), event-related potentials (ERPs; Landes et al, 2018 ; Kim et al, 2020 ; Fan et al, 2021 ; Xin et al, 2021 ), and cognitive indicators ( Pan et al, 2019 ). MDD is a heterogeneous disease ( Harald and Gordon, 2012 ), and its etiology remains vague.…”

Section: Introductionmentioning

confidence: 99%

Multiple examinations indicated associations between abnormal regional homogeneity and cognitive dysfunction in major depressive disorder

Wang

Li²,

Yan³

et al. 2023

Front. Psychol.

View full text Add to dashboard Cite

BackgroundThis study aimed to investigate the relationships between regional neural activity and multiple related indicators in patients with major depressive disorder (MDD).MethodsForty-two patients and 42 healthy controls (HCs) were enrolled. Pearson/Spearman correlation analyses were applied to examine the associations between abnormal regional homogeneity (ReHo) and different indicators in the patients.ResultsCompared with HCs, patients with MDD had increased ReHo in the left inferior temporal gyrus (ITG) and decreased ReHo values in the left putamen, anterior cingulate cortex (ACC), and precentral gyrus. The ReHo of the left putamen was positively correlated with the PR interval, Repeatable Battery for the Assessment of Neuropsychological Status 4A, and Discriminant analysis (D), and negatively correlated with Ae (block) and Ae (total) in the patients. The ReHo value of the left ACC was positively correlated with the severity of depression, Stroop Color Word Test of C − 2B + 100 in reaction time, and negatively correlated with Ce (Missay) and Perseverative Responses in the patients. The ReHo of the left ITG was positively correlated with the Neuroticism scores and negatively correlated with the Lie scores in the patients.ConclusionThese results suggested that the decreased ReHo of the salience network might be the underpinning of cognitive impairments in patients with MDD.

show abstract

Hypersensitivity to negative feedback during dynamic risky-decision making in major depressive disorder: An event-related potential study

Cited by 10 publications

References 112 publications

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

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

Adaptations of the balloon analog risk task for neuroimaging settings: a systematic review

Multiple examinations indicated associations between abnormal regional homogeneity and cognitive dysfunction in major depressive disorder

Contact Info

Product

Resources

About