Highlighting psychological pain avoidance and decision‐making bias as key predictors of suicide attempt in major depressive disorder—A novel investigative approach using machine learning

2021 ◽  
Author(s):  
Xinlei Ji ◽  
Jiahui Zhao ◽  
Lejia Fan ◽  
Huanhuan Li ◽  
Pan Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Major Depressive Disorder ◽  
Suicide Attempt ◽  
Depressive Disorder ◽  
Psychological Pain ◽  
Major Depressive ◽  
Investigative Approach

scholarly journals Temperament, character and decision-making characteristics of patients with major depressive disorder following a suicide attempt

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251935
Author(s):  
Klára M. Hegedűs ◽  
Bernadett I. Gál ◽  
Andrea Szkaliczki ◽  
Bálint Andó ◽  
Zoltán Janka ◽  
...  
Keyword(s):  
Decision Making ◽  
Major Depressive Disorder ◽  
Suicide Attempt ◽  
Depressive Disorder ◽  
Depressive Episode ◽  
Suicidal Behaviour ◽  
Harm Avoidance ◽  
Control Group ◽  
Major Depressive ◽  
Suicidal Crisis

Background Multiple psychological factors of suicidal behaviour have been identified so far; however, little is known about state-dependent alterations and the interplay of the most prominent components in a suicidal crisis. Thus, the combined effect of particular personality characteristics and decision-making performance was observed within individuals who recently attempted suicide during a major depressive episode. Methods Fifty-nine medication-free major depressed patients with a recent suicide attempt (within 72 h) and forty-five healthy control individuals were enrolled in this cross-sectional study. Temperament and character factors, impulsivity and decision-making performance were assessed. Statistical analyses aimed to explore between-group differences and the most powerful contributors to suicidal behaviour during a depressive episode. Results Decision-making and personality differences (i.e. impulsivity, harm avoidance, self-directedness, cooperativeness and transcendence) were observed between the patient and the control group. Among these variables, decision-making, harm avoidance and self-directedness were shown to have the strongest impact on a recent suicide attempt of individuals with a diagnosis of major depressive disorder according to the results of the binary logistic regression analysis. The model was significant, adequately fitted the data and correctly classified 79.8% of the cases. Conclusions The relevance of deficient decision-making, high harm avoidance and low self-directedness was modelled in the case of major depressed participants with a recent suicide attempt; meaning that these individuals can be described with the myopia for future consequences, a pessimistic, anxious temperament; and a character component resulting in the experience of aimlessness and helplessness. Further studies that use a within-subject design should identify and confirm additional characteristics specific to the suicidal mind.

Neurobehavioral Relationships During an Approach-Avoidance Conflict Decision-Making Task in Adults With Major Depressive Disorder During fMRI

2021 ◽  
Vol 89 (9) ◽  
pp. S362
Author(s):  
Timothy McDermott ◽  
Namik Kirlic ◽  
Ryan Smith ◽  
Elisabeth Akeman ◽  
Jessica Santiago ◽  
...  
Keyword(s):  
Decision Making ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Major Depressive ◽  
Approach Avoidance

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder

2013 ◽  
Vol 124 (10) ◽  
pp. 1975-1985 ◽  
Author(s):  
Ahmad Khodayari-Rostamabad ◽  
James P. Reilly ◽  
Gary M. Hasey ◽  
Hubert de Bruin ◽  
Duncan J. MacCrimmon
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Learning Approach ◽  
Major Depressive ◽  
Eeg Data ◽  
Machine Learning Approach ◽  
Ssri Treatment

scholarly journals Binge eating, trauma, and suicide attempt in community adults with major depressive disorder

PLoS ONE ◽  
2018 ◽  
Vol 13 (6) ◽  
pp. e0198192 ◽  
Author(s):  
Ji Hyun Baek ◽  
Kiwon Kim ◽  
Jin Pyo Hong ◽  
Maeng Je Cho ◽  
Maurizio Fava ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Suicide Attempt ◽  
Depressive Disorder ◽  
Binge Eating ◽  
Major Depressive

scholarly journals Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods

2021 ◽  
Vol 15 ◽  
Author(s):  
Shu Zhao ◽  
Zhiwei Bao ◽  
Xinyi Zhao ◽  
Mengxiang Xu ◽  
Ming D. Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Meta Analysis ◽  
Single Gene ◽  
Support Vector ◽  
Svm Classifier ◽  
Diagnostic Model ◽  
Roc Curve Analysis ◽  
Major Depressive

BackgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients’ lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes.Materials and MethodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package “MetaOmics” was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.ResultsOur analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset.ConclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.

Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder

2019 ◽  
Vol 18 (05) ◽  
pp. 1579-1603 ◽  
Author(s):  
Zhijiang Wan ◽  
Hao Zhang ◽  
Jiajin Huang ◽  
Haiyan Zhou ◽  
Jie Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Single Channel ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Method ◽  
Learning Method ◽  
Eeg Analysis ◽  
Major Depressive

Many studies developed the machine learning method for discriminating Major Depressive Disorder (MDD) and normal control based on multi-channel electroencephalogram (EEG) data, less concerned about using single channel EEG collected from forehead scalp to discriminate the MDD. The EEG dataset is collected by the Fp1 and Fp2 electrode of a 32-channel EEG system. The result demonstrates that the classification performance based on the EEG of Fp1 location exceeds the performance based on the EEG of Fp2 location, and shows that single-channel EEG analysis can provide discrimination of MDD at the level of multi-channel EEG analysis. Furthermore, a portable EEG device collecting the signal from Fp1 location is used to collect the second dataset. The Classification and Regression Tree combining genetic algorithm (GA) achieves the highest accuracy of 86.67% based on leave-one-participant-out cross validation, which shows that the single-channel EEG-based machine learning method is promising to support MDD prescreening application.

A further assessment of decision-making in anorexia nervosa

2015 ◽  
Vol 30 (1) ◽  
pp. 121-127 ◽  
Author(s):  
C. Adoue ◽  
I. Jaussent ◽  
E. Olié ◽  
S. Beziat ◽  
F. Van den Eynde ◽  
...  
Keyword(s):  
Decision Making ◽  
Major Depressive Disorder ◽  
Anorexia Nervosa ◽  
Depressive Disorder ◽  
Cognitive Processes ◽  
Depressive Disorders ◽  
Iowa Gambling Task ◽  
Learning Task ◽  
Adult Women ◽  
Major Depressive

AbstractObjective:Anorexia nervosa (AN) may be associated with impaired decision-making. Cognitive processes underlying this impairment remain unclear, mainly because previous assessments of this complex cognitive function were completed with a single test. Furthermore, clinical features such as mood status may impact this association. We aim to further explore the hypothesis of altered decision-making in AN.Method:Sixty-three adult women with AN and 49 female controls completed a clinical assessment and were assessed by three tasks related to decision-making [Iowa Gambling Task (IGT), Balloon Analogue Risk Task (BART), Probabilistic Reversal Learning Task (PRLT)].Results:People with AN had poorer performance on the IGT and made less risky choices on the BART, whereas performances were not different on PRLT. Notably, AN patients with a current major depressive disorder showed similar performance to those with no current major depressive disorder.Conclusion:These results tend to confirm an impaired decision making-process in people with AN and suggest that various cognitive processes such as inhibition to risk-taking or intolerance of uncertainty may underlie this condition Furthermore, these impairments seem unrelated to the potential co-occurent major depressive disorders.

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