Abstract Background It has been proposed that a history of suicide attempts could be a correlate of severe depressive disorder and that suicide attempters (SA) could represent a particular subtype of ...subjects suffering from major depressive disorder. We investigated clinical and demographic characteristics associated with SA and tested the hypothesis that a history of suicide attempts predicts poor response to antidepressants. Methods One-hundred-and-forty-one SA and 670 non-SA subjects with major depressive disorder (MDD) were treated for twelve weeks with escitalopram or nortriptyline in GENDEP, a part-randomized multi-center clinical and pharmacogenetic study. Baseline characteristics were compared using linear and logistic regression. Linear mixed models were used to analyse continuous outcomes during the twelve weeks of follow-up. Results At baseline, SA subjects suffered from more severe depression (mean Montgomery-Asberg Depression Rating Scale : 30.29 (7.61) vs 28.43 (6.54), p = 0.0002), reported higher level of suicidal ideation (1.21 (0.82) vs 0.73 (0.48), p < 0.0001), had a younger age of onset and experienced more depressive episodes, had higher harm avoidance scores and poorer socio-demographic environment than non-SA individuals. However, during the twelve weeks of treatment and after adjustment for baseline severity of depression there was no difference in treatment response between SA and non-SA. Limitations Due to its retrospective design, it is possible that more severely depressed subjects might report more suicide attempts than less depressed individuals. Conclusions While SA differed from non-SA in several clinical and demographic characteristics, the antidepressants were similarly effective in SA as in comparably severely depressed subjects without a history of suicide attempts.
Objective: Some studies suggest that depressive subtypes, defined by groups of symptoms, have predictive or diagnostic utility. These studies make the implicit assumption of stability of symptoms ...across episodes in mood disorders, which has rarely been investigated.
Methods: We examined prospective data from a cohort of 3,750 individuals with bipolar I or II disorder participating in the Systematic Treatment Enhancement Program for Bipolar Disorder study, selecting a subset of individuals who experienced two depressive episodes during up to two years of follow‐up. Across‐episode association of individual depressive or hypomanic/mixed symptoms was examined using the weighted kappa measure of agreement as well as logistic regression.
Results: A total of 583 subjects experienced two prospectively observed depressive episodes, with 149 of those subjects experiencing a third. Greatest evidence of stability was observed for neurovegetative features, suicidality, and guilt/rumination. Loss of interest and fatigue were not consistent across episodes. Structural equation modeling suggested that the dimensional structure of symptoms was not invariant across episodes.
Conclusion: While the overall dimensional structure of depressive symptoms lacks temporal stability, individual symptoms including suicidality, mood, psychomotor, and neurovegetative symptoms are stable across major depressive episodes in bipolar disorder and should be considered in future investigations of course and pathophysiology in bipolar disorder.
Background It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study ...aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way. Methods and Findings The NEWMEDS consortium, an academia-industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10-8). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10-8) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D. Conclusions No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study. Please see later in the article for the Editors' Summary
Depression, a prevalent mental health disorder impacting millions globally, demands reliable assessment systems. Unlike previous studies that focus solely on either detecting depression or predicting ...its severity, our work identifies individual symptoms of depression while also predicting its severity using speech input. We leverage self-supervised learning (SSL)-based speech models to better utilize the small-sized datasets that are frequently encountered in this task. Our study demonstrates notable performance improvements by utilizing SSL embeddings compared to conventional speech features. We compare various types of SSL pretrained models to elucidate the type of speech information (semantic, speaker, or prosodic) that contributes the most in identifying different symptoms. Additionally, we evaluate the impact of combining multiple SSL embeddings on performance. Furthermore, we show the significance of multi-task learning for identifying depressive symptoms effectively.
Current automatic depression detection systems provide predictions directly without relying on the individual symptoms/items of depression as denoted in the clinical depression rating scales. In ...contrast, clinicians assess each item in the depression rating scale in a clinical setting, thus implicitly providing a more detailed rationale for a depression diagnosis. In this work, we make a first step towards using the acoustic features of speech to predict individual items of the depression rating scale before obtaining the final depression prediction. For this, we use convolutional (CNN) and recurrent (long short-term memory (LSTM)) neural networks. We consider different approaches to learning the temporal context of speech. Further, we analyze two variants of voting schemes for individual item prediction and depression detection. We also include an animated visualization that shows an example of item prediction over time as the speech progresses.
Use of socioeconomic status in health research Uher, Rudolf; Dragomirecka, Eva; Papezova, Hana ...
JAMA : the journal of the American Medical Association,
2006-Apr-19, Letnik:
295, Številka:
15
Journal Article
Previous works on depression detection use datasets collected in similar environments to train and test the models. In practice, however, the train and test distributions cannot be guaranteed to be ...identical. Distribution shifts can be introduced due to variations such as recording environment (e.g., background noise) and demographics (e.g., gender, age, etc). Such distributional shifts can surprisingly lead to severe performance degradation of the depression detection models. In this paper, we analyze the application of test-time training (TTT) to improve robustness of models trained for depression detection. When compared to regular testing of the models, we find TTT can significantly improve the robustness of the model under a variety of distributional shifts introduced due to: (a) background-noise, (b) gender-bias, and (c) data collection and curation procedure (i.e., train and test samples are from separate datasets).
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