Suicidal Ideation and Treatment-Resistant Depression: Clinical Endophenotype Characterization and Exploration of Novel Brain Stimulation Interventions
Bibliographic record
Abstract
Suicide is a highly stigmatized symptom construct associated with multiple psychiatric conditions. Over 700,000 individuals across the world die by suicide annually. Major depressive disorder (MDD) is the most common co-morbid psychiatric illness in suicide completers. Suicidal ideation (SI) is more prevalent in individuals with treatment-resistant depression (TRD) compared to those with depression who respond to treatment. Given treatment limitations of SI in TRD, novel interventions paired with an improved understanding of the neurobiology of SI are needed. In this thesis I explore the clinical phenotype of the overlap of TRD and SI through a retrospective analysis of the largest clinical trial in depression, the STAR*D trial. Results from this analysis demonstrate the association between SI and TRD. This analysis also confirms that standard antidepressant treatments do not appear to be effective interventions for individuals with TRD and co-morbid SI, suggesting that novel interventions should be developed for patients struggling with this nexus of conditions. In another published report described in this thesis, our group characterizes the placebo response rates of TRD through a meta-analysis. Placebo response rates tended to be lower in TRD then non-TRD depression, and the implications of this finding are discussed. In the remaining four publications in this thesis, I explore novel brain stimulation interventions for the treatment of SI and TRD. Bilateral repetitive transcranial magnetic stimulation (rTMS) and magnetic seizure therapy (MST) are assessed for their efficacy through secondary analyses of large clinical trials. Findings suggest a significant effect of bilateral rTMS over sham for inducing remission of SI, and a potentially even stronger effect for MST for the treatment of SI in both unipolar and bipolar depression. The significance of these findings and the potential neurophysiological mechanisms of action are explored. Finally, this thesis contains an editorial about the promise of accelerated forms of rTMS treatment combined with a personalized medicine approach. I conclude by exploring how this paradigm of intervention could be applied in suicide prevention trials in the future directions portion of this report.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".