Multiomics and blood-based biomarkers of electroconvulsive therapy in severe and treatment-resistant depression: study protocol of the DetECT study
Bibliographic record
Abstract
Electroconvulsive therapy (ECT) is commonly used to treat treatment-resistant depression (TRD). However, our knowledge of the ECT-induced molecular mechanisms causing clinical improvement is limited. To address this issue, we developed the single-center, prospective observational DetECT study ("Multimodal Biomarkers of ECT in TRD"; registered 18/07/2022, www.clinicalTrials.gov , NCT05463562). Its objective is to identify molecular, psychological, socioeconomic, and clinical biomarkers of ECT response in TRD. We aim to recruit n = 134 patients in 3 years. Over the course of 12 biweekly ECT sessions (± 7 weeks), participant blood is collected before and 1 h after the first and seventh ECT and within 1 week after the twelfth session. In pilot subjects (first n = 10), additional blood draws are performed 3 and 6 h after the first ECT session to determine the optimal post-ECT blood draw interval. In blood samples, multiomic analyses are performed focusing on genotyping, epigenetics, RNA sequencing, neuron-derived exosomes, purines, and immunometabolics. To determine clinical response and side effects, participants are asked weekly to complete four standardized self-rating questionnaires on depressive and somatic symptoms. Additionally, clinician ratings are obtained three times (weeks 1, 4, and 7) within structured clinical interviews. Medical and sociodemographic data are extracted from patient records. The multimodal data collected are used to perform the conventional statistics as well as mixed linear modeling to identify clusters that link biobehavioural measures to ECT response. The DetECT study can provide important insight into the complex mechanisms of ECT in TRD and a step toward biologically informed and data-driven-based ECT biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".