Dosing considerations with the partial NMDA receptor agonist D-cycloserine as an adjuvant to intermittent theta-burst stimulation for major depressive disorder
Bibliographic record
Abstract
Major depressive disorder (MDD) is a leading cause of disability worldwide, with high rates of treatment resistance.Deep brain stimulation (DBS) delivers therapeutic electrical stimulation to key regions (nodes) in brain networks and is an emerging treatment for MDD.The ability to predict how perturbations to discrete nodes impact network connectivity and clinical outcome will be critical for the development of a robust theoretical foundation for depression therapy and advance personalization of DBS.Intracranial electroencephalography (iEEG) data were collected from five human subjects who were enrolled in a clinical trial of personalized closed-loop DBS for treatment resistant MDD (https://clinicaltrials. gov/ct2/show/NCT04004169, Krystal).Continuous intracranial electroencephalography (iEEG) recordings and clinical ratings of depressive symptom state were collected over 10 days when 10 depth electrodes were temporarily implanted prior to chronic DBS device placement.An unsupervised clustering framework was used to identify disease states representative of high and low symptom severity.Multi-day maps of effective brain network connectivity were constructed via single pulse stimulation and evoked potential mapping and application of graph theoretical approaches.Targeted electrical stimulation was delivered to test for therapeutic effects and potentially induce acute plasticity of the depression network.Multi-day variation in brain network connectivity structure was observed.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".