Identification of cognitive predictors of remission in depression following limited effect of repetitive transcranial magnetic stimulation on hot and cold cognitive systems
Bibliographic record
Abstract
Introduction Cognitive dysfunction is a chronic and debilitating element of major depressive disorder (MDD), which manifests as abnormal processing in hot (emotion-laden) and cold (emotion-independent) cognitive systems. Although the antidepressant properties of repetitive transcranial magnetic stimulation (rTMS) are well-established, its impact on hot and cold cognition requires further elucidation. Methods Prospective study of patients with MDD undergoing an acute course of high frequency rTMS to the left dorsolateral prefrontal cortex (L-DLPFC). MDD patients ( N = 24) received a 4-to-6-week course of rTMS during which they were evaluated for depressive symptoms and completed cognitive assessments. Age-, sex-, and education-matched healthy controls ( N = 33) also completed the cognitive tasks at the same intervals as the MDD patients. Results Sustained antidepressant effect was observed following rTMS in MDD patients. Hot and cold cognition remained unaltered over the course of treatment. A pre-treatment baseline cognitive phenotype of those who achieve remission of their depressive symptoms with rTMS was identified, characterized by greater sustained attention, speed in correct identification of facial expressions, and free recall of words. Conclusion Our findings further validate the cognitive safety and clinical efficacy of rTMS as an intervention for MDD. Future research is required to further characterize the utility of pre-rTMS cognitive phenotyping in identified remitters, to aid in patient selection and treatment prognostication.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".