Neural Mechanisms and Cognitive Outcomes Of Electroconvulsive Therapy: A Transdiagnostic Approach
Bibliographic record
Abstract
Introduction.Electroconvulsive therapy (ECT) is an effective treatment for severe mental health disorders (e.g., depression, psychosis).However, cognitive side effects and neural mechanisms underlying its therapeutic effect are not well understood.This study aims to probe the mechanisms of ECT using transcranial magnetic stimulation (TMS) and investigate cognitive outcomes in a diverse psychiatric population.Methods.Sixteen individuals receiving ECT completed three assessments (baseline/72-hrs post-/1-month post-ECT).Clinical symptoms were assessed and cognition was measured with the Montreal Cognitive Assessment (MoCA) and ElectroConvulsive therapy Cognitive Assessment (ECCA).Nine of those individuals underwent TMS to probe cortical inhibition and excitation.Results.Clinical symptoms significantly improved following ECT.MoCA scores significantly declined 72-hrs post-ECT, while ECCA scores were unchanged.Cortical inhibition significantly increased post-ECT and was correlated with cognitive and clinical changes.Conclusion.Our findings suggest that cortical inhibition is involved in the mechanisms of ECT and potentially linked to clinical and cognitive outcomes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".