Modulation of Brain Temporal Complexity During Treatment for Depression: A CAN-BIND-1 Study Report: Modulation de la complexité temporelle du cerveau pendant le traitement de la dépression: rapport de l’étude CAN-BIND-1
Bibliographic record
Abstract
ObjectivesCurrent pharmacological antidepressant treatments suffer from low remission rates and slow initiation of therapeutic effects. In addition, the development of new antidepressant treatments is confounded by the lack of consensus on efficient and valid neurophysiological targets. Temporal complexity is an alternative measure of dynamic brain activity that estimates brain signal variability at several timescales. It can be easily extracted from non-invasive brain recordings and provides new insights into pathophysiological mechanisms. We aim to assess the potential of brain temporal complexity as a novel neuromarker to predict the effectiveness of antidepressant treatments.MethodWe measured longitudinal changes in temporal complexity of electroencephalography signals in patients undergoing 8 weeks of escitalopram treatment through a Canadian Biomarker Integration Network in Depression (CAN-BIND) trial.ResultsAs early as 2 weeks after the start of treatment, reduction of complexity in fine timescales was associated with improvement in depressive symptoms. After 8 weeks of treatment, the treatment-related effect shifted towards an increase in coarse timescale complexity, linked to symptom improvement.ConclusionsThese results suggest a relative shift away from local, segregated information processing, measured by complexity at fine timescales, in the short term, potentially in favour of a higher long-range communication across networks, as indicated by higher complexity measures at coarse timescales in the long term. Further research into the modulation of multiscale temporal complexity by antidepressant treatments could open new possibilities for faster-acting and more efficient treatments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".