High-Frequency Cognitive Control Training for Depression: Case Report
Bibliographic record
Abstract
BACKGROUND: Cognitive control training (CCT) has gained attention in recent years as a preventative intervention in the context of major depressive disorder. To date, uncertainty exists around the working mechanisms of CCT and how its effects unfold overtime. OBJECTIVE: This study aimed to examine cognitive and affective transfer effects following an unusually high number of training sessions. METHODS: This case report presents data of a participant completing a large amount of training sessions (n=55) over the course of 1 year in 2 training phases: 10 initial sessions, followed by 45 additional sessions. Reliable change indices were calculated for several self-report questionnaires, measuring cognitive and affective functioning. RESULTS: Cognitive task performance suggests improved cognitive functioning after training (accuracy scores increased from 43/181, 24% at baseline to 110/181, 61% shortly after training), which was maintained at follow-up (accuracy scores around 50%). Reliable change indices suggest a decrease in depressive symptoms (Beck Depression Inventory-II score decreased from 23 at baseline to 3 following initial training). Similarly, burnout symptoms following CCT showed a similar decrease. Maladaptive emotion regulation strategies displayed high variability, decreasing after periods of training but increasing when no training was performed. However, no changes in repetitive negative thinking were observed. Thematic analysis from an in-depth interview focusing on CCT adherence and user experience pointed to the importance of independency and accessibility of CCT in perceived agency, as well as the need for clear feedback mechanisms following training. CONCLUSIONS: Training task performance indicates further increases in performance beyond typical amounts of training sessions (10-20 sessions), hinting that more sessions could be beneficial for continued improvement in cognitive functioning. In line with previous research, CCT decreased depressive symptomatology. However, its effects on emotion regulation remain unclear. Further mechanistic studies into the temporal unfolding of CCT effects are necessary to investigate potential working mechanisms. TRIAL REGISTRATION: ClinicalTrials.gov NCT05166798; https://clinicaltrials.gov/study/NCT05166798.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".