Slowing Cognitive Decline in Major Depressive Disorder and Mild Cognitive Impairment
Bibliographic record
Abstract
Importance: Older adults with major depressive disorder (MDD) or mild cognitive impairment (MCI) are at high risk for cognitive decline. Objective: To assess the efficacy of cognitive remediation (CR) plus transcranial direct current stimulation (tDCS) targeting the prefrontal cortex in slowing cognitive decline, acutely improving cognition, and reducing progression to MCI or dementia in older adults with remitted MDD (rMDD), MCI, or both. Design, Setting, and Participants: This randomized clinical trial was conducted at 5 academic hospitals in Toronto, Ontario, Canada. Participants were older adults who had rMDD (with or without MCI, age ≥65 y) or MCI without rMDD (age ≥60 y). Assessments were made at baseline, month 2, and yearly from baseline for 3 to 7 years. Interventions: CR plus tDCS (hereafter, active) or sham plus sham 5 days a week for 8 weeks followed by twice-a-year 5-day boosters and daily at-home CR or sham CR. Main Outcomes and Measures: The primary outcome was change in global composite cognitive score. Secondary outcomes included changes in 6 cognitive domains, moderating effect of the diagnosis, moderating effect of APOE ε4 status, change in composite score at month 2, and progression to MCI or dementia over time. Results: Of 486 older adults who provided consent, 375 (with rMDD, MCI, or both) received at least 1 intervention session (mean [SD] age, 72.2 [6.4] years; 232 women [62%] and 143 men [38%]). Over a median follow-up of 48.3 months (range, 2.1-85.9), CR and tDCS slowed cognitive decline in older adults with rMDD or MCI (adjusted z score difference [active - sham] at month 60, 0.21; 95% CI, 0.07 to 0.35; likelihood ratio test [LRT] P = .006). In the preplanned primary analysis, CR and tDCS did not improve cognition acutely (adjusted z score difference [active - sham] at month 2, 0.06, 95% CI, -0.006 to 0.12). Similarly, the effect of CR and tDCS on delaying progression from normal cognition to MCI or MCI to dementia was weak and not significant (hazard ratio, 0.66; 95% CI, 0.40 to 1.08; P = .10). Preplanned analyses showed treatment effects for executive function (LRT P = .04) and verbal memory (LRT P = .02) and interactions with diagnosis (P = .01) and APOE ε4 (P < .001) demonstrating a larger effect among those with rMDD and in noncarriers of APOE ε4. Conclusions and Relevance: The study showed that CR and tDCS, both targeting the prefrontal cortex, is efficacious in slowing cognitive decline in older adults at risk of cognitive decline, particularly those with rMDD (with or without MCI) and in those at low genetic risk for Alzheimer disease. Trial Registration: ClinicalTrials.gov Identifier: NCT02386670.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".