Neuropsychological Assessments of Cognitive Impairment in Major Depressive Disorder: A Systematic Review and Meta-Analysis with Meta-Regression
Bibliographic record
Abstract
INTRODUCTION: Cognitive dysfunction or deficits are common in patients with major depressive disorder (MDD). The current study systematically reviews and meta-analyzes multiple domains of cognitive impairment in patients with MDD. METHODS: PubMed/MEDLINE, PsycINFO, Cochrane Library, Embase, Web of Science, and Google Scholar were searched from inception through May 17, 2023, with no language limits. Studies with the following inclusion criteria were included: (1) patients with a diagnosis of MDD using standardized diagnostic criteria; (2) healthy controls (i.e., those without MDD); (3) neuropsychological assessments of cognitive impairment using Cambridge Neuropsychological Test Automated Battery (CANTAB); and (4) reports of sufficient data to quantify standardized effect sizes. Hedges' g standardized mean differences (SMDs) with corresponding 95% confidence intervals (CIs) were used to quantify effect sizes of cognitive impairments in MDD. SMDs were estimated using a fixed- or random-effects models. RESULTS: Overall, 33 studies consisting of 2,596 subjects (n = 1,337 for patients with MDD and n = 1,259 for healthy controls) were included. Patients with MDD, when compared to healthy controls, had moderate cognitive deficits (SMD, -0.39 [95% CI, -0.47 to -0.31]). In our subgroup analyses, patients with treatment-resistant depression (SMD, -0.56 [95% CI, -0.78 to -0.34]) and older adults with MDD (SMD, -0.51 [95% CI, -0.66 to -0.36]) had greater cognitive deficits than healthy controls. The effect size was small among unmedicated patients with MDD (SMD, -0.19 [95% CI, -0.37 to -0.00]), and we did not find any statistical difference among children. Cognitive deficits were consistently found in all domains, except the reaction time. No publication bias was reported. CONCLUSION: Because cognitive impairment in MDD can persist in remission or increase the risk of major neurodegenerative disorders, remediation of cognitive impairment in addition to alleviation of depressive symptoms should be an important goal when treating patients with MDD.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.049 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".