Cognitive component of psychomotor retardation in unipolar and bipolar depression: <scp>I</scp>s verbal fluency a relevant marker? Impact of repetitive transcranial stimulation
Bibliographic record
Abstract
AIMS: In the literature, psychomotor retardation (PMR) is increasingly highlighted as a relevant marker for depression. Currently, we chose to focus on the fluency capacities as an evaluation of the frontal lobes functioning to reach a better understanding of cognitive and neurobiological mechanisms involved in PMR in depression. The aims of this study were: (i) to explore the cognitive component of PMR through the analysis of verbal fluency (VF) performance in unipolar and bipolar depression; and (ii) to examine whether a repetitive transcranial magnetic stimulation treatment could improve concomitantly the PMR and VF capacities, as a relevant marker characteristic of the cognitive component of PMR. METHODS: Fifteen unipolar and 15 bipolar patients were compared to 15 healthy adults. Before treatment, the results showed VF deficits, particularly marked in the bipolar group. The investigation of the interplay between PMR, VF performance, Montgomery-Åsberg Depression Rating Scale scores, and Montreal Cognitive Assessment scores showed that the deficits in these various dimensions were not homogeneous. RESULTS: The absence of correlation between the psychomotor retardation scale (the French Retardation Rating Scale for Depression) and VF, and the correlation with MoCA raise the hypothesis of a more global cognitive impairment associated with PMR in the BD group. The repetitive transcranial magnetic stimulation treatment had a positive impact on depression, PMR, and fluency scores. CONCLUSION: Correlations between the Retardation Rating Scale for Depression and VF performances appeared after treatment, showing the cognitive role of psychomotor functioning in depression. Further analyses, including other cognitive measures in an objective evaluation of PMR, are required for a better understanding of these complex relationships.
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
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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, 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".