Impact of pretreatment interhemispheric hippocampal asymmetry on improvement in verbal learning following erythropoietin treatment in mood disorders: a randomized controlled trial
Bibliographic record
Abstract
Background: Treatment development that targets cognitive impairment is hampered by a lack of biomarkers that can predict treatment efficacy. Erythropoietin (EPO) improves verbal learning and memory in mood disorders, and this scales with an increase in left hippocampal volume. This study investigated whether pretreatment left hippocampal volume, interhemisphere hippocampal asymmetry or both influenced EPO treatment response with respect to verbal learning. Methods: Data were available for 76 of 83 patients with mood disorders from our previous EPO trials (EPO = 37 patients; placebo = 39 patients). We performed cortical reconstruction and volumetric segmentation using FreeSurfer. We conducted multiple linear regression and logistic regression to assess the influence of left hippocampal volume and hippocampal asymmetry on EPO-related memory improvement, as reflected by change in Rey Auditory Verbal Learning Test total recall from baseline to post-treatment. We set up a corresponding exploratory general linear model in FreeSurfer to assess the influence of prefrontal cortex volume on verbal learning improvement, controlling for age, sex and total intracranial volume. Results: At baseline, more rightward (left < right) hippocampal asymmetry — but not left hippocampal volume per se — was associated with greater effects of EPO versus placebo on verbal learning (p ≤ 0.05). Exploratory analysis indicated that a larger left precentral gyrus surface area was also associated with improvement of verbal learning in the EPO group compared to the placebo group (p = 0.002). Limitations: This was a secondary analysis of our original EPO trials. Conclusion: Rightward hippocampal asymmetry may convey a positive effect of EPO treatment efficacy on verbal learning. Clinical trial registration: Clinicaltrials.gov NCT00916552
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".