The effect of antidepressant treatment on white matter integrity in Major Depression
Bibliographic record
Abstract
Introduction White matter abnormalities have been identified in major depressive disorder (MDD). Although several diffusion tensor imaging studies found decreased fractional anisotropy (FA) in MDD, the effect of antidepressants (AD) treatment on white matter integrity has been insufficiently studied. Objectives We sought to examine the effect of AD treatment of MDD on white matter, using DTI, in responders compared to nonresponders. Methods We included 25 individuals with MDD (HAMD >/=20) without inflammatory, unstable medical/neurological conditions or prolonged duration (> 1 year),or AD or anti-inflammatory treatment >/=1 week preceding first evaluation. Evaluation before treatment and at 16 weeks included depression rating scales, a cognitive battery, inflammatory markers and MRI. Desvenlafaxine was initiated at 50mg with a possible increase to 100mg at 8 weeks. Results Changes included: increased volume in responders in the right Inferior Fronto-Occipital fasciculus (p=0.0315) and Superior Longitudinal Fasciculus part 3 (p=0.0050); in remitters in the right Inferior Fronto-Occipital fasciculus (p=0.0359) and Superior Longitudinal Fasciculus part 2 (p<0.05) and 3 (p=0.0481); decreased volume in responders in the left Superior Longitudinal Fasciculus part 1 (p=0.0147) and left Corona Radiata(p<0.05); and in remitters in the left Superior Longitudinal Fasciculus part 1 (p=0.0109) and the Corpus Callosum part 5 (p<0.05); decreased FA in the right Cortico Spinal Tract in remitters (p=0.0175) and responders (p=0.0272), and an increase in FA in the left Uncinate Fasciculus in nonremitters (p=0.0493). These results lose significance following Bonferroni correction. Conclusions Overall, AD treatment of MDD was not associated with significant changes in FA, whole brain, or specific tract volume in this study. Disclosure This research was funded by Pfizer Canada.
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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 | 0.001 |
| 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".