Impaired interhemispheric connectivity in medication-naive patients with major depressive disorder
Bibliographic record
Abstract
BACKGROUND: Abnormalities in the anterior interhemispheric connections provided by the corpus callosum (CC) have long been implicated in major depressive disorder (MDD). The purpose of this study was to investigate interhemispheric connectivity in medication-naive patients with MDD by measuring fractional anisotropy in the CC with diffusion tensor imaging (DTI) techniques. METHODS: We obtained DTI scans from medication-naive patients with MDD and from matched healthy controls. Fractional anisotropy values were compared using semiautomatic region of interest methods to localize the regional CC differences between these 2 groups. RESULTS: We enrolled 27 patients and 27 controls in our study. Fractional anisotropy values were significantly lower in the anterior genu of the CC in the MDD group than in the control group (p = 0.009, corrected); results were not significantly different in any other CC subregions. LIMITATIONS: As patients with MDD were already experiencing acute episodes, future studies of individuals at risk for MDD are warranted to elucidate the interhemispheric connectivity abnormalities associated with the predisposition to MDD. CONCLUSION: The findings demonstrate abnormalities in the structural integrity of the anterior genu of the CC in medication-naive individuals with MDD, which may contribute to impairment of interhemispheric connectivity in patients with this disorder.
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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".