Peripheral Levels of the Micro-RNA MiR-1202 are Correlated with Changes in Brain Activity and Connectivity During an Antidepressant Treatment
Bibliographic record
Abstract
Introduction Micro-RNAs are short non-coding sequences playing a major role in regulating gene expression. Peripheral levels of the micro-RNA miR-1202 have been shown to predict antidepressant response and to change during treatment. However, it is not clear to what extent these peripheral measures reflect central neural changes in vivo. Objectives We aimed at investigating a potential link between peripheral micro-RNA and neuroimaging measures. Methods At baseline and after 8 weeks of desvenlafaxine (50–100 mg die), twenty depressed patients were scanned with 3 T magnetic resonance imaging, first at rest then during the Go/NoGo task, a classical test of response inhibition. Blood samples were taken for RNA extraction. Results During resting state, baseline miR-1202 levels were predictive of decreased connectivity between the posterior cingulate and the prefrontal, occipital and parietal cortices. Changes in miR-1202 levels were correlated with changes in activity in right precuneus within the default-mode network, and with decreased connectivity between the posterior cingulate and the temporal and prefrontal cortices, and the precuneus. During the Go/NoGo task, baseline levels and changes in these levels were correlated with activity changes in different regions, including bilateral prefrontal, insular, cingulate, and temporal cortices. Finally, secondary analyses suggest an association between miR-1202 levels and glutamate levels measured by spectroscopy in dorsomedial prefrontal cortex. Conclusions This is the first study showing that baseline and changes in peripheral levels of one micro-RNA were associated with changes in brain activity and connectivity during an antidepressant treatment. MiR-1202 may act through the modulation of the glutamatergic system.
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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 | 0.000 |
| 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) | 0.002 | 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".