The impulse noise of TMS inside a 3 T and 9.4 T MRI
Bibliographic record
Abstract
Introduction: Rumination is a common symptom of major depressive disorder (MDD), however, few studies assessed behavioral and physiological effects of repetitive transcranial magnetic stimulation (rTMS) on this specific symptom, which is the aim of this investigation.Methods: 61 participants (36 women) with minimum Hamilton's score of 19 were included in a double-blind study in three groups of sham, bilateral, and unilateral stimulation.Electroencephalography was recorded before and after 20 rTMS sessions.Ruminative response scale (RRS) was administered and total score as well as sub-scales of brooding and reflection were analyzed.Phase locked value (PLV) and eLORETA measures were used as the sensor and source level connectivity measures, respectively.Results: Hamilton's scores decreased after treatment in both unilateral and bilateral groups.Considering responders to the treatment, unilateral and bilateral groups significantly differed in RRS total scores, with more reductions in the bilateral group.There were significant modulations in PLV in the default mode network (DMN) in delta, theta, alpha, and beta frequencies in the bilateral group, responders of which showed decreased PLVs in DMN in beta and gamma frequency bands.Positive PLV-brooding correlations in delta and theta, and negative PLV-reflection correlations in theta, alpha, and beta frequency bands were found.In the unilateral group, increased connectivity in the DMN in beta frequency was observed, in responders of which, PLVs increased in theta, beta, and decreased in alpha and beta frequencies.Positive PLV-brooding correlations in delta and theta plus negative PLV-reflection correlations in theta frequencies were observed in DMN.Source level connectivity analyses yielded increased connectivity in DMN.Conclusion: The protocols resulted in differential DMN modulations, however, they were both effective in alleviating rumination as well as depression symptoms.DMN activity can thus be considered a candidate for treatment response prediction, especially based on specific symptoms of depression, e.g, rumination.
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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 | 0.001 | 0.002 |
| 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".