Choroidal thickening and temporal RNFL thinning in TRD patients: no structural retinal changes following TMS treatment
Bibliographic record
Abstract
Treatment-resistant depression (TRD) is a particularly challenging subset of major depressive disorder (MDD), often unresponsive to conventional antidepressants. Transcranial magnetic stimulation (TMS) is recognized as an effective and reliable alternative treatment for patients with TRD. However, the effects of long-term functional changes induced by TMS treatment on retinal structures remain unclear. In this prospective study, a total of 32 patients with TRD were evaluated for retinal nerve fiber layer (RNFL), macula and choroidal thickness changes before and after TMS treatment. Patients received either intermittent theta burst stimulation (iTBS) or repetitive TMS (rTMS) targeting the left dorsolateral prefrontal cortex. The protocol consisted of five days a week, with a total of 20 daily sessions over four weeks. Retinal measurements, including central macula thickness, RNFL, and choroidal thickness, were conducted using Spectral Domain Optical Coherence Tomography (SD-OCT) and enhanced depth imaging (EDI) mode. TRD patients had thicker choroids in both eyes and reduced left eye temporal RNFL thickness compared to healthy controls (p values<0.05). TMS treatment did not produce significant changes in RNFL, macula, or choroidal thicknesses overall (p values>0.05). TRD patients exhibited significant changes in choroid and RNFL thickness, as measured by OCT imaging. TMS treatment did not result in significant alterations in these retinal measurements overall, but some retinal measurements may be affected under specific conditions such as gender, antidepressant drug type, and additional drug use. Further research focusing on these subgroups is necessary to better understand the effects of TMS on retinal structures.
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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 | 0.001 | 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".