Glaucoma Rehabilitation Using ElectricAI Transcranial Stimulation (GREAT)–Optimizing Stimulation Protocol for Vision Enhancement Using an RCT
Bibliographic record
Abstract
Purpose: We compared the effect of three different transcranial electrical stimulation (tES) protocols delivered to the occipital lobe on peripheral vision in patients with glaucoma. Methods: A double-masked, placebo-controlled study was conducted with 35 patients with glaucoma. We compared three different tES protocols: anodal transcranial direct current stimulation (a-tDCS), transcranial alternating current stimulation (tACS), and transcranial random noise stimulation (tRNS) against sham stimulation. Each patient attended four stimulation sessions (a-tDCS, tACS, tRNS, and sham) in a random order with at least 48 hours between visits. Stimulation involved placing an anodal electrode over the occipital lobe (Oz) and cathodal electrode on the cheek for 20 minutes. High-resolution perimetry (HRP) and multifocal visual evoked potential (mfVEP) measurements were made before and immediately after stimulation. Changes in HRP detection accuracy/reaction time and mfVEP signal-to-noise ratio (SNR)/latency were analyzed using linear mixed models. Results: Compared to sham, HRP detection accuracy was significantly improved after a-tDCS in both the central 20-degree (b = 0.032, P = 0.018) and peripheral analysis (b = 0.051, P = 0.002). Additionally, mfVEP SNR was significantly increased (b = 0.016, P = 0.017) and the latency was shortened (b = -1.405, P = 0.04) by the a-tDCS in the central 20-degree analysis. In the peripheral analysis, there was a trend toward an enhancement of SNR after a-tDCS stimulation (b = 0.014, P = 0.052), but it did not reach statistical significance; latency was increased after tACS (b = 1.623, P = 0.041). No significant effects were found in comparison to other active tES protocols. Conclusions: A single session of a-tDCS enhances perceptual and electrophysiologic measures of vision in patients with glaucoma. However, the small magnitude of changes observed in HRP (3.2% for accuracy in central and 5.1% in peripheral) did not exceed previous test variability and may not be clinically meaningful. Translational Relevance: a-tDCS holds promise as a potential treatment for enhancing visual function. However, future studies are needed to evaluate the long-term effects and clinical relevance of this intervention using validated measures of perimetric changes in the visual field.
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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) | 0.001 | 0.001 |
| 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".