Efficacy of Non-invasive Brain Stimulation on Vision: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
ABSTRACT Objective Multiple studies have explored the use of non-invasive brain stimulation (NIBS) to enhance visual function. These studies vary in sample size, outcome measures, and NIBS methodology. We conducted a systematic review and meta-analyses to assess the effects of NIBS on visual functions in human participants with normal vision. Methods We followed the PRISMA guidelines, and a review protocol was registered with PROSPERO before study commencement (CRD42021255882). We searched Embase, Medline, PsychInfo, PubMed, OpenGrey and Web of Science using relevant keywords. The search covered the period from 1 st January 2000 until 1 st September 2021. Comprehensive meta-analysis (CMA) software was used for quantitative analysis. Results Forty-nine studies were included, of which 19 were included in a meta-analysis (38.8%). Meta-analysis indicated acute (Hedges’s g=0.232, 95% CI: 0.023-0.442, p =0.029) and aftereffects (0.590, 95% CI: 0.182-0.998, p =0.005) of transcranial electrical stimulation (tES, including three different stimulation protocols) on contrast sensitivity. Visual evoked potential (VEP) amplitudes were significantly enhanced immediately after tES (0.383, 95% CI: 0.110-0.665, p =0.006). Both tES (0.563, 95% CI: 0.230 to 0.896, p =0.001)] and anodal-transcranial direct current stimulation (a-tDCS) alone (0.655, 95% CI: 0.273 to 1.038, p =0.001) reduced crowding in peripheral vision. The effects of NIBS on visual acuity, motion perception and reaction time were not statistically significant. Conclusions There are significant effects of visual cortex NIBS on contrast sensitivity, VEP amplitude, an index of cortical excitability, and crowding among normally sighted individuals. Future studies with robust experimental designs are needed to substantiate these findings in populations with vision loss. PROSPERO registration number CRD42021255882 Highlights We conducted a meta-analysis and a systematic review on the efficacy of non-invasive brain stimulation for improving on visual function Visual cortex non-invasive brain stimulation can enhance contrast sensitivity, reduce crowding in peripheral vision and enhance visually evoked potential amplitude among normally sighted individuals.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".