Improved cognitive function, efficiency, saccadic eye movement, and depressive symptoms in mild cognitive impairment with transcranial photobiomodulation
Bibliographic record
Abstract
Background Mild cognitive impairment (MCI) is a critical stage with higher progression to Alzheimer's disease, yet effective interventions are still lacking. Objective Some empirical studies have shown that transcranial photobiomodulation (tPBM) may be effective in enhancing cognitive function. To further investigate its effectiveness, a controlled experiment was conducted. Methods In this study, 36 community-dwelling older adults with MCI were assigned to receive either real tPBM (experimental group; n = 25) and others without intervention (control group; n = 11) over three weeks. Participants underwent comprehensive assessments before and after the intervention, including neuropsychological tests, measurements of oxygenated hemoglobin (HbO) using function near-infrared spectroscopy during a visual working memory task, saccadic movement measurement using an eye-tracking device, and a questionnaire assessing depressive symptoms. Results Compared to the control group, the experimental group demonstrated significant improvements. They showed enhanced cognitive efficiency, as evidenced by improved visual working memory performance, reduced anti-saccade latency, higher scores in the Montreal Cognitive Assessment, and faster completion time in the Shape Trail Test B. In addition, significantly more participants in the experimental group showed improvement in depressive symptoms after the intervention. Conclusions These findings provide preliminary evidence that tPBM may effectively improve neuropsychological, physiological, and psychological outcomes in individuals with MCI. This trial was registered in the Chinese Clinical Trial Registry ( http://www.chictr.org.cn , registration number: ChiCTR2400090408).
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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) | 0.001 | 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) | 0.001 | 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".