Dose dependence and neurovascular mechanisms of the fMRI response to pulsed photobiomodulation in humans
Bibliographic record
Abstract
Abstract Background Transcranial photobiomodulation (tPBM) utilizes near-infrared light to penetrate the skull to stimulate neural tissue. However, the in vivo physiological response and the factors influencing this response in the human brain have yet to be understood. Methods In this study, we utilize functional magnetic resonance imaging (fMRI) to evaluate the effect of tPBM on the blood-oxygenation (BOLD) and cerebral blood flow (CBF), while varying stimulation parameters such as wavelength, irradiance, and frequency. We further examine the influence of skin tone and sex. We further model the neurovascular interactions underlying the response. Results Our results show that the fMRI responses to tPBM is not restrained to the site of irradiation, but quickly spreads to distal sites. Certain regions display an fMRI response sustained after tPBM cessation. Importantly, the responses are dependent on biological and stimulation parameters. Lastly, biophysical modeling revealed a consistent neurovascular coupling-like behaviour underlying these responses. Conclusion Empirical characterizations of dose dependence are critically important to brain stimulation methods in general but have yet to be demonstrated in most cases. This is the first tPBM study to do just that, establishing the foundation for precision medicine using tPBM, and sets a valuable precedent for the field of brain stimulation.
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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 | 0.000 | 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".