Simulation-based dosimetry of transcranial and intranasal photobiomodulation of the human brain: the roles of wavelength, power density and skin colour
Bibliographic record
Abstract
Abstract Photobiomodulation (PBM) is a novel technique that is actively studied for neuromodulation. However, despite the many in vivo studies, the stimulation protocols for PBM vary amongst studies, and the current understanding of neuromodulation via PBM is limited in terms of the extent of light penetration into the brain and its dosage dependence. Moreover, as near-infrared light can be absorbed by melanin in the skin, skin tone is a highly relevant but under-studied variable of interest. In this study, to address these gaps, we use Monte Carlo simulations (with MCX) of a single laser source for transcranial (tPBM) and intranasal (iPBM, nostril position) irradiated on a healthy human brain model. We investigate wavelengths of 670, 810 and 1064 nm in combination with light (“Caucasian”), medium (“Asian”) and dark (“African”) skin tones. Our simulations show that a maximum of 15% of the incidental energy for tPBM and 1% for iPBM reaches the cortex from the light source at the skin level. The rostral dorsal prefrontal cortex in tPBM and the ventromedial prefrontal cortex for iPBM accumulates the highest highest light energy, respectively for both wavelengths. Specifically, the 810 nm wavelength for tPBM and 1064 nm wavelength for iPBM produced the highest energy accumulation. Optical power density was found to be linearly correlated with energy. Moreover, we show that “Caucasian” skin allows the accumulation of higher light energy than other two skin colours. This study is the first to account for skin colour as a PBM dosing consideration, and provides evidence for hypothesis generation in in vivo studies of PBM.
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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 | 0.001 |
| 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 | 0.001 | 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".