Modulating cerebrospinal fluid dynamics using pulsed photobiomodulation
Bibliographic record
Abstract
INTRODUCTION: The use of photobiomodulation (PBM) to enhance brain health, specifically glymphatic drainage and thus neurotoxic waste clearance, may make it a promising therapeutic tool against neurodegenerative diseases such as Alzheimer's disease. MATERIAL AND METHOD: This study investigates whether PBM can modulate cerebrospinal fluid (CSF) flow in 45 healthy young adults. We conducted forehead transcranial PBM (tPBM) and intranasal PBM (iPBM) at the nostril, and measured CSF dynamics using blood-oxygenation level-dependent (BOLD) functional MRI (fMRI). Our data demonstrates 4 min of PBM-induced increases in CSF flow. CONCLUSION AND RESULT: Our data shows that (1) even a short PBM of 4 min can induce a change in CSF dynamics, in the form of an immediate increase in intracranial CSF volume and a reduction in CSF inflow; (2) skin melanin had a significant effect on the CSF response in tPBM, with lighter skin associated with higher responses; (3) both iPBM and tPBM displayed a dose-dependent effect on CSF dynamics in terms of a wavelength-irradiance interaction; (4) intranasal PBM (iPBM) can be used to produce a significant change in CSF dynamics that is equivalent to forehead transcranial PBM (tPBM) with a small fraction of the irradiance. The most likely explanation for the observed fMRI signal changes in CSF regions of interest for both tPBM and iPBM is an increased CSF outflow pressure due to PBM-induced vasodilation that transiently increases intracranial CSF volume and reduces net CSF inflow. IMPACT: This study establishes that PBM can modulate CSF flow in the healthy human brain in real time. This study also suggests that iPBM may be more efficient in CSF modulation due to the proximity to the olfactory system and the lack of melanin dependence. The influence of melanin on tPBM, the feasibility of iPBM and the dose dependence of both will require further investigation in healthy and patient populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".