Near-Infrared Light Increases Functional Connectivity with a Non-thermal Mechanism
Bibliographic record
Abstract
Abstract Although techniques for noninvasive brain stimulation are under intense investigation, an approach that has received limited attention is transcranial photobiomodulation (tPBM), the delivery of near-infrared light to the brain with a laser or light-emitting diode directed at the scalp. Here we employed functional magnetic resonance imaging to measure the blood-oxygenation-level–dependent signal in n = 20 healthy human participants while concurrently stimulating their right frontal pole with a near-infrared laser. Functional connectivity with the illuminated region increased by up to 15% during stimulation, with a quarter of all connections experiencing a significant increase. The time course of connectivity exhibited a sharp rise approximately 1 min after illumination onset. Brain-wide connectivity increases were also observed, with connections involving the stimulated hemisphere showing a significantly larger increase than those in the contralateral hemisphere. We subsequently employed magnetic resonance thermometry to measure brain temperature during tPBM (separate cohort, n = 20) and found no significant temperature differences between active and sham stimulation. Our findings suggest that near-infrared light synchronizes brain activity with a nonthermal mechanism, underscoring the promise of tPBM as a new technique for stimulating brain function.
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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) | 0.000 | 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) | 0.004 | 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".