Non-invasive quantification of the photon fluence rate in the prefrontal cortex for transcranial photobiomodulation (tPBM)

Transcranial photobiomodulation (tPBM) has emerged as a novel non-invasive intervention for several neuropsychiatric or neurodegenerative conditions due to its neuroprotective and neuroenhancement effects by applying red/near-infrared (NIR) light to the forehead. tPBM has been applied to improve cognition in chronic traumatic brain injury, whereas tPBM-induced enhancement of the brain is dose-dependent and the effectiveness of each dose is affected by several factors such as the brain structure. In this study, we perform Monte Carlo simulations on 154 head models built with magnetic resonance images of healthy subjects, and propose a machine-learning based model that predicts the fraction of the energy of 1064-nm photons delivered to the gray matter (GM) based on the diffuse reflectance exiting the scalp surface (with wavelengths of 660, 730, 810, 850, and 940 nm) and demographic variables such as gender and age.

Automated summary

Transcranial photobiomodulation (tPBM) is a new non-invasive intervention that applies red/near-infrared light to the forehead for neuroprotective and neuroenhancement effects. A machine-learning model was proposed in this study to predict the energy distribution of photons reaching the gray matter based on diffuse reflectance and demographic variables.