The effect of transcranial photobiomodulation on EEG power: variation with light pulsation frequency
Poster No:
67
Submission Type:
Abstract Submission
Authors:
Hannah Van Lankveld1,2, Alicia Mathew2, Sophie Niculescu2, Reza Zomorrodi3, Lew Lim4, Nazanin Hosseinkhah4, J. Jean Chen1,2
Institutions:
1University of Toronto, Toronto, Ontario, 2Rotman Research Institute, Baycrest, Toronto, Ontario, 3Centre for Addiction and Mental Health, Toronto, Ontario, 4Vielight Inc., Toronto, Ontario
First Author:
Hannah Van Lankveld
University of Toronto|Rotman Research Institute, Baycrest
Toronto, Ontario|Toronto, Ontario
University of Toronto|Rotman Research Institute, Baycrest
Toronto, Ontario|Toronto, Ontario
Co-Author(s):
J. Jean Chen
University of Toronto|Rotman Research Institute, Baycrest
Toronto, Ontario|Toronto, Ontario
University of Toronto|Rotman Research Institute, Baycrest
Toronto, Ontario|Toronto, Ontario
Introduction:
Transcranial photobiomodulation (tPBM) involves the delivery of near-infrared (NIR) light through the cranium to stimulate neural tissues [1]. Evidence of the interaction between NIR light and neuronal processes in the human brain will not only help establish PBM as a brain-stimulation tool on par with such established methods as transcranial magnetic stimulation but will also lead to a deeper understanding of biophotonics and their role in brain function. In PBM research, EEG has been used to show an increase in alpha (α), beta (β) and gamma (γ) power as well as a decrease in gamma (γ) power post PBM in comparison to the sham condition [2], [3], [4], [5]. However, the real-time EEG response to tPBM in humans is currently unknown. In this study, we use pulsed tPBM at two frequencies to demonstrate the real-time in vivo human EEG response in the γ band.
Methods:
EEG data from five healthy subjects (4F/1M, age 19-25) were recorded during transcranial photobiomodulation stimulation using the Magstim geodesic EEG system (256 channels). tPBM was applied using a 1064nm pulsed NIR laser with an optical power density of 150 mW/cm2. The frequency of the pulsation was alternated between 10Hz and 40Hz. The stimulation paradigm was [4-min off; 6-min on; 4-min off]. The target area was the left forehead, with the irradiated surface area and application technique remaining the same for all subjects and recordings.
EEGlab toolbox was used for data resampling, artifact removal, independent component analysis and channel rejection. A custom MATLAB script was used to divide the EEG signal into the five main frequency bands (δ, θ, α, β, γ) and to compute epoch-specific band-specific power spectra using a sliding window of 4.5 seconds. The tPBM response in these power spectra time courses for each band were assessed using a general linear model. We focused on the γ band specifically, and used cluster-based permutation thresholding to determine electrodes that responded to the stimulus.
EEGlab toolbox was used for data resampling, artifact removal, independent component analysis and channel rejection. A custom MATLAB script was used to divide the EEG signal into the five main frequency bands (δ, θ, α, β, γ) and to compute epoch-specific band-specific power spectra using a sliding window of 4.5 seconds. The tPBM response in these power spectra time courses for each band were assessed using a general linear model. We focused on the γ band specifically, and used cluster-based permutation thresholding to determine electrodes that responded to the stimulus.
Results:
Figure 1 shows both positive and negative γ power responses to tPBM at both 10Hz pulsation frequency and 40Hz. Most subjects displayed a localized frontal-lobe positive EEG response, in accordance with the site of stimulation. However, while subjects 2, 3 and 4 also showed spatially extensive negative responses, subjects 1 and 5 did not. The responses to 10Hz and 40Hz stimulation are largely spatially similar.
Figure 2 shows the average percent change in γ power, across all subjects at all significantly responding electrodes, which are also summarized as bar graphs. The 40Hz pulsation frequency elicits a larger positive response across all positively significant electrodes, and the 10Hz pulsation frequency elicits a larger negative response across all negatively significant electrodes. In both cases, a time lag of approximately 90 seconds after the onset of photobiomodulation is observed (Fig. 2a).
Figure 2 shows the average percent change in γ power, across all subjects at all significantly responding electrodes, which are also summarized as bar graphs. The 40Hz pulsation frequency elicits a larger positive response across all positively significant electrodes, and the 10Hz pulsation frequency elicits a larger negative response across all negatively significant electrodes. In both cases, a time lag of approximately 90 seconds after the onset of photobiomodulation is observed (Fig. 2a).
Conclusions:
This study demonstrates, for the first time in humans, a real-time lagged EEG-power response to tPBM. Our work further shows an important relationship between light pulsation frequency and the neuronal current response. We demonstrate localized positive γ response in contrast to an extensive negative γ response for both 10 Hz and 40 Hz pulsation. The 40 Hz stimulation elicits a greater positive response than 10 Hz. Given the lack of such data in the literature, this work is an important first step towards accelerating PBM research to the level of other brain stimulation modalities.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 1
Novel Imaging Acquisition Methods:
EEG 2
Keywords:
Acquisition
Electroencephaolography (EEG)
Other - Transcranial Photobiomodulation (tPBM)
1|2Indicates the priority used for review
·Figure 1
·Figure 2
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[2] X. Wang. (2019), "Transcranial photobiomodulation with 1064-nm laser modulates brain electroencephalogram rhythms," Neurophotonics , pp. 6(2), 025013
[3] R. Zomorrodi, G. Loheswaran, A. Pushparaj and L. Lim. (2019), "Pulsed Near Infrared Transcranial and Intranasal Photobiomodulation Significantly Modulates Neural Oscillations: a pilot exploratory study," Scientific Reports 9, p. Article 6309
[4] S. Shahdadian. (2022), "Neuromodulation of brain power topography and network topology by prefrontal transcranial photobiomodulation," Neural Eng, p. 19 066013
[5] X. Wang, H. Wanniarachchi, A. Wu and H. Liu. (2022), "Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation," Front. Hum. Neurosci