The effect of stimulation parameters on the BOLD fMRI response to pulsed forehead photobiomodulation
Poster No:
51
Submission Type:
Abstract Submission
Authors:
Hannah Van Lankveld1, Joanna Chen1, Xiaole Zhong1, J. Jean Chen1
Institutions:
1University of Toronto, Rotman Research Institute, Toronto, Ontario
First Author:
Co-Author(s):
Introduction:
Photobiomodulation (PBM) is increasingly recognized as a non-invasive neuromodulation technique with positive cognitive impacts [1], and therapeutic applications for neurological conditions [2]. The mechanisms of action for PBM have been established through cell culture studies, demonstrating that near-infrared (NIR) light stimulates key cellular processes, including enhanced mitochondrial activity and improved blood flow [3]. Recently, transcranial PBM (tPBM) has been applied using pulsed light, which may prove more effective than continuous irradiation. However, the brain's response to pulsed PBM has not been mapped.
Moreover, stimulation parameters that define PBM, including wavelength, optical power density, frequency, and melanin levels, can all influence light penetration and how light interacts with neural tissues, as shown in simulations [4]. However, these theoretical expectations have not been tested experimentally. In this work, we test the effect of different stimulation parameters on the real-time PBM response using blood-oxygen-level-dependent (BOLD) fMRI.
Moreover, stimulation parameters that define PBM, including wavelength, optical power density, frequency, and melanin levels, can all influence light penetration and how light interacts with neural tissues, as shown in simulations [4]. However, these theoretical expectations have not been tested experimentally. In this work, we test the effect of different stimulation parameters on the real-time PBM response using blood-oxygen-level-dependent (BOLD) fMRI.
Methods:
Forty-one healthy adults (20–35 years), were recruited with an equal representation of sex and skin colour (three melanin groups: light, intermediate and dark). Each participant underwent four 12-minute tPBM-fMRI scans with varying optical power densities (100, 150, 200 mW/cm²), wavelengths (808, 1064 nm), and frequencies (10, 40 Hz). Thus, different subjects underwent PBM with different combinations of parameters in a randomized design. PBM was delivered to the right forehead via MRI-compatible lasers.
Data was collected on a Siemens Prisma 3T scanner using DE-pCASL (TR = 4.5 s; TE1/TE2 = 9.4/30 ms) with a [4-min-OFF, 4-min-ON, 4-min-OFF] stimulation protocol. A custom preprocessing script employed the FMRIB Software Library (FSL) for brain extraction, motion correction, slice timing and registration. Independent component analysis was applied with FSL MELODIC for model-free detection of stimulus-driven BOLD response, followed by dual regression to identify significant regions of interest, where the effects of the stimulation parameters were then analyzed. A linear mixed effects analysis (Figure 2), modeled the average response of the last minute of stimulation, as a function of melanin level, wavelength, and frequency.
Data was collected on a Siemens Prisma 3T scanner using DE-pCASL (TR = 4.5 s; TE1/TE2 = 9.4/30 ms) with a [4-min-OFF, 4-min-ON, 4-min-OFF] stimulation protocol. A custom preprocessing script employed the FMRIB Software Library (FSL) for brain extraction, motion correction, slice timing and registration. Independent component analysis was applied with FSL MELODIC for model-free detection of stimulus-driven BOLD response, followed by dual regression to identify significant regions of interest, where the effects of the stimulation parameters were then analyzed. A linear mixed effects analysis (Figure 2), modeled the average response of the last minute of stimulation, as a function of melanin level, wavelength, and frequency.
Results:
The time series plot (Figure 1) shows a visual representation of the data time-series across conditions. It is noted that the intermediate melanin group The time series plot (Figure 1) shows a visual representation of the data time-series across conditions. It is noted that the intermediate melanin group shows a lower peak compared to the darker and lighter melanin groups. Additionally, the response at 10Hz appears consistently higher than at 40Hz, indicating a potential frequency-dependent effect.
The LME results are summarized in Table 1, which summarizes the effects of the stimulation parameters and their interactions on the BOLD response. The model explains 7.32% (R2) of the variance.
The LME results are summarized in Table 1, which summarizes the effects of the stimulation parameters and their interactions on the BOLD response. The model explains 7.32% (R2) of the variance.
Conclusions:
The time series and LME analyses provide detailed insights into the effects of stimulation parameters on the BOLD response, highlighting key differences and providing a detailed understanding of the relationships. Across both analyses it was clear that the 10Hz elicited a larger response than the 40Hz frequency, aligning with existing literature showing the link of 10Hz PBM to enhanced cognitive function and microglial activation [5]. Importantly, darker-skinned participants exhibited significantly lower BOLD responses compared to lighter and intermediate melanin groups; this aligns with our simulation study [4], that showed lighter skin pigmentation led to deeper energy accumulation. This study supports our previous simulation findings and demonstrates differences between pulsation frequencies. It also emphasizes the need to investigate differences in neural rhythms associated with 10Hz and 40Hz PBM.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
MRI
Other - Brain Stimulation; Photobiomodulation; Laser Stimulation
1|2Indicates the priority used for review
·Figure 1
·Figure 2
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
[2] Hamblin, M. "Shining light on the head: Photobiomodulation for brain disorders," BBA Clin. Volume 6. December 2016.
[3] Hamblin MR. Mechanisms and Mitochondrial Redox Signaling in Photobiomodulation. Photochem Photobiol. 2018 Mar;94(2):199-212. doi: 10.1111/php.12864. Epub 2018 Jan 19. PMID: 29164625; PMCID: PMC5844808
[4] Van Lankveld, H. Mai, A. Lim, L. Hosseinkhah, N. Cassano, P. Chen, JJ. Simulation-based dosimetry of transcranial and intranasal photobiomodulation of the human brain: the roles of wavelength, power density and skin colour. bioRxiv 2024.04.05.588330; doi: https://doi.org/10.1101/2024.04.05.588330
[5] Tao L, Liu Q, Zhang F, Fu Y, Zhu X, Weng X, Han H, Huang Y, Suo Y, Chen L, Gao X, Wei X. Microglia modulation with 1070-nm light attenuates Aβ burden and cognitive impairment in Alzheimer's disease mouse model. Light Sci Appl. 2021 Sep 8;10(1):179. doi: 10.1038/s41377-021-00617-3. PMID: 34493703; PMCID: PMC8423759.