Real-time evolution of the fMRI response to transcranial photobiomodulation in the human brain
Poster No:
46
Submission Type:
Abstract Submission
Authors:
Joanna Chen1,2, Hannah Van Lankveld1,2, Xiaole Zhong3,4, Jean Chen3,4,1
Institutions:
1Biomedical Engineering, University of Toronto, Toronto, Ontario, 2Rotman Research Institute, Toronto, Canada, 3Rotman Research Institute, Baycrest, Toronto, Ontario, 4Medical Biophysics, University of Toronto, Toronto, Canada
First Author:
Joanna Chen
Biomedical Engineering, University of Toronto|Rotman Research Institute
Toronto, Ontario|Toronto, Canada
Biomedical Engineering, University of Toronto|Rotman Research Institute
Toronto, Ontario|Toronto, Canada
Co-Author(s):
Hannah Van Lankveld
Biomedical Engineering, University of Toronto|Rotman Research Institute
Toronto, Ontario|Toronto, Canada
Biomedical Engineering, University of Toronto|Rotman Research Institute
Toronto, Ontario|Toronto, Canada
Xiaole Zhong
Rotman Research Institute, Baycrest|Medical Biophysics, University of Toronto
Toronto, Ontario|Toronto, Canada
Rotman Research Institute, Baycrest|Medical Biophysics, University of Toronto
Toronto, Ontario|Toronto, Canada
Jean Chen
Rotman Research Institute, Baycrest|Medical Biophysics, University of Toronto|Biomedical Engineering, University of Toronto
Toronto, Ontario|Toronto, Canada|Toronto, Ontario
Rotman Research Institute, Baycrest|Medical Biophysics, University of Toronto|Biomedical Engineering, University of Toronto
Toronto, Ontario|Toronto, Canada|Toronto, Ontario
Introduction:
Transcranial photobiomodulation (tPBM) uses near-infrared light to stimulate neural tissue [4]. Recent research on tPBM reports significant potential for applications in the treatment of neurological conditions such as neurodegenerative disorders, neurotrauma, and neuropsychiatric conditions, as well as producing improvements in emotional and cognitive function in healthy individuals [1-2,4-8]. However, the exact underlying mechanism of action is still unclear; little is known about the localization of the brain response to the site of stimulation and how it spreads and dissipates depending on dose parameters.
Methods:
14 healthy adults (7M, aged 24.4 ± 3.7 years) were scanned on a Siemens Prisma 3T system while receiving pulsed tPBM via single laser to the right forehead (spot size 1 cm). The tPBM protocol included a 4-minute stimulation period with equivalent pre-stimulus and post-stimulus baseline blocks. The laser was pulsed at two frequencies (10 and 40 Hz), with two wavelengths (808 and 1064 nm) and three optical power densities (100, 150 and 200 mW/cm2). The brain response was measured using a dual-echo pseudo-continuous arterial-spin labelling sequence (TR=4.5s, TE1=9.4ms, TE2=30ms, voxel size=2x2x2 mm). Surround averaging of the TE2 data was used to generate the blood-oxygenation-level-dependent (BOLD) signal.
Brain extraction, motion & distortion correction, slice timing, and bandpass filtering to [0.005, 0.0006] Hz were performed using FSL. Rapid time delay analysis was then applied using Rapidtide (v2.9.6), with the stimulus timing vector as the probe regressor [9,3]. The cross-correlation maps with the probe were thresholded at p < 0.05 at each time delay, and from these, spatial density maps were produced to show how many subjects responded at each given voxel. Based on this, voxels exhibiting > 0.85 density were used to characterize the dose dependence of the spatial fMRI response. Group-averaged time courses were also generated from the voxels demonstrating the maximum cross-correlations (p<0.05).
To quantify the temporal evolution of the fMRI response, significant voxels per time window were counted and then plotted, and voxel-count decline fitted to an exponential function. Along with the peak voxel count (V), the time till peak voxel count (Tp, to represent the response ramp-up) and the exponential time constant (τ, to represent the ramp-down) and submitted to a stepwise linear mixed-effects model (LME), with the predictor variables being wavelength, frequency, and power density.
Brain extraction, motion & distortion correction, slice timing, and bandpass filtering to [0.005, 0.0006] Hz were performed using FSL. Rapid time delay analysis was then applied using Rapidtide (v2.9.6), with the stimulus timing vector as the probe regressor [9,3]. The cross-correlation maps with the probe were thresholded at p < 0.05 at each time delay, and from these, spatial density maps were produced to show how many subjects responded at each given voxel. Based on this, voxels exhibiting > 0.85 density were used to characterize the dose dependence of the spatial fMRI response. Group-averaged time courses were also generated from the voxels demonstrating the maximum cross-correlations (p<0.05).
To quantify the temporal evolution of the fMRI response, significant voxels per time window were counted and then plotted, and voxel-count decline fitted to an exponential function. Along with the peak voxel count (V), the time till peak voxel count (Tp, to represent the response ramp-up) and the exponential time constant (τ, to represent the ramp-down) and submitted to a stepwise linear mixed-effects model (LME), with the predictor variables being wavelength, frequency, and power density.
Results:
Based on the density maps in Fig. 1, the brain response is not confined to the site of stimulation. Instead, it is shown to quickly spread across the anterior brain. Pulsation at 10 Hz produced a larger spatial extent in the BOLD response, with the use of 808 nm at 10 Hz resulting in the largest extent among the four combinations. However, while 1064 nm at 10 Hz produced the lowest peak response, the 1064-nm-40-Hz combination produced the highest % BOLD amplitude. It is also shown that the bulk of the common responding voxels disappear by ~20 s irrespective of parameter setting. The stepwise LME of the parameters of temporal evolution revealed that higher power resulted in slower decline of the response voxel count, and higher frequency resulted in a faster decline (higher τ, p<0.05 and shorter τ, p<0.05, respectively).
Conclusions:
In this work, we illustrate how quickly the tPBM response spreads from the stimulation site and how quickly it subsides post stimulation. We also show that the response is predominantly in the frontal brain regions, in accordance with a forehead stimulation, but that different regions respond at different lags. Furthermore, 808 nm at 10 Hz elicited the strongest and most consistent response across different subjects. This work lays the foundation for a better understanding of the mechanisms of action of tPBM.
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:
ADULTS
Data analysis
FUNCTIONAL MRI
Modeling
MRI
NORMAL HUMAN
Other - transcranial Photobiomodulation (tPBM); brain stimulation; biophotonics
1|2Indicates the priority used for review
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
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