Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation

Abstract

Transcranial Photobiomodulation (tPBM) has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram (EEG) networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm2. In data processing, a novel methodology by combining group singular value decomposition (gSVD) with the exact low-resolution brain electromagnetic tomography (eLORETA) was implemented and performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components (PCs) projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band (delta, theta, alpha, beta and gamma) were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes.

Full text

fnhum-16-853909 May 4, 2022 Time: 18:25 # 1
ORIGINAL RESEARCH
published: 10 May 2022
doi: 10.3389/fnhum.2022.853909
Edited by:
F. Gonzalez-Lima,
University of Texas at Austin,
United States
Reviewed by:
Yingchun Zhang,
University of Houston, United States
Ali Jahan,
Tabriz University of Medical Sciences,
Iran
*Correspondence:
Hanli Liu
hanli@uta.edu
Specialty section:
This article was submitted to
Brain Imaging and Stimulation,
a section of the journal
Frontiers in Human Neuroscience
Received: 13 January 2022
Accepted: 04 April 2022
Published: 10 May 2022
Citation:
Wang X, Wanniarachchi H, Wu A
and Liu H (2022) Combination of
Group Singular Value Decomposition
and eLORETA Identifies Human EEG
Networks and Responses to
Transcranial Photobiomodulation.
Front. Hum. Neurosci. 16:853909.
doi: 10.3389/fnhum.2022.853909
Combination of Group Singular Value
Decomposition and eLORETA
Identifies Human EEG Networks and
Responses to Transcranial
Photobiomodulation
Xinlong Wang, Hashini Wanniarachchi, Anqi Wu and Hanli Liu*
Department of Bioengineering, University of Texas at Arlington, Arlington, TX, United States
Transcranial Photobiomodulation (tPBM) has demonstrated its ability to alter
electrophysiological activity in the human brain. However, it is unclear how tPBM
modulates brain electroencephalogram (EEG) networks and is related to human
cognition. In this study, we recorded 64-channel EEG from 44 healthy humans
before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation
with an irradiance of 257 mW/cm2. In data processing, a novel methodology by
combining group singular value decomposition (gSVD) with the exact low-resolution
brain electromagnetic tomography (eLORETA) was implemented and performed on the
64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11
gSVD-derived principal components (PCs) projected in the 2D sensor and 3D source
domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and
were justified as 11 EEG brain networks. Finally, baseline-normalized power changes
of each EEG brain network in each EEG frequency band (delta, theta, alpha, beta and
gamma) were quantified during the first 4-min, second 4-min, and post tPBM/sham
periods, followed by comparisons of frequency-specific power changes between tPBM
and sham conditions. Our results showed that tPBM-induced increases in alpha
powers occurred at default mode network, executive control network, frontal parietal
network and lateral visual network. Moreover, the ability to decompose EEG signals
into individual, independent brain networks facilitated to better visualize significant
decreases in gamma power by tPBM. Many similarities were found between the cortical
locations of SVD-revealed EEG networks and fMRI-identified resting-state networks.
This consistency may shed light on mechanistic associations between tPBM-modulated
brain networks and improved cognition outcomes.
Keywords: transcranial photobiomodulation, singular value decomposition, eLORETA, default mode network,
executive control network, frontal parietal network
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Wang et al. Brain Networks Modulated by tPBM
INTRODUCTION
Transcranial Photobiomodulation (tPBM) is an emerging optical
neuromodulation method that uses near-infrared (NIR) light
(700–1,070 nm) for non-invasive stimulation of cerebral cellular
functions (Eells et al., 2004;Wong-Riley et al., 2005;Lampl
et al., 2007;Zivin et al., 2009;Barrett and Gonzalez-Lima, 2013).
Several studies have reported that the low-power, high-fluence
NIR light emitted from lasers or light-emitting diodes (LEDs)
can penetrate the extracranial layers of the human head to
reach the cerebral cortex and 3–4 cm within the brain (Jagdeo
et al., 2012;Henderson and Morries, 2015;Tedford et al., 2015),
enabling beneficial neuromodulation to treat a variety of brain
disorders or diseases (Rojas and Gonzalez-Lima, 2011;Hamblin,
2016;Hamblin and Huang, 2019). In particular, two recent
clinical publications have reported significant improvement of
cognitive activity and sustained beneficial effects in patients
with dementia by LED-based tPBM over 4-week (Dougal et al.,
2021) and 8-week (Nizamutdinov et al., 2021) longitudinal multi-
session treatments, respectively. Another clinical study has also
evidenced acute augmentation of human memory in dementia
patients by single-session tPBM (Chan et al., 2021). Furthermore,
single-session delivery of tPBM with 1,064-nm laser on the
forehead of healthy humans has been reported to facilitate
acute human cognition enhancement across different groups
of participants (Barrett and Gonzalez-Lima, 2013;Rojas and
Gonzalez-Lima, 2013;Gonzalez-Lima and Barrett, 2014;Blanco
et al., 2015, 2017;Gonzalez-Lima and Auchter, 2015;Hwang
et al., 2016;Vargas et al., 2017).
The mechanism of action of tPBM is based on the rationale
that NIR light gives rise to photo-oxidation of cytochrome-
c-oxidase (CCO), the terminal enzyme in the mitochondrial
respiratory chain and the main intracellular light-absorbing
enzyme in the near-infrared range (Rojas and Gonzalez-
Lima, 2011;Hamblin, 2016;Hamblin and Huang, 2019). The
oxidized form of CCO (oxi-CCO) plays a key role in the
utilization of neuronal oxygen for energy metabolism (Rojas
and Gonzalez-Lima, 2011). This CCO-driven mechanism of
PBM was first experimentally evidenced by Wang et al. in
the human arm and forehead (Wang et al., 2016, 2017;
Wu et al., 2019), demonstrating that tPBM at 1,064 nm
can non-invasively stimulate mitochondrial metabolism and
hemodynamic oxygenation in tissue vasculature. This set of
experimental observations was supported by reproducible results
(Pruitt et al., 2020) and another independent human study
(Saucedo et al., 2021), all of which provide strong evidence for
the well-accepted, CCO-driven mechanism of action for tPBM.
However, research on the mechanism of tPBM-evoked
electrophysiological effects in the human brain is on its early
stage with only a handful of publications (Berman et al., 2017;
Vargas et al., 2017;Zomorrodi et al., 2019;Ghaderi et al., 2021;
Spera et al., 2021), besides ours (Wang et al., 2017, 2019),
in the last 4–5 years. Most of these studies have reported
alterations of electroencephalography (EEG) powers by tPBM
compared to sham stimulation. In particular, our group recently
observed that tPBM enabled to neuromodulate the eyes-closed,
task-free human brain, causing increases of EEG alpha and
beta powers with significantly distinct topographies compared
to those under sham and thermal stimulations (Wang et al.,
2021). However, these approaches would not permit spatial
identification of brain networks and/or functional connectivity
being activated or photo-modulated by given tPBM. It is of great
importance if specific brain networks and/or cortical activations
can be recognized and localized. As a result, researchers and
clinical scientists would be able to link the tPBM-altered
brain networks closely with improved human cognition or
performance and thus to better understand the underlying
mechanism of electrophysiological effects of tPBM. Accordingly,
our goal of this paper was to develop a novel EEG data processing
methodology that enabled us to achieve human EEG networks
and their responses to tPBM.
To accomplish our goal, we conducted a brief literature
review on methodologies used for quantification of EEG brain
networks at resting state. While many and diverse publications
are found with use of EEG-based brain network analysis, only
a few explored 3-dimensional (3D) volumetric EEG connectivity
based on volume source localization analysis. For example, Aoki
et al. in 2015 utilized exact low resolution brain electromagnetic
tomography (eLORETA) with independent component analysis
(ICA) to resolve EEG resting state functional networks and
the respective activities in five EEG frequency bands (Aoki
et al., 2015). Custo et al. in 2017 applied k-mean clustering
to classify EEG temporal topographies and applied the source
localization algorithm to identify respective cortical sources
(Custo et al., 2017). Snyder et al. more recently reported brain
network functional impairment in stroke patients with respect
to healthy participants using 3D volumetric, orthogonalized EEG
data analysis (Snyder et al., 2021). The reason of orthogonalizing
the EEG time courses was to reduce the effect of volume
conduction on connectivity analyses (Snyder et al., 2021). Along
the same direction or argument, we explored a novel approach
by combining group singular value decomposition (gSVD) and
eLORETA to identify human EEG brain networks and responses
to tPBM. This is because SVD enables us to separate EEG
temporal signal into orthogonal and uncorrelated components,
enhancing the independency of EEG networks (2D topographies)
with mathematical/scientific rigor.
SVD is a common matrix decomposition algorithm and
mainly used to decompose one complex matrix into several
orthogonal matrices or components (Bender et al., 2019). While
SVD is very useful in many areas of science, engineering,
and statistics, it has been extensively applied to solving linear
inverse problems, such as for signal processing or imaging
processing (Chen et al., 2019;Chowdhury and Dutta, 2019;
Ginebreda et al., 2019;Guillemot et al., 2019). Applications of
using SVD to decompose multi-channel EEG signals have been
reported theoretically and experimentally by previous studies.
For example, Shahid et al. applied SVD on EEG signals to identify
epileptic seizures (Shahid et al., 2014); Haddad et al. proved the
feasibility of using SVD to resolve EEG independent networks
(Haddad and Najafizadeh, 2015); Jonmohamadi et al. reported
the fused component using EEG and fMRI data with the help of
SVD (Jonmohamadi et al., 2019). To the best of our knowledge,
no study has examined SVD as an analytical tool to resolve EEG
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Wang et al. Brain Networks Modulated by tPBM
networks and/or quantify tPBM-induced stimulation effects on
the respective networks.
Thus, in this study, we hypothesized that group SVD (gSVD)
in conjunction with eLORETA enabled to identify (a) human
EEG networks on the 2D sensor and 3D source space and (b)
their responses to the 1,064-nm tPBM on the right forehead of
healthy humans. To test/support our hypothesis, we conducted
tPBM and sham experiments concurrently with 64-channel EEG
recordings before, during, and after the tPBM/sham stimulation
on 44 healthy human participants (Wang et al., 2016, 2017).
After implementing the gSVD approach (Harner, 1990;Bai
et al., 2019), we were able to recognize or characterize 11 most-
weighted, two-dimensional (2D) principal components (PCs)
from the gSVD outputs and considered them as dominant
EEG brain networks based on minimal temporal correlations
among any of them. By performing eLORETA (Jatoi et al.,
2014;Imperatori et al., 2015;Ikeda et al., 2019) on these
2D topographies of gSVD-derived brain networks, we further
achieved three-dimensional (3D) cortical source locations for
each network. Furthermore, the tPBM and/or sham induced
power changes on the temporal dynamics of each EEG brain
network were quantified in each EEG frequency band (delta,
theta, alpha, beta, and gamma). By conducting pair-wise non-
parametric statistic permutation comparisons of power changes
at each frequency band between tPBM vs. sham conditions,
we pinpointed several tPBM-modulated EEG brain networks
at alpha and gamma bands, which were consistent with MRI-
based brain networks and also highly associated with human
cognition and behavior.
MATERIALS AND METHODS
Participants
With the same inclusion and exclusion criteria as those in
our previous studies (Wang et al., 2016, 2018, 2021), a total
of 49 healthy human subjects were recruited (19 females of
27.4 ±6.1 years of age; 30 males of 28.7 ±4.7 years of age) from
the local community of the University of Texas at Arlington for
participating this study. The experimental protocol was approved
by the institutional review board (IRB) at the University of Texas
at Arlington and complied with all applicable federal and NIH
guidelines. A written informed consent for the sham-controlled
tPBM experiment was signed by each participant before each
experiment. Four subjects were removed for data analysis because
of being asleep during the experiments. Furthermore, one more
subject was removed during the data analysis phase, because the
power values of 11 SVD components were out of 4 standard
deviations of the group mean. Thus, this study analyzed the EEG
data sets from a total of 44 human subjects.
Instrumentation and Experimental
Protocol
The sham-controlled experimental protocol was reported earlier
(Wang et al., 2021) but briefly shown in Figure 1. Each tPBM or
sham experiment took a total of 13 min, ranging from −2 min to
11 min, with a 2-min baseline, an 8-min tPBM/sham, and a 3-min
FIGURE 1 | Experimental protocol with a 2-min baseline, 8-min tPBM/sham,
and a 3-min recovery period. The solid red and black-dashed open circles
mark the spatial sites of tPBM and sham delivery, respectively, on the
subject’s forehead. All participants and the experimental operator were
required to wear a pair of googles for eye protection.
recovery period. During the entire experiment, each participant
was required to close his or her eyes but stayed awake. The 2-
min baseline recording was designed as a reference for EEG signal
normalization to minimize the biological variation of EEG power
among individuals. tPBM or sham was delivered on the right
forehead above the eyebrow and below the hairline. 64-channel
EEG recording was concurrently taken throughout the entire
experiment for both sham and tPBM sessions. The sequences
of active and sham experiments were randomly assigned; the
time interval between the two experiments was at least 3 days to
minimize post-stimulation residuals/effects. The participants had
no information about sham or true tPBM stimulations until they
completed both experiments.
To conduct tPBM, we employed the same type of 1,064-nm
laser (Model CG-5000, Cell Gen Therapeutics LLC, Dallas, TX,
United States) as that reported in previous studies (Wang et al.,
2016, 2019, 2021;Wu et al., 2019). The illumination area of
laser for tPBM was 13.6 cm2, with a laser power of 3.5 W and
a laser aperture diameter of ∼4.16 cm. Thus, the active optical
energy (or dose) and energy density (or fluence) delivered to the
human forehead by tPBM were 3.5 W ×480 s = 1,680 J and
1,680 J/(13.6 cm2) = 123.5 J/cm2, respectively. The active power
density (irradiance) was to be 3.5 W/(13.6 cm2) = 257.4 mW/cm2.
On the other hands, the power used for sham was set to be
0.1 W. Furthermore, during the actual sham experiment, the
laser aperture was further covered by a black cap to avoid any
leaky tPBM light delivered to each subject’s head. Therefore, the
true light delivery in the sham experiment was 0 mW/cm2. The
correct power densities for both experiments were confirmed
by a sensitive optical power meter (Model 843-R, Newport
Corporation, 8 East Forge Parkway, Franklin, MA 02038,
United States). Since the laser beam was well collimated, there
was negligible difference between the illumination power at the
laser aperture vs. that on the human forehead. The consistency of
power density 0 vs. 2 cm away from the laser aperture was also
confirmed by the power meter (Wang et al., 2021).
A 64-electrode 10-10 EEG system (Biosemi Inc., Barcelona,
Spain) was employed to non-invasively record EEG readings for
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the whole duration of each experiment. Two separate electrodes,
Common Mode Sense (CMS) and Driven Right Leg (DRL), were
used as the “ground” of the recording system. The sampling
rate of EEG recording was 512 Hz for 22 subjects and 256 Hz
for the other 22 subjects, respectively, because of some research
topics beyond the scope of this paper. All the EEG data collected
with the 512-Hz sampling rate were down-sampled to the 256-
Hz rate during data pre-processing by EEGLAB (Delorme and
Makeig, 2004;Asadi et al., 2020). The highest EEG frequency to
be analyzed in this study was 70 Hz, about 1/4 of the 256-Hz
sampling frequency. Based on Nyquist’s theorem, our down-
sampling process would not alter the measures of frequency
powers (Cohen, 2014, 2021); more justification was given in
(Wang et al., 2021).
Data Analysis
Figures 2A,B display a flowchart and a corresponding
diagram, respectively, to describe the seven steps of data
processing, including (1) preprocessing for EEG noise removal,
(2) concatenation of all subjects’ clean EEG time series
(including EEG signal before, during and after tPBM/sham),
(3) performance of gSVD, (4) selection of 11 most-weighted
principal components (PCs) from the gSVD analysis, (5)
cortical source localization of each gSVD component, and
(6)–(7) computation of baseline-normalized, sham-subtracted
powers for all the gSVD components and at five EEG oscillation
frequency bands. All the signal processing operations were
carried out using two free-access software packages, EEGLAB
(on the platform of MATLAB) and eLORETA.1Specific analysis
procedures are described in detail in the following sub-sections.
Preprocessing (Step 1)
Data preprocessing was performed on the raw, 64-electrode EEG
data using EEGLAB. Because of two sampling rates (512 and
256 Hz) used in the experiments, we first down-sampled the
EEG time series with the 512-Hz sampling rate to the 256-
Hz time series by a native EEGLAB function “downsample”
(Delorme and Makeig, 2004). EEG signal of each electrode was
re-referenced to the average of all 64 electrodes. Then, all sets
of EEG time series were bandpass filtered between 1 and 70 Hz,
followed by a notch filter to remove 60-Hz power line noise. Next,
robust principal component analysis (rPCA) (Candès et al., 2011)
was applied for effective removal of common artefacts, such as
head motions, saccades, and jaw clenches, from our EEG data
(Turnip, 2014). Finally, independent component analysis (ICA)
(Iriarte et al., 2003;Delorme and Makeig, 2004;Ramkumar et al.,
2012) was performed to confirm artifact-free (both temporal
and spatial) component patterns/features. Due to the eyes-closed
recording condition and the robust performance of rPCA, we
did not observe any artifact-caused patterns after ICA for all the
participants, and thus no component was further removed before
performing further analysis for each participant.
Group Singular Value Decomposition (Steps 2–3)
The concept of group SVD in this study can be viewed in analogy
to the spatial group ICA that has been widely applied in the
1http://www.uzh.ch/keyinst/loreta.htm
field of fMRI (Calhoun et al., 2009;Li et al., 2012). As shown
in Figure 2B, after preprocessing, the artifact-free EEG time
series matrixes (1–70 Hz bandpass filtered) from all 44 subjects
were concatenated into one single matrix, including the 64-
channel EEG data from both tPBM and sham sessions of all
44 subjects. Previous studies suggested that there are intrinsic
cooperation or interconnection between slow and fast EEG
rhythms in mediating brain networks, so the whole frequency
band of EEG should be considered together rather than separate
it into individual EEG bands (i.e., delta, theta, alpha, beta, and
gamma) when doing mathematical operations (Mantini et al.,
2007). Next, gSVD algorithm was conducted on the concatenated
matrices to resolve common PCs across all the tPBM and
sham data sets from all the subjects using the native MATLAB
function “svd” with the “economy-size decomposition” setting,
which removes extra rows or columns of zeros in S and U,
where S and U are explained in Eq. (1). To minimize subjects’
biological variation, individual standardization was performed
for each subject’s data before being grouped for concatenation.
Specifically, each subject’s EEG time series of each channel was
first subtracted by its respective temporal mean, followed by
further being divided by its temporal standard deviation. This
operation is equivalent to a statistical Z standardization that
could avoid non-uniform weight or bias in the SVD calculation
from individuals who might have a larger oscillation power,
which could bias the common PC computation.
The mathematical expression of “svd” function is shown in
Eq. 1:
B=U×S×VT(1)
where B is the transposed matrix of A (i.e., B=AT), and A
is the concatenated EEG matrix, U denotes the corresponding
vectors of time dynamics for all the 64 vector components; V
(64 ×64) denotes the 64 vector components of 2D relative
electrical potential (rEP) topographies without a unit (because
of the Z transformation on every single subject’s EEG data
before gSVD); the diagonal elements of the square S matrix
contains 64 singular values of B, indicating the weight of each
component decomposed from B. Therefore, as the output of
gSVD, we obtained 64 gSVD components as the major PCs, their
corresponding 1D time series, and their respective weights over
the original EEG signal. This process is graphically shown as steps
2 and 3 in Figure 2B.
Extraction of 11 Group Singular Value Decomposition
Components (Step 4)
Based on the results obtained in Step 3, we extracted 11
most-weighted gSVD components, presented them in 2D
topography in the sensor space, and projected them in 3D
cortical source space using eLORETA (details to be given
in Step 5). The corresponding 1D time series for each
component was segmented into 2-min baseline, 0–4 min
tPBM/sham stimulation, 4–8 min tPBM/sham stimulation, and
recovery for each participant and for each stimulation type,
i.e., tPBM and sham. The reason for making two of 4-
min time segments was based on our previous observations
that tPBM induced gradual and significant effects only a
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FIGURE 2 | (A) A flow chart of seven steps for EEG data processing; (B) a corresponding diagram to graphically explain each data processing step in detail. Note
that in step 4 of (B), the time course shows only 2-s data as a demo of time series in U matrix for one SVD component. The number in yellow circles matches the
steps given in the flowchart (A).
few minutes after the stimulation onset, and that 4-min
temporal segments proved to exhibit meaningful effects of
tPBM (Urquhart et al., 2020;Wang et al., 2021) (details to be
given in Step 6–7).
Source Localization by eLORETA (Step 5)
eLORETA is a free academic software package (see text
footnote 1), which converts the 2D scalp distribution of
electric potential into cortical 3D distribution of current
density using a total of 6,239 voxels at 5-mm spatial
resolution to localize electric activity in the human cortex.
It offers a weighted least-square based inverse solution with
zero localization error under ideal conditions (Pascual-
Marqui et al., 2011). In this study, eLORETA was applied
on the 2D electric potential distribution (sensor space) of
SVD components to localize respective cortical sources
(source space). In the operation of eLORETA, the Montreal
Neurological Institute (MNI) coordinates of the 64-
channel international 10-10 system were employed, and the
regularization parameter for generation of the transformation
matrix was set to be 1 by default. The above procedures
produced 2D (sagittal, coronal, and axial) views as well
as 3D cortical maps or representations for each SVD
component.
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FIGURE 3 | One example of the group-averaged PSD (in log scale for both xand yaxis) for SVD component one, SVD #1, during 4–8 min tPBM (red) and sham
(black), respectively. Blue vertical dashed lines separate the five EEG frequency bands, namely, delta (δ: 1–4 Hz), theta (θ: 4–8 Hz), alpha (α: 8–13 Hz), beta
(β:13–30 Hz), and gamma (γ: 30–70 Hz) bands. The dashed black box provides a zoom-in view of the PSD in alpha band (in linear scale for both xand yaxis).
Power Spectral Density of Time Course of Each
Group Singular Value Decomposition Component
(Step 6)
For each subject, power spectral analysis was conducted on
the time course for each SVD component, respectively. This
process resulted in one power spectral density (PSD) curve
per component per subject during each temporal segment
that we selected (e.g., 2-min baseline, 0–4 min tPBM/sham,
4–8 min tPBM/sham, and 3-min recovery). As an example,
Figure 3 demonstrates group-level PSD curves of SVD #1 during
the 4–8 min of tPBM (Red) and sham (black). Blue vertical dashed
lines separate the five EEG frequency bands into spectral ranges,
in which each subject’s spectral power was calculated from its
own PSD curve. Note that the zoomed view on the alpha peak
of PSD graphically illustrates a potential increase in alpha power
compared to that under sham. To quantify power changes in
all SVD components statistically, the following processes in Step
7 were conducted.
Computation of Baseline-Normalized,
Sham-Subtracted Power Changes Induced by
Transcranial Photobiomodulation (Step 7)
To compute the absolute tPBM-induced, frequency-specific
EEG power changes of each SVD component, we obtained a
spectral power by multiplying the averaged PSD value over
the chosen spectral band with the corresponding bandwidth
for each subject. This operation was repeated for all four time
periods (i.e., baseline, 0–4 min tPBM/sham, 4–8 min tPBM/sham,
and recovery). Next, baseline normalization was performed
by dividing the frequency-specific power during and after
tPBM/sham period by its own baseline power. These processes
were repeated for all 11 SVD components, for all the subjects, in
all five frequency bands, and for all the three periods (0–4 min,
4–8 min, and recovery) for both tPBM and sham conditions, as
expressed mathematically in Eqs. (2) and (3), respectively. In this
way, a baseline-normalized and sham-subtraction index, 1nP,
was produced per frequency band (i) per gSVD component (j) per
subject (m) per time segment (t) by subtracting nPj
im,t,sham
from nPj
i(m,t,tPBM), as shown in eq. 4. Indeed, baseline-
normalized and sham-subtracted powers reflect the absolute
percent changes in power induced by tPBM. As a result, we
observed high consistency of PSD curves between the two
baselines of tPBM and sham treatment conditions retained in
all 11 SVD components, as shown in Supplementary Figure 1.
This set of results demonstrated minimal difference in PSD
baselines and justified for baseline-normalized comparisons of
power changes between tPBM and sham.
nPj
i(m,t,tPBM)=Pj
i(m,t,tPBM)
Pj
i(m,baseline,tPBM)
,(2)
nPj
im,t,sham=Pj
i(m,t,sham)
Pj
i(m,baseline,sham)
,(3)
1nPj
i(m,t)=nPj
i(m,t,tPBM)−nPj
im,t,sham,(4)
where nP: baseline-normalized power;
1nP: baseline-normalized and sham-subtracted power;
i: delta, theta, alpha, beta, and gamma bands;
j: 1, 2, 3, . . . 11 for SVD components;
m:1,2,3.. . . 44 for participants;
t: three time periods, 0–4 min, 4–8 min, and 3-min recovery.
Finally, one-sample, non-parametric permutation tests
(Manly, 2007;Clements et al., 2021;Krol, 2021) were conducted
between the 1nP vs. zero at the significance level of p<0.05
and p<0.01 for each SVD component, j, at frequency band, i,
and during each of three time periods, t, over total participants,
m= 44. This is essentially equivalent to two-sample, pair-wise,
non-parametric permutation comparisons between tPBM and
sham, giving rise to bar plots (as shown in Supplementary
Figure 3 as an example).
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RESULTS
Extraction of 11 Most-Weighted
Components From Group Singular Value
Decomposition Algorithm (Results of
Step 3)
Results in this section were obtained from Step 3 in data
processing. Figure 4 plots the diagonal values of Sin Eq. 1,
demonstrating the ranking of all the 64 PCs/gSVD components
based on their weights in EEG signal after gSVD. An exponential
decay pattern of weights is shown across the components. In
particular, we selected all the components that had less than 90%
decay of the first/most-weighted component (=21,500). Thus, all
the components with weight factors smaller or lower than 2,150
were excluded, giving rise to 11 dominant components (marked
as red color in Figure 4) for further data analyses. Each of these
gSVD components can be represented by a 2D topography. The
total EEG signals represented by these 11 components accounted
for 70% of the entire measured EEG signals (i.e., the area under
the curve of all the red regions/dots over that of all the 64 dots in
Figure 4).
Justification of Group Singular Value
Decomposition-Derived Independent
Brain Networks (Results of Step 4)
Results in this section were achieved from Step 4 in data
processing. Figure 5A shows 2D topographies of relative
electrical potential (rEP) at sensor space for the 11 extracted
gSVD components. Clear spatial distinction of rEP was observed
across the 11 topographies, indicating the uniqueness and
independency of neural activities associated with each SVD
component. Moreover, these 2D spatial patterns are highly
reproducible regardless of “number of subjects,” “perturbation
FIGURE 4 | Ranks and weights of all the 64 gSVD components after gSVD
process. The x-axis indicates the rank number of the 64 components, while
the y-axis denotes the weight of each component. An exponential decay
pattern of weight appears across the 64 components. The 11 most-weighted
components, each of which has more than 90% weight of the most weighted
component, are marked by red dots and selected for further data processing.
The total weight of the 11 selected components (red) takes 70% of the total
weight (all the dots).
methods,” and “mental state condition.” Supplementary Figure 2
exhibits the highly repeatable topographical patterns derived by
gSVD using “88 data sets of 2-min baseline data,” “44 data
sets from tPBM-active measurements through 11-min recordings
(i.e., baseline and tPBM and recovery),” “44 data sets from
sham measurements through 11-min recordings,” and “random
selection of 22 participants” data.
On the other hand, to quantify temporal correlations among
all 11 gSVD components, Pearson Correlation Coefficients (PCC)
was performed between each pair of the 13-min temporal
dynamics of SVD networks for each subject. The group-averaged
PCC values between every pair of the 11 gSVD components are
shown in Figure 5B, illustrating negligible PCC values (<0.07)
for all pairs of components. It provided strong evidence that
the 11 gSVD-derived components were temporally independent.
Thus, we considered and claimed these 11 PCs as the “EEG
brain networks” under the eyes-closed, task-free condition based
on their complete independency in both spatial and temporal
features among all 11 networks following the widely recognized
definitions of large-scale brain networks (Mantini et al., 2007;
Raichle and Snyder, 2007;Tagliazucchi et al., 2012).
Mapping Neural Activities of EEG
Networks in Source Spaces
(Results of Step 5)
Results in this section were obtained from Step 5 in data
processing with the help of eLORETA to compute 3D maps of the
cortical/subcortical current density for each EEG brain network,
FIGURE 5 | (A) 2D topographies of relative electrical potential (rEP) at sensor
space for the 11 extracted gSVD components. (B) PCC values of temporal
dynamics between each pair of gSVD-derived component across 11-min
measurement time. It shows that the 11 components have minimally
time-correlated values (PCC <0.07). Note that self-correlation for each
component is meaningless and thus marked with N/A (i.e., not applicable).
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as shown in Figure 6. The leftmost column of Figure 6 shows
the 2D topographies of the 11 EEG brain networks. The color
bar indicates the relative electrical potential, rEP, of the “dipoles”
across the whole scalp without a unit. The middle three columns
of Figure 6 display the axial, sagittal, and coronal views of the
current density of neural activity. Each panel in the rightmost
column exhibits a 3D rendered brain model in top and side
views of the left and right hemispheres of each respective EEG
brain network. Note that for networks 1, 2, 3, 4, 5, 6, and 9,
medial sagittal views of the brain are given to explicitly expose
anatomical locations of subcortical sources. On the other hand,
for networks 7, 8, 10, and 11, lateral views of the brain are
presented to clearly show locations of cortical sources. Yellow
color on the rendered brain models indicates the binarized,
associated cortical locations under a threshold of >75% of the
maximum neural activity (i.e., cortical current density) in the
network or 3D brain model. In other words, each voxel in one
network was rendered with yellow color if its neural activity
was within the top 25 percentile across the 6,239 voxels in the
brain model. Note that eLORETA facilitated anatomical and/or
structural locations of associated cortical lobes and regions for the
11 EEG networks we identified through gSVD, as listed in Table 1.
Baseline-Normalized, Sham-Subtracted
Network Power Changes (Results of
Steps 6–7)
For both tPBM and sham conditions, Eqs. (2) and (3)
were utilized to quantify frequency-specific, baseline-normalized
network powers during three time periods (0–4 min, 4–8 min,
and 3-min recovery) for each of the 11 brain networks. Next,
following Eq. (4), the baseline-normalized, sham-subtracted
index, 1nP, was calculated in each frequency band and for each
brain network. Figure 7 shows the group-level (n= 44) 1nP
in alpha and gamma bands. Specifically, Figure 7A shows that
in most of SVD networks across all three time periods, tPBM
enhanced alpha powers in networks 1, 4, and 8 significantly across
all three time periods, namely, through entire 8-min tPBM and 3-
min recovery after removal of sham effect. Moreover, network 6
became significantly augmented during 4–8 min and post tPBM,
while alpha powers of networks 2, 5, 9 were significantly boosted
only within the 4–8 min stimulation phase. After checking the
corresponding cortical locations listed in Table 1, results in
Figure 6A revealed that tPBM enabled to neuromodulate EEG
alpha powers with a sustained period in numerous cortical and
subcortical locations, such as the cingulate gyrus, precuneus, left
inferior frontal and parietal lobules, medial frontal gyrus, anterior
cingulate, middle and inferior occipital gyrus, and the superior
parietal lobule. On the other hand, tPBM did not generate any
significant modulation on EEG delta, theta, and beta powers with
comparative plots shown in Supplementary Figure 3.
In contrast, as shown in Figure 7B, tPBM significantly
decreased EEG gamma powers of networks 4 and 9 throughout
all three time periods. Also, network 5 power became significantly
reduced during the onset of tPBM and recovered after the stop
of tPBM. After close inspection on the brain regions in each SVD
network given in Table 1, we suggested that tPBM tended to lower
gamma oscillations at the bilateral inferior frontal and parietal
lobule, medial frontal gyrus, and anterior cingulate.
DISCUSSION
Given the previous findings that right-prefrontal tPBM could
enhance cognitive functions (Barrett and Gonzalez-Lima, 2013;
Rojas and Gonzalez-Lima, 2013;Gonzalez-Lima and Barrett,
2014;Blanco et al., 2015, 2017;Gonzalez-Lima and Auchter,
2015;Hwang et al., 2016;Vargas et al., 2017) and modulate EEG
power globally in the human brains (Wang et al., 2017, 2019), we
hypothesized that the locally delivered tPBM can neuromodulate
the power of frequency-specific EEG brain networks in the
task-free human brain, which may result in significant effects
on human cognition beneficially. To support or prove our
hypothesis, we developed a novel methodology by combining
the gSVD algorithm with eLORETA to identify and localize 11
dominant EEG networks presented in both 2D scalp and cortical
source space. Several key and/or novel findings are reported in
the following sub-sections.
Large-Scale Neural Activities Presented
by Group Singular Value
Decomposition-Derived EEG Brain
Networks
Similarity of Group Singular Value
Decomposition-Derived EEG Brain Networks to
fMRI-Defined Networks
The application of gSVD in this study enabled the isolation and
identification of 11 intrinsic EEG brain networks. In Figure 4,
these 11 networks take 70% of the total contribution in the
recorded EEG signal. This is in great consistency with respect to
the well-known neural physiology that the human brain takes 60–
80% of energy in supporting the communication among neurons
and functional activity (Raichle and Snyder, 2007). Furthermore,
by careful inspection on the active cortical localizations of each
EEG network shown in Figure 6 and Table 1, we recognized
spatial or cortical co-localizations between the SVD-identified
EEG networks and fMRI-recognized networks (Jann et al., 2010;
Veer et al., 2010;Aoki et al., 2015;Shen, 2015;Piano et al.,
2017;Jonmohamadi et al., 2019). This indicates that, although
EEG recorded cerebral electrophysiological oscillations at much
higher frequencies with lower spatial resolution than fMRI, SVD-
derived EEG brain networks may potentially reflect or share
the contribution from the same neural activity with fMRI-
defined brain networks. Supplementary Table 1 shows the most
associated Brodmann Areas (BAs) of each EEG brain network
found in this study and the corresponding fMRI-identified
networks covering similar active BAs. For example, gSVD-
derived EEG networks # 1–3 are co-located anatomically with the
posterior default mode network (pDMN) of fMRI. gSVD-derived
EEG networks #4 and #5 share the anatomical sites with the left
and right fontal-parietal networks (L- or R-FPN). EEG networks
#6 involves the anatomical sites with the executive control
networks (ECN). Note that, for some of the fMRI networks,
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FIGURE 6 | (Continued)
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FIGURE 6 | The leftmost column excluding the number column shows 2D rEP maps for 11 EEG brain networks corresponding to 11 SVD components. Accordingly,
the middle three columns display axial, sagittal, and coronal views of the current density of neural activity for each EEG network, while the right most column
illustrates respective 3D source localizations of cortical current density. Yellow color on the brain models indicates the binarized cortical current density at the
associated cortical locations under a threshold of >75% of the maximum neural activity in the network/brain model.
FIGURE 7 | Group-level (n= 44) 1nP (i.e., baseline-normalized, sham-subtracted EEG powers) for each brain network in (A) alpha and (B) gamma bands, during
0–4 min (green), 4–8 min (blue) tPBM/sham, and 3-min recovery (purple) periods. Error bars represent the standard error of the mean. Significant differences of 1nP
between tPBM vs. sham when pair-wise, two-sample, non-parametric tests between nP values for tPBM and sham (equivalent to one-sample non-parametric test
between 1nP vs. zero) were performed at the significance level of p<0.05 (marked by “∗”) and p<0.01 (marked by “&”).
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TABLE 1 | Main associated cortical lobes and regions for the 11
networks from eLORETA.
SVD Associated cerebral lobes Associated brain regions
# 1 Limbic lobe, parietal lobe Cingulate gyrus, precuneus
# 2 Limbic lobe, parietal lobe Cingulate gyrus
# 3 Limbic lobe, parietal lobe Cingulate gyrus, precuneus
# 4 Left: frontal, parietal lobe Left: inferior frontal gyrus, inferior
parietal lobule
# 5 Right: frontal, parietal, occipital
lobe
Right: inferior frontal gyrus, inferior
parietal lobule, precuneus
# 6 Medical frontal lobe, limbic lobe Medial frontal gyrus, anterior cingulate,
cingulate gyrus
# 7 Frontal lobe, parietal lobe Precentral gyrus, postcentral gyrus,
inferior parietal lobule
# 8 Occipital lobe, parietal lobe Middle occipital gyrus, inferior occipital
gyrus, superior parietal lobule
# 9 Frontal lobe Medial frontal gyrus, anterior cingulate
# 10 Right: occipital lobe Right: middle occipital gyrus, cuneus
# 11 Left: occipital lobe Left: middle occipital gyrus, inferior
occipital gyrus
inconsistent names have been used in the literature. Thus, the
right-most column of Supplementary Table 1 lists all the names
of brain networks used by different publications.
Three EEG Networks Sharing Posterior Cortical
Sources of Neural Activity
Notably, it is difficult to physiologically understand why the first
three EEG brain networks, # 1–3, seemed to be linked to the same
single fMRI-identified brain network, namely, parietal DMN. The
reason that they were sorted as three individual/separate EEG
brain networks is because of the independent features in the
spatial distribution of rEP (i.e., having distinct 2D topographies)
and in the temporal dynamics (i.e., small PCC values <0.07),
as demonstrated in Figure 5 and Supplementary Figure 4. The
reason why they were not resolved in the cortical source domain
might be because of the limited spatial resolution that eLORETA
is able to achieve for high-spatial source localization.
Compared to fMRI, EEG signals can achieve a much
higher temporal resolution with a much higher frequency
range of interest. In this study, the frequencies of interest
ranged between 1 and 70 Hz, while the frequency span of
interest in fMRI is ∼0.1 Hz (Huotari et al., 2019;Yuen
et al., 2019). The temporal and spectral differences between
the two measurement/image methods may result in different
and/or complementary characteristics discovered from the same
network. Accordingly, EEG networks #1–3 could be attributed
to three independent sources of EEG neural activities/networks
that have distinct temporal features but are located around the
posterior cingulate gyrus and/or the precuneus regions. Because
of much lower sampling frequency of fMRI than EEG, the three
spatially adjacent but temporally independent sources may not
be temporally resolved by fMRI. In the meantime, due to the
lower spatial resolution of EEG, the spatial distinction among the
three independent sources could not be visualized/identified as
the results of eLORETA analysis.
Advantage of Group Singular Value Decomposition to
Monitor Neural Activities in Cognition-Sensitive
Networks
As can be observed in Figures 4,6, and Supplementary Table 1,
the first 6 most weighted gSVD-derived EEG networks took more
than 60% (area under the curve of the first 6 points in Figure 4)
of the total EEG signal contributions and co-located with the
fMRI-recognized, cognition-sensitive networks, such as DMN,
FPN and ECN. This set of observations revealed a potential
advantage of the gSVD +eLORETA algorithm that facilitates
EEG network-based monitoring of fast neural activities in these
cognition-related networks.
The DMN is one of the most dominant and important
networks in the human brains (Mohan et al., 2016;Murphy
et al., 2018;Sormaz et al., 2018). Our gSVD-derived EEG brain
networks found are consistent with this statement. Specifically,
four out of the 11 most-weighted EEG networks (i.e., SVD # 1,
2, 3 and 9) were found being associated with the DMN. The
weights of these four components (shown in Figure 4) took
66.4 and 47.8% of the top 11 networks and the whole EEG
signal (64 components). Thus, due to its dominancy in EEG
signal, the gSVD-detected DMN activity are expected to have
a high signal-to-noise ratio, offering a potentially rapid and
feasible means to extract DMN fluctuations of the human brain
from EEG recordings.
Although the DMN is believed active during mind wondering
and wakeful rest, recent studies have reported the important role
DMN plays during many cognitive activities, such as attention
(Dastjerdi et al., 2011), memory encoding (Sormaz et al., 2018),
and memory consolidation (Lefebvre and D’Angiulli, 2019).
Moreover, DMN is closely related to psychological conditions
and mental health of the human brain (Buckner et al., 2008;
Akiki et al., 2018). Therefore, the ability to sensitively detect
DMN activity by EEG will advance/broaden EEG’s applications
as a new neural monitoring tool to complement fMRI with high
temporal resolution.
Apart from the DMN, FPN (left and right) and ECN
are the next three predominant EEG networks identified by
the gSVD algorithm, ranked as # 4–6 and preceded only by
the DMN. They took a total of 20.4% weights among the
11 dominant networks. It is known that the FPN plays a
key role in human cognition (Jann et al., 2010;Marek and
Dosenbach, 2018), including attention and memory during
tasks, such as memory encoding and cognitive flexibility (Chan
et al., 2008;Kawasaki et al., 2010;Collins and Koechlin, 2012;
Diamond, 2013;Nielsen et al., 2017). Likewise, the ECN is
vital to human executive control functions during attention and
other tasks, such as attentional control, cognitive inhibition,
inhibitory control, working memory, planning, reasoning, and
problem solving (Janicak, 2002;Chan et al., 2008;Diamond,
2013). Being able to detect neural activities in FPN and ECN
would make EEG more practically and broadly useful for rapid,
high-temporal-resolution monitoring of cognitive functions in
the human brain.
In addition, The significantly modulated alpha and gamma
power on DMN, FPN and ECN observed in this study provided
a potential mechanistic link or association between tPBM and its
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impact on improvement of behavioral performances in human
memory, attention performance (Jahan et al., 2019), top-down
mental processes (Osipova et al., 2008;Voytek et al., 2010) and
executive performance (Barrett and Gonzalez-Lima, 2013;Rojas
and Gonzalez-Lima, 2013;Gonzalez-Lima and Barrett, 2014;
Blanco et al., 2015;Gonzalez-Lima and Auchter, 2015).
Enhancement of Alpha Power by
Transcranial Photobiomodulation in
Selected Group Singular Value
Decomposition-Derived EEG Brain
Networks
Our results in Figure 7A demonstrated that after the self-
baseline normalization to reduce biological variations of the
brain state, tPBM intervention tended to increase EEG alpha
powers in several EEG brain networks, as compared to those
under sham condition. Significant modulation effects displayed
a network-dependent manner with significantly enhanced alpha
power at SVD networks 1 (pDMN), 4 (left-FPN), 6 (ECN),
and 8 (lateral visual network; LVN) throughout the during
and post-tPBM period while SVD networks 2, 5, 9 were
boosted only in the last 4 min of tPBM. According to previous
reports by other research groups and ours, alpha power
enhancement has been consistently observed as a main effect
of tPBM (Wang et al., 2019, 2021;Zomorrodi et al., 2019;
Yao et al., 2020). This consistent observation demonstrates the
reproducibility of the tPBM effects in the human brains. Also,
since alpha wave is one of the essential brain oscillations for
cognitive functions (Clements et al., 2021) and neurofeedback
(Angelakis et al., 2007), enhanced alpha power may indicate
potential benefits to the human cognition. However, in previous
studies, the observed enhancements in alpha power were
usually reviewed or presented with a large cortical area of
the human brain in the EEG topographical format. There
was a lack of association between the modulation effects
and brain networks to localize specific cortical regions being
neuromodulated by tPBM. Thus, it was difficult to explain
the previously observed cognition enhancement with direct
neuromodulation effects.
The implementation of gSVD enabled the time-spatial
decomposition of the 64-electrode EEG signal (Bender et al.,
2019), following a similar concept or analogy to the process
of principal component analysis (Lagerlund et al., 1997;Wall
et al., 2003). In other words, the gSVD-derived components
from EEG can be regarded as principal components or dominant
networks of the EEG signal. This operation allowed us to visualize
and analyze the EEG activities with 64 orthogonal dimensions,
followed by dimension reduction using the scale of the singular
values (the diagonal values of Sin Eq. 1, which equals to the root-
mean-square amplitude of each spatiotemporal feature (Harner,
1990)) to select the most weighted effects by tPBM.
Furthermore, using eLORETA, the 64-channel electric
potential dipoles could be further reconstructed to localize
their cortical/sub-cortical sources (Pascual-Marqui et al., 2011).
Table 1 revealed that the previously observed global alpha power
enhancement originated from cortical neuromodulation effects
of the cingulate gyrus, precuneus, bilateral inferior frontal and
parietal lobules, medial frontal gyrus, anterior cingulate, middle
and inferior occipital gyrus, and the superior parietal lobule.
All these cortical regions are responsible for human cognitive
functions. For example, the cingulate gyrus is essential for
human memory process (Kozlovskiy et al., 2012;Leech and
Sharp, 2014); the precuneus is active in memory tasks (Wallentin
et al., 2006) and episode memories (Lundstrom et al., 2003);
the inferior frontal and parietal lobules are responsible for
social cognition, decision making, and attentional performances
(Binder and Desai, 2011;Tops and Boksem, 2011;Numssen et al.,
2021); the anterior cingulate and the medial frontal gyrus are
majorly involved in emotion (Stevens et al., 2011) and decision
making (Euston et al., 2012); the occipital gyrus is in charge of
perception and object recognition (Grill-Spector et al., 2001);
and the superior parietal lobule is essential for the information
manipulation during working memory (Koenigs et al., 2009).
Therefore, we speculated that the tPBM-induced augments of
human cognitive functions (Barrett and Gonzalez-Lima, 2013;
Rojas and Gonzalez-Lima, 2013;Gonzalez-Lima and Barrett,
2014;Blanco et al., 2015;Gonzalez-Lima and Auchter, 2015) may
result, at least partially, from the enhancement of alpha powers
in those cognition-active EEG cortical networks.
Reduction of Gamma Power by
Transcranial Photobiomodulation in
Selected Group Singular Value
Decomposition-Derived EEG Brain
Networks
In addition to the effect on alpha power of EEG networks,
significant reduction of gamma power was also observed in
this study after exclusion of sham effects. Figure 7B displayed
decreases of gamma power in networks 4 (left-FPN) and 9
(DMN) throughout all three time periods. Network 5 (right-
FPN) became significantly weaker during the onset of tPBM
and recovered after the stop of tPBM compared to the sham
stimulation condition.
In our previous study, using the same datasets with a non-
decomposition data processing method, the significant decrease
of gamma power was not revealed (Wang et al., 2021). This is
expected because, in our previous study, the gamma oscillation
in EEG signal was grossly being processed with a mixture of all
the networks; in this study, the novel application of gSVD was
able to decompose 11 principal components or networks from
the EEG signal, including respective responses to tPBM. As seen
in Figure 7B, among the 11 EEG networks, only five of them
showed significant responses. It is expected that a mixture of EEG
networks, more than 50% of which had non-significant responses
to tPBM, would have much less sensitivity to respond to tPBM.
This was why previous analysis did not observe any significant
change in gamma power. In turn, the capability of detecting
gamma power reduction emphasized another advantage of the
gSVD analysis: being able to sense changes in distributed EEG
networks induced by different conditions, such as tPBM.
According to Table 1 and Figure 7B, the major cortical regions
with modulated gamma oscillation included the bilateral inferior
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Wang et al. Brain Networks Modulated by tPBM
frontal and parietal lobule, the right precuneus and cuneus, the
medial frontal gyrus, anterior cingulate, left inferior occipital
gyrus, and the bilateral middle occipital gyrus. Additionally,
tPBM also modulates the gamma power at the bilateral
middle occipital gyrus, which is essential for the mediation of
auditory and tactile information (Renier et al., 2010) as well
as for the processing of cognitive biases for depression (Teng
et al., 2018). Thus, the tPBM-modulated gamma power at the
above cortical locations can potentially be used to explain the
improved cognitive functions (Barrett and Gonzalez-Lima, 2013;
Chaudhari et al., 2021).
To the best of our knowledge, the decrease of gamma
power has essential neurophysiological significances. Zhou
et al. reported that suppressed alpha power and increased
gamma power are related to neural pain (or neuralgia)
(Zhou et al., 2018). Tanaka-Koshiyama found that elevated
gamma power is associated with learning and memory
dysfunction and Schizophrenia (Tanaka-Koshiyama et al.,
2020). Fitzgerald et al. reviewed in literature that increased
power of gamma wave was a biomarker of depression
(Fitzgerald and Watson, 2018). In this study, we observed
that tPBM facilitated enhancement of alpha oscillation power
and decrease of gamma oscillation power, creating exactly
opposite effects to those from the mentioned brain disorders.
Thus, the reduction of gamma power in EEG networks by
tPBM is another piece of supporting evidence that tPBM
may create multiple beneficial and therapeutic effects on
the human brain.
Limitations and Future Work
While our exploratory work revealed several novel findings on
tPBM-evoked neuromodulation on human EEG brain networks,
we acknowledge several limitations of the current study and point
out potential development for future investigations.
First, although many spatial similarities were found between
the EEG-derived brain networks and the fMRI-recognized
networks in this study, we did not have any quantitative
means to pair them between the two brain-mapping/imaging
systems (i.e., EEG and fMRI). Also, the EEG electrodes in
our study were systematically moved ∼1–1.5 cm toward
the back of the head; that would generate certain shifts in
the 2D and/or 3D EEG brain networks. This physical shift
added uncertainty for good matches between our SVD-
derived EEG brain network and conventional fMRI-created
brain network. Thus, concurrent EEG-fMRI recording
along with tPBM would be an ideal experimental design,
with high technical challenges, that enables to confirm or
better understand the relationships between tPBM and
responsive brain networks in the human brain. Second,
in this study, the source localization was performed based
on 64 electrodes of EEG, which might not be adequate
to provide high spatial resolution for accurate localization
of cortical sources. For example, although we provided
evidence of temporal independency of the first three SVD
components that had close-to-zero correlations between their
time dynamics, we were unable to spatially separate their
cortical active regions with eLORETA, mainly caused by
the low resolution in 3D source localization. Nevertheless,
this inseparable sources among the three SVD components
do not diminish the feasibility and correctness of SVD-
derived components. This is because the mathematical rigor
of the SVD algorithm warrants to accurately decompose
multi-electrode EEG into independent components (i.e., 2D
topographies) in the EEG sensor space. One solution is to take
high-density EEG measures with 128 or 256 electrodes in the
future, which can improve the spatial resolution of 3D source
localization.
CONCLUSION
We implemented gSVD as a novel methodology to analyze
human EEG signal, followed by eLORETA source localization,
two of which together facilitated the novel identification
of 11 independent and orthogonal EEG brain networks in
both EEG sensor and source spaces. Following a sham-
controlled experimental protocol, we found that right-forehead,
1,064-nm tPBM could neuromodulate the alpha and gamma
powers on several of the gSVD-derived EEG brain networks.
Moreover, many similarities were observed/found between
the EEG cortical networks and fMRI-recognized networks,
demonstrating that prefrontal tPBM can neuromodulate the
well-defined (i.e., MRI-derived) default-mode network, frontal-
parietal network, and executive control network. These results
also clearly reveal mechanistic associations or causal effects
of tPBM and modulated brain networks versus improved
cognition outcomes.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will
be made available by the authors upon request, without
undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by the Institutional Review Board of the University
of Texas at Arlington. The patients/participants provided their
written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
XW analyzed the data, interpreted the results, and prepared
the manuscript. HW recruited human participants, collected
experimental data, and managed/organized the data. AW assisted
the data collection, discussed the results, and reviewed the
manuscript. HL initiated and supervised the study, discussed
and interpreted the results, as well as reviewed and revised the
manuscript.
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Wang et al. Brain Networks Modulated by tPBM
FUNDING
This research was funded in part by the National Institute of
Mental Health, NIH (RF1MH114285).
ACKNOWLEDGMENTS
We wish to express our sincere appreciation to Cell Gen
Therapeutics LLC, Dallas, Texas, for their generous support with
the 1,064-nm laser. We also acknowledge technical support from
Professor Jacek Dmochowski who shared his EEG pre-processing
(rPCA) codes and had constructive discussions during the early
phase of this study.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fnhum.
2022.853909/full#supplementary-material
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Conflict of Interest: The authors declare that the research was conducted in the
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The handling editor FG-L declared a past co-authorship with the authors.
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