Access to this full-text is provided by Springer Nature.
Content available from Scientific Reports
This content is subject to copyright. Terms and conditions apply.
1
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
Pulsed Near Infrared Transcranial
and Intranasal Photobiomodulation
Signicantly Modulates Neural
Oscillations: a pilot exploratory
study
Reza Zomorrodi1,2, Genane Loheswaran2, Abhiram Pushparaj3 & Lew Lim2
Transcranial photobiomodulation (tPBM) is the application of low levels of red or near-infrared (NIR)
light to stimulate neural tissues. Here, we administer tPBM in the form of NIR light (810 nm wavelength)
pulsed at 40 Hz to the default mode network (DMN), and examine its eects on human neural
oscillations, in a randomized, sham-controlled, double-blinded trial. Using electroencephalography
(EEG), we found that a single session of tPBM signicantly increases the power of the higher oscillatory
frequencies of alpha, beta and gamma and reduces the power of the slower frequencies of delta and
theta in subjects in resting state. Furthermore, the analysis of network properties using inter-regional
synchrony via weighted phase lag index (wPLI) and graph theory measures, indicate the eect of tPBM
on the integration and segregation of brain networks. These changes were signicantly dierent when
compared to sham stimulation. Our preliminary ndings demonstrate for the rst time that tPBM can
be used to non-invasively modulate neural oscillations, and encourage further conrmatory clinical
investigations.
Photobiomodulation (PBM) refers to the application of low levels of red or near-infrared (NIR) light to either
stimulate or inhibit biological cells and tissues involving photochemical mechanisms1. It was rst discovered in
1967 when Endre Mester observed that low powered laser treatment promoted hair regrowth and wound healing
in rats2,3. is inspired numerous investigations into the use of low level lasers and light emitting diodes (LEDs)
for therapeutic purposes, collectively termed ‘low level light therapy’ (LLLT). In 2015, a global initiative was taken
by researchers in this eld to standardize the term to ‘photobiomodulation’.
Transcranial PBM (tPBM), targeting delivery of light energy to the brain, is associated with increased cerebral
blood ow, oxygen availability and consumption, adenoside triphophosphate (ATP) production, and improved
mitochondrial activity4. More recently, tPBM has demonstrated its value as a treatment for neurological5–10 and
neurodegenerative conditions, including Alzheimer’s disease11,12.
us, tPBM is a form of non-invasive brain stimulation (NIBS). However, compared to the more established
forms of NIBS, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation
(tDCS), the concept of the brain being responsive to light stimulation is unfamiliar to many. In recent years,
research on the potential ecacy of tPBM has gained momentum13. Research on the eect of PBM on brain cell
recovery has shown that, under laboratory conditions, damaged neurons can regrow their neurites with direct
exposure to visible red low level lasers14. In an animal study, PBM has been found capable of promoting neuro-
genesis aer ischemic stroke through the proliferation and dierentiation of internal neuroprogenitor cells15.
e eect of PBM on mitochondrial function is the most well investigated mechanism of its potential thera-
peutic eects4. PBM has been demonstrated to increase the activity of complexes in the electron transport chain
of mitochondria, including complexes I, II, III, IV and succinate dehydrogenase16. In particular, increased activity
of the transmembrane protein complex IV, also known as the enzyme cytochrome c oxidase, during PBM results
1Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.
2Vielight Inc., Toronto, Ontario, Canada. 3Ironstone Product Development Inc. & Qunuba Sciences Inc., Toronto, Ontario,
Canada. Correspondence and requests for materials should be addressed to R.Z. (email: Reza.Zomorrodi@camh.ca)
Received: 1 November 2018
Accepted: 5 April 2019
Published: xx xx xxxx
OPEN
Content courtesy of Springer Nature, terms of use apply. Rights reserved
2
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
in increased ATP production16. Furthermore, PBM results in activation of signaling pathways and transcription
factors resulting in increased expression of genes related to protein synthesis, cell migration and proliferation,
anti-inammatory signaling, anti-apoptotic protein and antioxidant enzymes4.
In a recent review of NIBS methods that included a comparison between tDCS, TMS and tPBM, Giordano et
al., recognized that the mechanisms of tPBM are better understood than tDCS but “there is little evidence to date
that (tPBM) produces direct neural activity”17. It is therefore timely that this study presents the eects of tPBM
in terms of electrophysiological measures of the brain using electroencephalography (EEG). Apart from the fun-
damental mitochondrial-based mechanism resulting from the delivery of NIR to brain tissues, there have been
indications that certain adjustable parameters may have an inuence on neural activities, particularly the pulsing
rate of the NIR delivery12,18,19. is double-blind, crossover study gives us the opportunity to analyze objective
data to understand the eect of a selected pulse frequency of 40 Hz and other PBM parameters on neural activity.
It may open up the possibility of exploring the eects of alternative PBM parameters in future studies.
Methods
Study Design. is was a small, exploratory, double-blind, prospective cross-over study. Study subjects
attended two study visits (Fig.1): one visit where the subjects received active tPBM stimulation with rest EEG and
another visit where the subjects received sham tPBM stimulation with rest EEG. e orders of the two visits were
randomized and there was a minimum one-week washout period between the two visits.
Study Visits. At the beginning of the rst study visit, each subject was screened for eligibility before proceeding
with enrollment. en the subject was asked to be seated in a comfortable chair and a 10 minute eyes closed EEG
recording was collected. Next, an active or sham tPBM device (described below) was positioned on the subject’s head
and turned on for 20 minutes. en the device was removed and another 10 minute eyes closed rest EEG was recorded.
Subjects. Twenty healthy adults (mean age 68.00 ± 5.94, 61–74 years of age, 9 Males) were recruited (Table1).
Written informed consent for participation in the study was obtained from all the subjects prior to enrollment
into the study. e study was conducted in compliance with the Declaration of Helsinki and was approved by the
research ethics board of IRB Services Canada. e individual included in Fig.2 and Supplementary Fig.2 has pro-
vided informed consent for publication of identifying information/images in an online open-access publication.
All data was anonymized and no subject identifying information or images are published. Subjects were excluded
if they had a Mini-Mental State Examination Score <27, a current major psychiatric or neurologic disease, a his-
tory of stroke, seizures and/or a medical condition uncontrolled with stable therapy. Subjects were compensated
for their participation at the end of the last study visit.
tPBM device. e tPBM device used in the study was the ‘Vielight Neuro Gamma’ (Neuro Gamma). It is a
portable, wearable, low-level light delivery device that administers near-infrared light to the brain transcranially
and intra-nasally. Its specications and intended use fall within the denition of a low risk general wellness device
in a policy guideline released by the Center for Devices and Radiological Health of the United States Food and
Drugs Administration in 2016, which exempts it from medical device regulations20. e device consists of a con-
troller, a nasal applicator, and a head set with four light emitting diode (LED) modules (Fig.2).
Figure 1. Schematic diagram of study design. Twenty healthy participants randomized to receive either active
or sham tPBM with a minimum 1-week washout period between the two visits. 10 minutes eye-closed rest EEG
recorded pre and post of each intervention.
Gender Sample Size (N) Age(years) Education(years)
Female 11 69 ± 6.32 12 ± 4.11
Male 9 66 ± 4.02 14 ± 3.71
Table 1. Demographic characteristics of the study population.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
3
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
e Neuro Gamma delivers painless, non-invasive, non-thermal, non-laser, pulsed (40 Hz; 50% duty cycle),
near-infrared light (810 nm wavelength) through 5 non-laser LEDs over a 20- minute session. e device is pow-
ered by three rechargeable NiMH batteries. e LEDs have been designed to be positioned to deliver the near
infrared light (NIR) to the subdivisions of the default mode network (DMN)19. ese subdivisions include the
ventral medial prefrontal cortex (vmPFC); the dorsal medial prefrontal cortex (dmPFC); the posterior cingulate
cortex (PCC); adjacent precuneus (PCu) plus the lateral parietal cortex (LPC) and the entorhinal cortex (EC)19.
“Hubs” and “nodes” described in literature are synonymous with these subdivisions or are located within the
vicinity of these subdivisions. As shown in Fig.2, one of the Neuro Gamma LEDs is placed inside the nose for
intranasal transmission of NIR light to the ventral section of the brain which includes the vmPFC and the olfac-
tory bulb which has a direct activating projection to the EC and parahippocampal area21. e remaining 4 LEDs
on the headset are positioned over selected locations to direct NIR to the neocortical subdivisions of the DMN -
the dmPFC, PCu, and posterior PCC (NIR light directed through the le and right angular gyri). ese locations
correspond to FPz, Cz, T3 and T4 respectively, described under the 10–20 EEG montage.
e group of LEDs containing those positioned on the dmPFC and intranasally (Group A LEDs) pulse in
synchrony (in-phased) and the other group of LEDs on the PCu and le and right LPCs (Group 2 LEDs) also
pulse in synchrony (in-phased) within its group. Between Group A and Group B LEDs, the pulsing frequencies
are completely asynchronous (out-of-phase).e power density output of the nasal applicator is 25 mW/cm2,
anterior LED is 75 mW/cm2, and three posterior LEDs, 100 mW/cm2. Over the set-time of 20 minutes, the energy
dose to the brain (headset and intranasal applicator) equals to 240 J/cm2. Detailed specications and parameters
are set out in Table2.
The Neuro Gamma has been independently tested by TUV SUD Canada for Electrical Safety as well as
Emissions & Immunity for Multimedia Class B Equipment.
EEG acquisition. The EEG signals were recorded using the DISCOVERY 24E (Brainmaster Inc.) at
256 Hz sampling rate and a bandwidth of 0.43–80 Hz. e amplier was a low-noise DC-sensitive and a 24-bit
analog-to-digital device. For the EEG cap, we used a 19-Channel free-cap set (Institut für EEG-Neurofeedback),
which allows EEG to be recorded during tPBM delivery. e EEG channels located at a 10–20 montage on the
elastic net. e data was collected during the 10-minute sessions with the subjects at rest and with the eyes closed,
before and aer Neuro Gamma stimulation.
Figure 2. e Vielight Neuro Gamma in use. e stimulation modules consist of a Nasal Applicator, and a
Head Set with four light emitting diode (LED) modules intended to be positioned over the hubs of the default
mode network (DMN).
Source LED
Wavelength (nm) 810
Power output of LED on the anterior band (mW) 75(transcranial)
Power output of each LED on the posterior band (mW) 25(intranasal)
Power density of LED on the anterior band (mW/cm2) 100(transcranial)
Power density of each LED on the posterior band (mW/cm2) 25(intranasal)
Pulse frequency (Hz) 40
Pulse duty cycle, percentage 50
Duration of each treatment session (min) 20
Beam spot size (cm2)≈1
Total energy delivered per session (Joules) 240
Total energy density per session (Joules/cm2) 240
Table 2. Vielight Neuro Gamma Parameters.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
4
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
EEG data preprocessing. EEG data was processed oine using a custom MATLAB script (MathWorks, MA,
USA), and EEGLAB toolbox (Swartz Center for Computational Neuroscience, University of California at San Diego)
in the following sequence. First, EEG data were visually inspected to remove noisy channels or highly contaminated
artifacts. ereaer, EEG data were digitally ltered by using a second order, Butterworth, zero-phase shi 1–55 Hz
band pass lter (24 dB/Oct), and then segmented into 2 sec epochs. en, an electrodes-by-trials matrix of ones was
created and assigned a value of zero if an epoch had: (1) amplitude larger than +/−150 μV; (2) standard deviation
3 times larger than the average of all trials; or (3) power spectrums that violated 1/f power law. An electrode was
rejected if its corresponding row had more than 60% of columns (trials) coded as zeros. An epoch was removed if its
corresponding column had more than 20% of rows (electrodes) coded as zeros. An independent component analysis
(ICA) (EEGLAB toolbox; Infomax algorithm) was performed to remove ocular, muscle artifacts, and other noise
from the EEG data. Finally, the data was re-referenced to the average for further analysis.
EEG analysis of power, network connectivity and synchrony. e power spectrum analysis was con-
ducted using the spectopo () function as implemented in EEGLAB, using Welch’s method (with a window length of
512 points, () length of 1024 points, non-overlap). e absolute power was calculated for each channel and each
frequency band: Delta (1–3) Hz, eta (4–7) Hz, Alpha (8–14) Hz, Beta (14–30) Hz and Gamma (30–50) Hz.
To explore a possible change in brain functional connectivity and synchrony, we used the weighted phase lag
index (wPLI) and graph theory measure. e weighted phase lag index is a functional connectivity measure and
assesses the phase ‘lagging’ consistency between each pair of EEG channels. e advantages of wPLI over other
cerebral synchrony assessments are its lower sensitivity to noise and volume conduction eects, therefore provid-
ing more reliable evaluation of long range synchronization of neural activity22. e wPLI ranges between 0 (no
phase consistency) and 1 (full synchrony).
A network based on wPLI was constructed and graph measures were employed to evaluate network integra-
tion, segregation and quantifying eciency of information transfer23,24. Four graph measures were computed
using the Brain connectivity toolbox (BCT)25: (I) e characteristic path length (CPL), which is the average
length of all pairwise shortest paths connecting any node to another, to assess integration; (II) the clustering
coecient (CC), which is the mean nodal CC averaged across all vertices to assess segregation; (III) the global
network eciency, which quanties the exchange of information across the whole network; and (IV) the local
network eciency, which quanties a network’s resistance to failure on a small scale and characterizes how well
information is exchanged by its neighbors when a node is removed.
EEG channels and the value of wPLI represent nodes (i.e, 19) and edge (i.e., 19 × 19) of the network, respec-
tively. For each frequency band, we applied dierent sparsity thresholds by choosing 5–100% of the strongest
wPLI to binarize the edges. e sparsity-based threshold ensures the same number of edges for each network.
en, we evaluated the signicance of changes in the network properties before and aer active and sham stimu-
lations for dierent sparsity levels.
Statistical analyses. Dierences in power spectrum over all electrodes were statistically assessed using a
cluster-based permutation test that identies clusters of electrodes with signicant changes, while correcting
for multiple comparisons26. Cluster-level statistics were computed by taking the sum of the t-values over adja-
cent neighboring electrodes. Clusters were dened as two or more spatially contiguous electrodes in which the
t-statistics of power spectrum exceeded a chosen threshold of alpha level of p < 0.05 and αcluster = 0.01. e null
distribution was obtained by randomly permuting 1000 pieces of data (i.e., randomizing data across pre and post
tPBM and rerunning the statistical test). e Matlab toolbox Fieldtrip was used for this analysis13,27. To sum-
marize the data, we averaged the power of 19 EEG electrodes for each subject and ran two sided nonparametric
Wilcoxon tests to compare the ratio of post over pre-stimulation values of active and sham conditions.
At each level of network sparsity level, the brain network properties were compared using two sided nonpar-
ametric Wilcoxon paired-sample t-tests. e brain networks with dierent sparsity levels are considered inde-
pendent graphs, and thus the correction for multiple comparisons (e.g. Bonferroni correction) is not required24,25.
Results
Power spectrum analysis. Cluster-based permutation tests, with t-values presented by topological maps,
are reported in Fig.3 for comparison between pre- and post-tPBM sessions in both sham and active conditions.
In Fig.3, clusters of electrodes with power values that are signicantly dierent (p < 0.05 aer Bonferroni correc-
tion) between the two conditions are marked by a plus sign. Decreases or increases in absolute power are color
coded in dark blue and dark red, respectively.
Signicant absolute power spectrum alterations aer 20 minutes of active tPBM were observed in all oscilla-
tory frequency bands. Figure3a illustrates the pre- and post-stimulation dierences in power spectrum for active
tPBM. In the delta and theta frequency bands, there were signicant increases in power in the centro-frontal elec-
trodes (4 < t-values < 6, p < 0.05) but no signicant dierence in the temporal, parietal and occipital electrodes
(t = 0.156, p > 0.05). In the alpha, beta and gamma bands, we observed signicant increases (t-value > 7, p < 0.05)
in almost all of the electrodes. Twenty minutes of sham tPBM resulted in signicant changes in power spectrum,
but with non-specic locations and frequency signatures (5 < t-values < 6, p < 0.05) (Fig .3b). e comparison
between rest-EEG before (or pre) both active and sham conditions (1 week interval) showed no signicant dier-
ence across all electrodes (Fig.3c). However, the comparison between rest-EEG aer (or post-) active and sham
conditions revealed a distinct eect of active tPBM on the power spectrum. Our data showed: (I) decreases in
the lower oscillatory frequencies (i.e., delta and theta) with signicant reductions in the posterior region and, (II)
increases in higher oscillatory frequency bands with signicant alterations in centro-frontal regions for alpha and
beta, and a signicant global change in gamma power (t-value > 3, p < 0.05) (Fig.3d).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
5
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
To evaluate the overall tPBM eects, we averaged the power spectrum across all electrodes for each oscillatory
frequency band and compared the ratio of post- over pre-session rest-EEG for both active and sham conditions.
Figure4 illustrates the boxplot of absolute power ratio. ere was an overall increase in power when comparing
post- to pre-stimulation in both the active (t = 6.855; p < 0.01; Fig.4a) and sham stimulation conditions (t = 6.996;
p < 0.01; Fig.4b). is increase was seen in all frequency bands for both sham (delta: t = 3.403, p < 0.01; theta:
t = 4.415, p < 0.01; alpha: t = 11.876, p < 0.01; beta: t = 10.756, p < 0.01; gamma: t = 10.876; p < 0.01; Fig.4b) and
active stimulation (delta: t = 6.177, p < 0.01; theta: t = 7.589, p < 0.01; alpha: t = 8.572, p < 0.01; beta: t = 9.553,
p < 0.01; gamma: t = 8.637; p < 0.01; Fig .4a). Interestingly, the change in power was frequency dependent during
active stimulation. In the active stimulation, there was a suppression in the increase in power of the delta and
theta bands that was observed during sham stimulation. Conversely, active stimulation produced a facilitation of
the increase in power in alpha, beta and gamma compared to sham.
A comparison between the active and sham groups presented signicant dierences in delta (t = −3.513
p < 0.01), theta (t = −3.736 p < 0.01), alpha (t = 4.455 p < 0.01), beta (t = 3.221 p < 0.01), and gamma (t = 2.658,
p < 00.1) frequency bands (Fig.4c).
Brain network functional connectivity and synchrony analyses. Brain network connectivity and
synchrony analyses in this study were based on the weighted phase lag index (wPLI) and graph measures to show
changes in the clustering coecient, characteristic path length (CPL) and local eciency measures. For each
frequency band and each sparsity level, mean and standard deviation of network indexes were plotted for both
active and sham tPBM conditions (Figs5–8).
e results of the functional network connectivity and synchrony analyses for dierent measures are as fol-
lows: For the active device, (I) e average cluster coecient (CC) showed signicant changes for a wide range
of sparsity levels: 45–80% sparsity levels in the alpha band, 35–55% and 65% sparsity levels in the gamma band,
75% in the theta band, and 80–85% in the beta band (Fig.5a). When the sham device was used (Fig.5b), the
average CC did not show signicant changes for most of the sparsity levels and frequency bands, apart from the
75%, 82% and 90% sparsity levels in the beta band. (II) e characteristic path length (CPL) showed a signicant
change only for active tPBM in the alpha band for 40–55% sparsity levels and in the gamma band for 50–55%
and 80–85% sparsity levels (Fig.6a). Sham treatments did not cause changes in the CPL for sparsity levels in any
frequency band (Fig.6b). (III) Aer active tPBM, local eciency of the network changed mostly in the alpha
band for 45–60% and 70–75% sparsity levels, in the gamma band for 40–50% sparsity levels, in the beta band for
80–85% sparsity levels, and in delta and theta bands for 85% and 80% sparsity levels, respectively (Fig.7a). In the
sham treatment condition, data showed alterations in local eciency in the beta band for 80% and 90% sparsity
levels and in the delta band at 80% (Fig.7b). (IV) Aer active tPBM treatments, the global eciency of the net-
work changed mostly in the alpha band for 50–60%, 75%, 85–90% sparsity levels, and in the gamma band for
15%, 50–60%, 70–75% and 90% sparsity levels (Fig.8a). e global eciency in sham condition did not change
in any frequency band (Fig.8b).
Figure 3. Non-parametric cluster-based permutation test comparing the rest EEG power spectrum between
active and sham tPBM. Topographical maps are color-coded according to the permutation tests t-values.
Clusters of electrodes with signicant dierence between the two conditions are marked in ‘+’ sign (p < 0.05
and αcluster = 0.01). (a) Dierence between post and pre active tPBM. (b) Dierence between post and pre sham
tPBM. (c) Dierence between pre active and sham tPMB. (d) Dierence between post active and sham tPMB.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
6
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
Absence of Side Eects. roughout the study, none of the 20 participants self-reported any adverse events
or any unusual sensation.
Discussion
The ease-of-use of tPBM, along with its seemingly advantageous tolerability profile, make it an appealing
non-invasive brain stimulation (NIBS) method. To date, no published study has demonstrated the eect of tPBM
on neural activities and brain oscillations. In this cross-over, double-blind study, our results revealed a signicant
eect of transcranial near-infrared light (810 nm wavelength) at 40 Hz pulsing rate on the power, functional con-
nectivity and synchrony of endogenous brain activity.
Both the active and sham stimulations produced an increase in power in each of the frequency bands com-
pared to baseline. is increase in power in sham stimulation was unexpected and suggests that resting wake-
fulness increases cortical activation. Previous studies have demonstrated that subjective sleepiness following
prolonged periods of wakefulness (ie. 40 hours) have resulted in dierential changes in the power of various
frequency bands during the resting wakefulness state, measured with EEG28,29. However, to our knowledge, there
has been no report on the eects of 30 minutes of resting state on the power density spectrum in absolute values.
While a consistent increase in power was observed across all the frequency bands with both active and sham stim-
ulations, the change in power was frequency dependent with active stimulation. Compared to sham, active stimulation
presented suppression of the increase in the lower frequency bands (delta and theta) and a further increase in power
in the higher frequency bands (alpha, beta, and gamma). Interestingly, higher power in the lower frequency bands and
reduced power in the higher frequency bands have been associated with disorders involving cognitive impairment,
such as dementia and Alzheimer’s disease30–34. e data observed from the use of the active tPBM device has produced
relevant results that are counter to the power spectrum characteristics of those conditions. erefore, this suggests that
active stimulation with 40 Hz NIR tPBM might be a desirable intervention to improve cognitive impairment, and could
potentially improve the cognitive function of patients with dementia and Alzheimer’s disease35,36.
e signicant alteration in the power spectrum and functional network properties in the alpha frequency
band could be due to the targeting of the default mode network (DMN) through its recognized hubs/subdi-
visions emphasized by EEG recording of subjects in resting wakefulness37–40. e DMN is a network of brain
regions that are activated when the mind wanders without engaging in tasks such as attention or action, and it
is associated with introspection37,41, which are also largely characteristic of the brain in alpha state. Among all
DMN hubs, the medial prefrontal cortex (mPFC) plays a mechanistic role in the alpha generation process37,42.
Increased alpha power is posited to aid in the inhibition of irrelevant cortical areas while integrating relevant
ones, sharing another introspective characteristic in the function of the DMN43–45. Additionally, spontaneous
self-referential thought is linked to the increase of alpha power in the posterior DMN (including the precuneus
and posterior cingulate) and gamma oscillations in the mPFC38,41,46,47. Several studies demonstrate deactivation of
DMN during task-related activities and its essential role in alternating between internal and external attention48.
Pathologies aecting the DMN include dementia, schizophrenia, autism, anxiety and depression42,49, suggesting
the importance of normalizing DMN functions. A way to achieve this could be through normalizing alpha fre-
quency oscillation.
Figure 4. Inuence of tPBM on resting-state electroencephalography. Box plot illustrates the median and range
of power spectrum across all electrodes for each oscillatory frequency bands. (a) Eect of active tPBM on power
spectrum pre (green line) and post (red line). (b) Eect of sham tPBM on power spectrum pre (green line) and
post (red line). (c) Dierence between Active and Sham tPBM: Change of power spectrum Post-Pre for active
(red line) and sham (green line) tPBM. Active versus sham stimulation revealed signicant lower alteration in
delta and theta power and higher change in alpha, beta and gamma frequency bands.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
7
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
Figure 5. Connectivity assessment using Clustering coecient (CC). (a) Active tPBM caused index
signicantly changed CC for wide range of 45–80% sparsity levels in the alpha band, and 35–55% sparsity levels
in the gamma band. (b) Sham tPBM did not caused signicant change in CC index, but beta at 75, 82 and 90%
of sparsity levels. Blue and red lines line indicate pre and post conditions, respectively. e gray lines indicate a
signicant dierence (p < 0.01) at a certain sparsity level.
Figure 6. C onnectivity assessment using the characteristic path length (CPL). (a) Active tPBM caused index
signicantly changed CLP in the alpha band for 40–55% of sparsity levels and the gamma band for 50–55% and 80–85%.
(b) Sham tPBM did not cause any signicant change in CPL index. Blue and red lines line indicates pre and post
conditions, respectively. e gray lines indicate a signicant dierence (P < 0.01) at a certain sparsity level.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
8
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
Figure 7. Connectivity assessment using Local Eciency measure. (a) Active tPBM caused signicant changes
in network local ecacy mostly in the alpha band for 45–60% and 70–75% of sparsity levels, in the gamma band
for 40–50% of sparsity levels, in the beta band for 80–85% of sparsity levels, and in delta and theta bands for 85%
and 80% of sparsity levels, respectively. (b) Sham tPBM did not cause any signicant change in the local ecacy
except in the beta band for 80 and 90% of sparsity levels. Blue and red lines line indicate pre and post conditions,
respectively. e gray lines indicate a signicant dierence (P < 0.01) at a certain sparsity level.
Figure 8. Connectivity assessment using Global Eciency measure. (a) Active tPBM caused signicant
changes in network global ecacy in the alpha band for 50–60%, 75%, 85–90% of sparsity levels, in the gamma
band for 15%, 50–60%, 70–75% and 90% of sparsity levels. (b) Sham tPBM did not cause any signicant change
in the global ecacy. Blue and red lines line indicate pre and post conditions, respectively. e gray lines
indicate a signicant dierence (P < 0.01) at a certain sparsity level.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
9
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
Additionally, by analyzing brain network properties using wPLI and the graph theory measures, we observed
signicant eects of active tPBM. e connectivity measures, which assessed the integration and segregation
properties of the network, showed a signicance increase in clustering coecient, characteristic path length
(CPL) and local eciency measures for each oscillation frequency band. e changes were most prominently in
the alpha frequency bands. e increased connectivity and synchrony achieved by inducing 40 Hz to the DMN
is novel. It warrants new investigations on how this could help with conditions that have characteristically low
connectivity and synchrony in fast frequency bands.
A recent study by Chao and colleagues also demonstrated the impact of tPBM on DMN connectivity, specically
increased connectivity in the posterior cingulate cortex and lateral parietal lobes, using functional magnetic res-
onance imaging (fMRI). is increase in connectivity was correlated with improved cognition in patients with
dementia50.
e pulse frequency employed likely plays an important role in the eects of tPBM on brain activity. Pulsing
NIR light not only minimizes the heating eect and increases the possible penetration depth51,52, but may eec-
tively interact with cellular activity via two proposed mechanisms by: (a) impacting the ionic channels kinetic
such as potassium and calcium in the mitochondria16,51,53 (b) increasing the dissociation rate of nitric oxide from
cyctochrome c oxidase9,16,54. A study by Iaccarino et al. has demonstrated that the visual processing of 40 Hz
pulsing light signicantly reduces β-amyloid deposits in the visual cortex of an AD animal model, similar to
that observed from optogenetic “gamma entrainment” of fast-spiking parvalbumin-positive interneurons19. is
nding further suggests that the 40 Hz pulsing of NIR tPBM should be further explored to address AD pathology.
We hypothesize that the action of 40 Hz NIR tPBM results in increased organization of neural function, which
is likely to be accompanied by an increase in inhibition. During cognitive processes such as memory consolida-
tion, the presence of gamma oscillations prevents neurotoxicity55. e amplitude of gamma oscillations is associ-
ated with GABA levels, with increased levels of GABA being correlated to an increased amplitude of the gamma
band56. Future studies are required to conrm the eect of 40 Hz NIR tPBM on GABA levels.
ere are several established theories for the ability of PBM to act locally and systemically; however, to our
knowledge, this is the rst study demonstrating an eect at the network level, through the observed changes in
brain oscillations. is network eect may be mediated by increased intracellular Ca2+ concentration which has
been observed at certain PBM doses57,58 potentially occurring through the engagement of the voltage-gated cal-
cium channels59. Animal experiments have produced evidence of signicant increases in the membrane potential
with Ca2+ potentially underlying gamma oscillations of around 40 Hz60,61.
Together, these ndings present the potential of tPBM as a valuable form of NIBS, oering a safe experi-
mental tool to interact with the brain. PBM has minimal reported adverse eects, provided the parameters are
understood at least at a basic level62. e potential of a tPBM device to signicantly modulate the brain opens
new opportunities for its use in research and therapeutic settings. is study presents for the rst time, the sig-
nicant modulatory eect tPBM has on the brain oscillatory patterns as measured with EEG. e main mod-
ulating parameters in the device used in the study are likely to be the 810 nm wavelength and the pulse rate of
40 Hz. Delivering these parameters to the hubs/subdivisions of the default mode network this way, signicantly
increased the power of the high oscillatory frequencies of alpha, beta and gamma; and reduced the power of the
slower frequencies of delta and theta. ese have been achieved aer each single session of active treatment with
the subjects in resting wakefulness state. In addition, the brain presented global inhibition, indicating increased
organization.
Some important indications have been achieved in this study: (I) Despite not being obvious and no experience
of sensation by the subject, NIR light delivered with relatively low power density can draw response from the
brain that are measurable with EEG. (II) e signicant eects of tPBM when applied as presented in this study.
ese promising preliminary results present a considerable forward step leading to the need of a larger conrm-
atory clinical investigation, which could establish the role of tPBM as an eective non-invasive neuromodulatory
tool.
e potential capacity of tPBM to induce brain wave entrainment provides the opportunity to explore the
eect of varying selected parameters, such as the pulse frequencies, location of the LED modules, power out-
put, and pulse coherency between selected LEDs. For example, we could investigate the eect of two selected
LED locations to pulse in-phase (to increase coherency/synchrony) or out-of-phase (to reduce coherency/syn-
chrony). We could also investigate the dierences in outcomes of pulse synchrony between brain networks or
compare them with whole-brain synchrony. Answers to these investigations could potentially provide customized
approaches to the modulation of brain wave patterns. e relatively quick brain response suggests that tPBM is
a good candidate for use with neurofeedback methodologies, in order to optimize parameters for specic con-
ditions. Medical conditions such as Alzheimer’s disease which express low power and synchrony in the alpha,
beta and gamma and high power and synchrony in delta and theta brainwaves are obvious targets of future tPBM
research. e adjustability of the various parameters of tPBM, along with the relatively quick response observed
by EEG, is optimal for the exploration of customized treatments for varying indications.
References
1. Hamblin, M. . Photobiomodulation or low-level laser therapy. J Biophotonics 9, 1122–1124, https://doi.org/10.1002/jbio.201670113
(2016).
2. Mester, E., Ludany, G., Sellyei, M. & Szende, B. On the biologic eect of laser rays. Bull Soc Int Chir 27, 68–73 (1968).
3. Mester, E., Szende, B. & Gartner, P. e eect of laser beams on the growth of hair in mice. Radiobiol Radiother (Berl) 9, 621–626
(1968).
4. Hamblin, M. . Shining light on the head: Photobiomodulation for brain disorders. BBA Clin 6, 113–124, https://doi.org/10.1016/j.
bbacli.2016.09.002 (2016).
5. Detaboada, L. et al. Transcranial application of low-energy laser irradiation improves neurological decits in rats following acute
stroe. Lasers Surg Med 38, 70–73, https://doi.org/10.1002/lsm.20256 (2006).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
10
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
6. Oron, A. et al. Low-level laser therapy applied transcranially to rats aer induction of stroe signicantly reduces long-term
neurological decits. Stroke 37, 2620–2624, https://doi.org/10.1161/01.ST.0000242775.14642.b8 (2006).
7. Oron, A. et al. Near infrared transcranial laser therapy applied at various modes to mice following traumatic brain injury signicantly
reduces long-term neurological decits. J Neurotrauma 29, 401–407, https://doi.org/10.1089/neu.2011.2062 (2012).
8. Lampl, Y. Laser treatment for stroe. Expert Rev Neurother 7, 961–965, https://doi.org/10.1586/14737175.7.8.961 (2007).
9. Hashmi, J. T. et al. ole of low-level laser therapy in neurorehabilitation. PM R 2, S292–305, https://doi.org/10.1016/j.
pmrj.2010.10.013 (2010).
10. Naeser, M. A., Saltmarche, A., rengel, M. H., Hamblin, M. . & night, J. A. Improved cognitive function aer transcranial, light-
emitting diode treatments in chronic, traumatic brain injury: two case reports. Photomed Laser Surg 29, 351–358, https://doi.
org/10.1089/pho.2010.2814 (2011).
11. Sommer, A. P. et al. 670 nm laser light and EGCG complementarily reduce amyloid-beta aggregates in human neuroblastoma cells:
basis for treatment of Alzheimer’s disease? Photomed Laser Surg 30, 54–60, https://doi.org/10.1089/pho.2011.3073 (2012).
12. Saltmarche, A. E., Naeser, M. A., Ho, . F., Hamblin, M. . & Lim, L. Signicant Improvement in Cognition in Mild to Moderately
Severe Dementia Cases Treated with Transcranial Plus Intranasal Photobiomodulation: Case Series eport. Photomed Laser Surg 35,
432–441, https://doi.org/10.1089/pho.2016.4227 (2017).
13. Salehpour, F. et al. Brain Photobiomodulation erapy: a Narrative eview. Mol Neurobiol 55, 6601–6636, https://doi.org/10.1007/
s12035-017-0852-4 (2018).
14. Giuliani, A. et al. Low infra red laser light irradiation on cultured neural cells: eects on mitochondria and cell viability aer
oxidative stress. BMC Complement Altern Med 9, 8, https://doi.org/10.1186/1472-6882-9-8 (2009).
15. Yang, L. et al. Photobiomodulation therapy promotes neurogenesis by improving post-stroe local microenvironment and
stimulating neuroprogenitor cells. Exp Neurol 299, 86–96, https://doi.org/10.1016/j.expneurol.2017.10.013 (2018).
16. de Freitas, L. F. & Hamblin, M. . Proposed Mechanisms of Photobiomodulation or Low-Level Light erapy. IEEE J Sel Top
Quantum Electron 22, https://doi.org/10.1109/JSTQE.2016.2561201 (2016).
17. Giordano, J. et al. Mechanisms and Eects of Transcranial Direct Current Stimulation. Dose Response 15, 1559325816685467,
https://doi.org/10.1177/1559325816685467 (2017).
18. Ando, T. et al. Comparison of therapeutic eects between pulsed and continuous wave 810-nm wavelength laser irradiation for
traumatic brain injury in mice. PLoS One 6, e26212, https://doi.org/10.1371/journal.pone.0026212 (2011).
19. Iaccarino, H. F. et al. Gamma frequency entrainment attenuates amyloid load and modies microglia. Nature 540, 230–235, https://
doi.org/10.1038/nature20587 (2016).
20. Health, C. f. D. a. . Device Regulation and Guidance, https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/
guidancedocuments/ucm429674.pdf) (2016).
21. Biella, G. & de Curtis, M. Olfactory inputs activate the medial entorhinal cortex via the hippocampus. J Neurophysiol 83, 1924–1931,
https://doi.org/10.1152/jn.2000.83.4.1924 (2000).
22. Ortiz, E. et al. Weighted phase lag index and graph analysis: preliminary investigation of functional connectivity during resting state
in children. Comput Math Methods Med 2012, 186353, https://doi.org/10.1155/2012/186353 (2012).
23. Peters, J. M. et al. Brain functional networs in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity.
BMC Med 11, 54, https://doi.org/10.1186/1741-7015-11-54 (2013).
24. Stam, C. J., Jones, B. F., Nolte, G., Breaspear, M. & Scheltens, P. Small-world networs and functional connectivity in Alzheimer’s
disease. Cereb Cortex 17, 92–99, https://doi.org/10.1093/cercor/bhj127 (2007).
25. ubinov, M. & Sporns, O. Complex networ measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069,
https://doi.org/10.1016/j.neuroimage.2009.10.003 (2010).
26. Maris, E. & Oostenveld, . Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164, 177–190, https://doi.
org/10.1016/j.jneumeth.2007.03.024 (2007).
27. Oostenveld, ., Fries, P., Maris, E. & Schoelen, J. M. FieldTrip: Open source soware for advanced analysis of MEG, EEG, and
invasive electrophysiological data. Comput Intell Neurosci 2011, 156869, https://doi.org/10.1155/2011/156869 (2011).
28. Strijstra, A. M., Beersma, D. G., Drayer, B., Halbesma, N. & Daan, S. Subjective sleepiness correlates negatively with global alpha
(8–12 Hz) and positively with central frontal theta (4–8 Hz) frequencies in the human resting awae electroencephalogram. Neurosci
Lett 340, 17–20, S0304394003000338 (2003).
29. Dij, D. J. et al. Melatonin eect on daytime sleep in men: suppression of EEG low frequency activity and enhancement of spindle
frequency activity. Neurosci Lett 201, 13–16, doi:030439409512118N (1995).
30. Brenner, . P. et al. Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. Electroencephalogr
Clin Neurophysiol 64, 483–492, doi:0013-4694(86)90184-7 (1986).
31. Coben, L. A., Danziger, W. L. & Berg, L. Frequency analysis of the resting awae EEG in mild senile dementia of Alzheimer type.
Electroencephalogr Clin Neurophysiol 55, 372–380, doi:0013-4694(83)90124-4 (1983).
32. Coben, L. A., Danziger, W. & Storandt, M. A longitudinal EEG study of mild senile dementia of Alzheimer type: changes at 1 year
and at 2.5 years. Electroencephalogr Clin Neurophysiol 61, 101–112, doi:0013-4694(85)91048-X (1985).
33. Huang, C. e t al. Discrimination of Alzheimer’s disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional
and longitudinal study. Clin Neurophysiol 111, 1961–1967, doi:S1388245700004545 (2000).
34. Babiloni, C. et al. Mapping distributed sources of cortical rhythms in mild Alzheimer’s disease. A multicentric EEG study.
Neuroimage 22, 57–67, https://doi.org/10.1016/j.neuroimage.2003.09.028S1053811903005743 (2004).
35. Vielight Neuro Rx Gamma- Feasibility Pilot, https://clinicaltrials.gov/ct2/show/NCT03328195?term=Vielight&recrs=ab&ran=1
(2018).
36. Neuro RX Gamma - Pivotal Phase, https://clinicaltrials.gov/ct2/show/NCT03484143 (2018).
37. B onnard, M. et al. esting state brain dynamics and its transients: a combined TMS-EEG study. Sci Rep 6, 31220, https://doi.
org/10.1038/srep31220 (2016).
38. Chen, A. C., Feng, W., Zhao, H., Yin, Y. & Wang, P. EEG default mode networ in the human brain: spectral regional eld powers.
Neuroimage 41, 561–574, https://doi.org/10.1016/j.neuroimage.2007.12.064 (2008).
39. nyazev, G. G., Slobodsoj-Plusnin, J. Y., Bocharov, A. V. & Pylova, L. V. e default mode networ and EEG alpha oscillations: an
independent component analysis. Brain Res 1402, 67–79, https://doi.org/10.1016/j.brainres.2011.05.052 (2011).
40. Neuner, I. et al. e default mode networ and EEG regional spectral power: a simultaneous fMI-EEG study. PLoS One 9, e88214,
https://doi.org/10.1371/journal.pone.0088214 (2014).
41. Mantini, D., Perrucci, M. G., Del Gratta, C., omani, G. L. & Corbetta, M. Electrophysiological signatures of resting state networs
in the human brain. Proc Natl Acad Sci USA 104, 13170–13175, https://doi.org/10.1073/pnas.0700668104 (2007).
42. Bowman, A. D. et al. elationship Between Alpha hythm and the Default Mode Networ: An EEG-fMI Study. J Clin Neurophysiol
34, 527–533, https://doi.org/10.1097/WNP.0000000000000411 (2017).
43. Deco, G., Jirsa, V. . & McIntosh, A. . Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat
Rev Neurosci 12, 43–56, https://doi.org/10.1038/nrn2961 (2011).
44. ut, G., Nietzel, A., Brandt, S. A. & Pascual-Leone, A. Alpha-band electroencephalographic activity over occipital cortex indexes
visuospatial attention bias and predicts visual target detection. J Neurosci 26, 9494–9502, https://doi.org/10.1523/
JNEUOSCI.0875-06.2006 (2006).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
11
SCIENTIFIC REPORTS | (2019) 9:6309 | https://doi.org/10.1038/s41598-019-42693-x
www.nature.com/scientificreports
www.nature.com/scientificreports/
45. Jensen, O. & Mazaheri, A. Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front Hum Neurosci 4,
186, https://doi.org/10.3389/fnhum.2010.00186 (2010).
46. Voyte, B. et al. Shis in gamma phase-amplitude coupling frequency from theta to alpha over posterior cortex during visual tass.
Front Hum Neurosci 4, 191, https://doi.org/10.3389/fnhum.2010.00191 (2010).
47. Berovich-Ohana, A., Glicsohn, J. & Goldstein, A. Mindfulness-induced changes in gamma band activity - implications for the
default mode networ, self-reference and attention. Clin Neurophysiol 123, 700–710, https://doi.org/10.1016/j.clinph.2011.07.048
(2012).
48. Singh, . D. & Fawcett, I. P. Transient and linearly graded deactivation of the human default-mode networ by a visual detection
tas. Neuroimage 41, 100–112, https://doi.org/10.1016/j.neuroimage.2008.01.051 (2008).
49. Broyd, S. J. et al. Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 33, 279–296,
https://doi.org/10.1016/j.neubiorev.2008.09.002 (2009).
50. Chao, L. (in press) Eects of home photobiomodulation treatments on cognitive and behavioral function, cerebral perfusion, and
resting-state functional connectivity in patients with dementia: A pilot trial. Photobiomodulation, Photomedicine, and Laser Surgery
(2019).
51. Hashmi, J. T. et al. Eect of pulsing in low-level light therapy. Lasers Surg Med 42, 450–466, https://doi.org/10.1002/lsm.20950
(2010).
52. Ilic, S. et al. Eects of power densities, continuous and pulse frequencies, and number of sessions of low-level laser therapy on intact
rat brain. Photomed Laser Surg 24, 458–466, https://doi.org/10.1089/pho.2006.24.458 (2006).
53. aru, T. I., Pyatibrat, L. V. & Afanasyeva, N. I. A novel mitochondrial signaling pathway activated by visible-to-near infrared
radiation. Photochem Photobiol 80, 366–372, https://doi.org/10.1562/2004-03-25-A-123 (2004).
54. Lane, N. Cell biology: p ower games. Nature 443, 901–903, https://doi.org/10.1038/443901a (2006).
55. Gan, J., Weng, S. M., Pernia-Andrade, A. J., Csicsvari, J. & Jonas, P. Phase-Loced Inhibition, but Not Excitation, Underlies
Hippocampal ipple Oscillations in Awae Mice In Vi vo. Neuron 93, 308–314, https://doi.org/10.1016/j.neuron.2016.12.018 (2017).
56. Chen, C. M. et al. GABA level, gamma oscillation, and woring memory performance in schizophrenia. Neuroimage Clin 4,
531–539, https://doi.org/10.1016/j.nicl.2014.03.007 (2014).
57. Sharma, S. . et al. Dose response eects of 810 nm laser light on mouse primary cortical neurons. Lasers Surg Med 43, 851–859,
https://doi.org/10.1002/lsm.21100 (2011).
58. Wang, L., Hu, L., Grygorczy, ., Shen, X. & Schwarz, W. Modulation of extracellular ATP content of mast cells and DG neurons
by irradiation: studies on underlying mechanism of low-level-laser therapy. Mediators Inflamm 2015, 630361, https://doi.
org/10.1155/2015/630361 (2015).
59. Jones, E. G. Calcium channels in higher-level brain function. Proc Natl Acad Sci USA 104, 17903–17904, https://doi.org/10.1073/
pnas.0709509104 (2007).
60. Llinas, ., ibary, U., Contreras, D. & Pedroarena, C. e neuronal basis for consciousness. Philos Trans R Soc Lond B Biol Sci 353,
1841–1849, https://doi.org/10.1098/rstb.1998.0336 (1998).
61. Singer, W. Consciousness and the structure of neuronal representations. Philos Trans R Soc Lond B Biol Sci 353, 1829–1840, https://
doi.org/10.1098/rstb.1998.0335 (1998).
62. Hamblin, M. . Mechanisms and applications of the anti-inammatory eects of photobiomodulation. AIMS Biophys 4, 337–361,
https://doi.org/10.3934/biophy.2017.3.337 (2017).
Author Contributions
R.Z., G.L., A.P. and L.L. designed the experiments. R.Z. and G.L. collected and analyzed the data. L.L. determined
the parameters of the device. R.Z., G.L., and L.L. wrote the paper. All authors discussed the results and
implications and commented on the manuscript at all stages.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-42693-x.
Competing Interests: Reza Zomorrodi and Abhi Pushparaj were advisors to the device manufacturer, Vielight
Inc., and were compensated for their time. ey had no other competing or conict of interest, nancial and
non-nancial. Genane Loheswaran is an employee of Vielight Inc. as the Research Manager and Lew Lim is the
Founder & Chief Executive Ocer who owns shares in Vielight Inc., and both have no other competing interest.
All the authors declare that these interests had no inuence on the results and discussion in the study.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-
ative Commons license, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not per-
mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© e Author(s) 2019
Content courtesy of Springer Nature, terms of use apply. Rights reserved
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Available via license: CC BY 4.0
Content may be subject to copyright.
