Content uploaded by Dongwei Li
Author content
All content in this area was uploaded by Dongwei Li on Dec 03, 2022
Content may be subject to copyright.
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
1 of 12
NEUROPHYSIOLOGY
Transcranial photobiomodulation enhances visual
working memory capacity in humans
Chenguang Zhao1,2,3†, Dongwei Li1,4†, Yuanjun Kong1, Hongyu Liu1, Yiqing Hu1,
Haijing Niu1, Ole Jensen4, Xiaoli Li1,2, Hanli Liu5, Yan Song1*
Transcranial photobiomodulation (tPBM) is a safe and noninvasive intervention that has shown promise for
improving cognitive performance. Whether tPBM can modulate brain activity and thereby enhance working
memory (WM) capacity in humans remains unclear. In this study, we found that 1064-nm tPBM applied to the right
prefrontal cortex (PFC) improves visual working memory capacity and increases occipitoparietal contralateral
delay activity (CDA). The CDA set-size effect during retention mediated the effect between the 1064-nm tPBM and
subsequent WM capacity. The behavioral benefits and the corresponding changes in the CDA set-size effect were
absent with tPBM at a wavelength of 852 nm or with stimulation of the left PFC. Our findings provide converging
evidence that 1064-nm tPBM applied to the right PFC can improve WM capacity.
INTRODUCTION
Working memory (WM), the ability to actively store useful infor-
mation “in mind” over seconds, plays a vital role in many cognitive
functions. Individual differences in WM capacity predict fluid in-
telligence and broad cognitive function (1), which has made
increasing WM capacity become an attractive aim for interventions
and enhancement. In the past decades, noninvasive brain stimula-
tion (NIBS) technology involving transcranial application of elec-
trical (direct or alternating) or magnetic fields to the specific scalp
or multiple brain circuits has been proven to be useful for improving
WM performance. Research using NIBS has found that behavioral
enhancement is associated with neurophysiological changes, in-
cluding increased interregional functional connectivity (2) and
oscillatory neuronal activity (3) as well as changes in event-related
potentials (ERPs) (4).
Transcranial photobiomodulation (tPBM) is a noninvasive light
illumination method that targets the brain at wavelengths between
600 and 1100nm. Recently, tPBM has been applied to modulate
metabolic processes in the brain, and it has emerged as a promising
intervention to improve cognitive functions. It has been suggested
that tPBM up-regulates complex IV of the mitochondrial respiratory
chain to modulate cytochrome c oxidase (CCO). This leads to
increased adenosine triphosphate (ATP) formation and initiates
secondary cell signaling pathways (5–7). The resulting metabolic
effects following PBM increase cerebral metabolic energy produc-
tion, oxygen consumption, and blood flow in animals and humans
(8–11). In addition, several studies suggest that PBM can (i) enhance
neuroprotection by modulating neurotrophic factors and inflam-
matory signaling molecules as well as anti-apoptotic mediators (12),
(ii) activate ion channels (13), and (iii) stimulate transcription
factors that up-regulate the expression levels of genes (14). In an
Alzheimer’s disease (AD) mouse model, tPBM can reduce peri-
vascular microglia and promote angiogenesis to further enhance
A clearance (15). In particular, in the past 2 years, several clinical
studies have provided convincing evidence that tPBM improves
cognition in patients with AD and dementia (16,17) and facilitates
treatment for other neurological disorders (18,19).
Regarding WM, Rojas etal. (20) demonstrated that tPBM could
improve prefrontal cortex (PFC) oxygen consumption and meta-
bolic energy, thereby increasing PFC-based memory functions in
rats. Other studies have shown that 1072-nm tPBM can reverse WM
deficits in middle-aged mice (21). These animal findings suggest
that the oxygen metabolism of cortical tissue exposed to tPBM is
enhanced and that this can explain the improved memory. Two
human behavioral studies have shown that 1064-nm tPBM over the
right PFC can improve accuracy and speed up reaction time in WM
tasks (22,23). Meanwhile, other behavioral studies suggested that
high-order cognitive functions, such as sustained attention and
emotion (22), as well as executive functions (24), could also be
improved after tPBM therapy.
However, even the simplest WM task involves multiple cogni-
tive processes, such as perceptual encoding, selective attention, and
motor execution, which might confound the associations between
the tPBM effect and WM enhancement. Taking this into account,
we chose the K values estimates to assess the accurate number of
items maintained in the visual WM for the given load array (25).
Given that the right PFC is associated with information mainte-
nance in WM (26), we hypothesized that 1064-nm tPBM over the
right PFC (Fig.1A) leads to enhancements in visual WM capacity.
However, we still lack WM-related neurological evidence to directly
bridge the gap between tPBM effects and WM behavioral benefits.
Previous studies have extensively demonstrated that contralateral
delay activity (CDA) tracks the number of objects stored in visual
WM. Furthermore, the set-size effects of the CDA (defined as the
increase in amplitudes from set-size two to set-size four) predicted
the individual differences in WM capacity (27). Thus, we linked
behavioral benefits (K values) in WM capacity from tPBM with ERP
biomarkers (CDA) of WM capacity. As the research on tPBM as a
potential tool for effective NIBS is in its early stages, several key
questions exist. For example, a good understanding of tPBM-evoked
electrophysiological effects in the human brain is lacking.
1State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern
Institute for Brain Research, Beijing Normal University, Beijing, China. 2Center for
Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience
and Learning, Beijing Normal University at Zhuhai, Guangdong, China. 3School of
Systems Science, Beijing Normal University, Beijing, China. 4Centre for Human
Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
5Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA.
*Corresponding author. Email: songyan@bnu.edu.cn
†These authors contributed equally to this work.
Copyright © 2022
The Authors, some
rights reserved;
exclusive licensee
American Association
for the Advancement
of Science. No claim to
original U.S. Government
Works. Distributed
under a Creative
Commons Attribution
License 4.0 (CC BY).
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
2 of 12
We conducted four double-blind, sham-controlled tPBM experi-
ments (Fig.1A), in which participants completed two different
sessions of tPBM separated by a week, with sham or active tPBM on
the PFC (Fig.1B). After stimulation, participants performed a
classical change detection task in which WM load was manipulated
(high versus low load; Fig.1C), while the electroencephalography
(EEG) was recorded. The classical change detection task required
participants to maintain the features of items (orientation for ex-
periment 1; color for experiment 2) at the cued side in WM for
subsequent recognition, which reliably induces sustained CDA
components. Then, we performed a series of follow-up experiments
that explored the specificity of tPBM in terms of wavelengths
(experiment 3) and stimulation site (experiment 4) on the enhance-
ment of WM capacity.
RESULTS
1064-nm tPBM on the right PFC enhanced individual
WM capacity
Two classic change detection tasks were implemented to assess WM
performance, which required the participants to remember the orien-
tations (experiment 1) or color (experiment 2) of a set of items in
the cued hemifield (see Fig.1C). We calculated the visual memory
capacity using a standard formula (27) that essentially assumes that
if an observer can hold in memory K items from an array of S items,
then the item that changed should be one of the items being held in
memory on K/S trials, leading to correct performance on K/S of the
trials in which an item changed. The formula for memory capacity
is K=S × (H − F), where S is the size of the presented array, H is the
observed hit rate, and F is the false alarm rate. We evaluated the
Probe
(maximum 2000 ms)
Delay
(900 ms)
Memory
(100 ms)
Cue
(200 ms)
Low load
High load
High load
Low load
Exp.
1
Orientation task
Exp.2
Color task
0
0.4
0.8
0
3.5
Sham Active
Kvalue
tPBM effect on K value
0
0.4
0.8
tPBM effect on K value
0
3
Sham Active
K value
C Working memory tasks
A tPBM protocol B Experimental protocol
D Behavioral results
**
*
0% 100%
50%
HitUncertain Miss
Chance level
48.1% 43.3%
8.6%
Day 1
Day 7
Day 8
Counterbalanced
Active tPBM
WM task
Sham tPBM
WM task
Report
Sham
Active
12
0.5 11.5
P(w)
t (min)
2
2
Fig. 1. Protocol, task, and behavioral results in experiments 1 and 2. (A) tPBM protocol. Active tPBM was delivered by a laser with 1064 nm to the right PFC for a total
of 12 min. (B) Experimental protocol. Each participant received two tPBM sessions (active and sham, randomized, and double-blinded design) separated by 1 week. On
the eighth day, participants were required to report or guess which session involved active or sham tPBM. (C) WM tasks. In experiment 1, the participants were required
to perform an orientation WM task. In experiment 2, the participant was required to perform a color WM task. Two tasks used the same relative timing and protocol, and
the only difference between the two tasks was the memory dimension (orientation in experiment 1 and color in experiment 2). Each participant only participated in one
experiment. (D) Left: Performance in terms of K values for orientation WM task (up) and color WM task (down) under sham tPBM (blue circles) and active tPBM (red circles).
Right: The tPBM effect on the K values (active minus sham). The dots indicate individual performance. *P < 0.05 and **P < 0.01.
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
3 of 12
WM capacity according to the K values under the high-load condi-
tion. A two-way mixed-effect analysis of variance (ANOVA) with
tPBM stimulation (active and sham; within-subjects) and tasks
(orientation and color; between-subjects) as factors was conducted
with K values as the dependent variable. The results revealed a sig-
nificant main effect of tPBM stimulation (F1,40=13.436, P<0.001,
P
2=0.925) but no significant interaction between tPBM stimula-
tion and task (F1,40=0.080, P=0.779, P
2=0.006). Specifically,
compared with sham tPBM, the K values increased after 1064-nm
tPBM both in the orientation WM task (experiment 1: t22= 2.841
and P=0.009, two-tailed) and in the color WM task (experiment 2:
t17=2.760 and P=0.013, two-tailed). The mean tPBM effect (active
minus sham) on the K values for experiment 1 was 0.186±0.065
(BF10=5.212 and Cohen’s d=0.568), and the mean tPBM effect on
K values for experiment 2 was 0.188±0.051 (BF10 =20.336 and
Cohen’s d=0.651). These results support the hypothesis that 1064-nm
tPBM on the right PFC enhances WM capacity.
Several studies investigating neuro-enhancement using transcranial
direct current stimulation (tDCS) have shown improved WM with
stimulation but mainly for individuals with low WM capacity
(4,28). To examine this effect with respect to tPBM, we divided
participants into two subgroups based on their K values in the
orientation WM task during sham tPBM stimulation in experiment 1
(n=11 and n=12 for low- and high-performance groups, respec-
tively). A two-way mixed-effects ANOVA showed no significant
interaction between tPBM stimulation and subgroup (F1,22=1.170,
P=0.291, P
2=0.110). Therefore, participants with both good and
poor WM capacity improved after 1064-nm tPBM. A similar analysis
involving the color WM task also showed no performance- dependent
effects in experiment 2 (F1,17=0.002, P=0.963, P2<0.001).
Our results in experiments 1 and 2 demonstrate that participants
could maintain more items in visual WM with external 1064-nm tPBM
stimulation on the right PFC. These effects were independent of prior W M
ability for both orientation and color. Participants could not report
or guess whether they were assigned to the sham or active tPBM group.
Subjects guessed at chance level (see Fig.1B, hit rate=48.1%), suggesting
that they had no awareness of whether they received active tPBM.
CDA tracks the enhancement in individual WM capacity
The EEG signals were recorded while participants performed the
WM tasks. Consistent with previous studies (27), the ERP results
show a negative deflection at contralateral relative to ipsilateral
scalp sites at PO7 and PO8 (see the Supplementary Materials). We
defined the CDA set-size effect as the CDA amplitude with respect
to the maintenance of S=2 objects (low load) minus the amplitude
of maintenance of S= 4 objects (high load). Note that we did not
track the “raw” CDA amplitude but rather used the increase in
CDA amplitude from low to high WM load as the dependent vari-
able. To investigate the effect of tPBM on the CDA set-size effect, a
two-way mixed ANOVA on the CDA set-size effect was conducted
considering the WM task (orientation and color) and tPBM stimu-
lation (active and sham) as factors. As expected, the results showed
a significant main effect of tPBM stimulation (F1,40 =12.249,
P = 0.001, P2 = 0.227). The main effect of task (F1,40 =0.660,
P=0.421, P2=0.012) and the tPBM stimulation × task interaction
(F1,40=0.474, P=0.495, P2=0.011) was not significant. Follow-up
t tests indicated that the CDA set-size effect during the delay period
was significantly stronger in the active tPBM session than in the
sham tPBM session for both the orientation WM task (t22=2.313,
P=0.030, Cohen’s d=0.463, two-tailed) and color WM task
(t17=2.506, P=0.023, Cohen’s d=0.591, two-tailed). Source esti-
mates using standardized low resolution electromagnetic tomog-
raphy (sLORETA; see Materials and Methods) of the CDA set-size
effect are shown in Fig.2. These results demonstrated that the in-
crease in CDA set-size effects (active minus sham) was local-
ized in the superior intraparietal sulcus (IPS) for two WM tasks
with 1064-nm tPBM applied over the right PFC (Ps<0.05).
To better understand the tPBM effect, we show the K values and
CDA set-size effects for the different stimulation sessions with respect
to the orientation and color WM tasks (Fig.2C). Although the K values
and CDA set-size effects were evaluated as separate dimensions
involving WM, as can be seen here, the changes in K values and CDA
across the two tasks have similar trends as when compared active
tPBM to the sham tPBM. These results are consistent with the
hypothesis that both WM improvement and CDA increases are asso-
ciated with the tPBM effect. As expected, the CDA set-size effect
from the two experiments shows a task-independent tPBM effect.
Next, we tested whether the EEG data provided evidence of the
beneficial tPBM effect. We performed Pearson correlation analyses
at the subject level to provide more detailed information on the
relationships between CDA and behavior. As shown in Fig.2D, the
differences in CDA set-size effects between active and sham sessions
were correlated positively with the behavioral differences between
these sessions (for orientation task: r=0.446 and P<0.040; for
color task: r=0.563 and P<0.020). The results suggest that, for both
color and orientation WM tasks, the tPBM-associated changes in CDA
set-size effects can predict tPBM-associated behavioral benefits.
CDA mediates the WM improvement with tPBM (1064 nm)
applied to the right PFC
Given the above notable relationship between the increase in
CDA set-size effect and the increase in K values after 1064-nm
tPBM, relative to sham sessions, we performed a mediation analysis
(see Materials and Methods) to examine whether the effect of tPBM
on WM capacity (reflected by K values) was mediated by the CDA
set-size effect. Therefore, we considered the tPBM sessions (active
versus sham) as predictors, WM performance (K values) as the pre-
dicted variable, and CDA set-size effect as a mediator (see Fig. 3A).
This mediation analysis revealed a significant indirect effect of the CDA
set-size effect (indirect effect: 0.300, 95% confidence interval: −0.107
to 0.706, P=0.146; direct effect: 0.245, 95% confidence interval:
0.021 to 0.844, P= 0.040). The mediation analysis demonstrated the
indirect effect of 1064-nm tPBM on the behavioral K values through an
increase in the amount of information maintained in visual WM, as
reflected by increases in the CDA set-size effect. We further correlated
the CDA set-size effect with the K values across all sessions in experi-
ment 1 and experiment 2. The correlation analysis showed that the
change in the CDA set-size effect was strongly correlated with the
change in the behavioral K values (r=0.404, P<0.001, confidence
interval: 0.154 to 0.654; Fig.3B), highlighting a robust relationship
between the CDA set-size effect and WM improvement. This result
is consistent with previous research (29) that CDA is indicative of
the number of maintained objects in visual WM.
The wavelength specificity of tPBM on the
enhancement of WM capacity
In experiment 3, we investigated whether the changes in neural
activity and behavior were specific to the wavelength of tPBM. An
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
4 of 12
approximate wavelength of 850nm has been used in near-infrared
light for testing WM improvements due to its absorption maxima
of CCO (9). This motivated us to test tPBM by using 852-nm laser
light over the right PFC (Fig.4A). We also sought to disambiguate
the effects of tPBM and tissue heating. We hypothesized that if
tPBM on the enhancement of WM capacity is not wavelength
specific, but temperature dependent, then we should expect to find
about the same enhancement in K values and CDA set-size effect
with 852-nm tPBM and 1064-nm tPBM. We used the same power
and stimulation duration for 1064-nm tPBM and 852-nm tPBM to
ensure that they produced the same quantity of heat. First, we found
that 852-nm tPBM did not change the participants’ behavioral K
values for the orientation WM task compared with sham tPBM
(t18=0. 381, P=0.707, Cohen’s d=0.085, two-tailed; Fig.4B). The
mean tPBM effect (active minus sham) on the K values for experi-
ment 3 was −0.029± 0.088 (BF10=0.244 and Cohen’s d=0.085).
We compared the data between the 852-nm tPBM in experiment 3
and the 1064-nm tPBM in experiment 1 (Fig.4D). A two-way
mixed-effect ANOVA on K values further revealed a significant
tPBM stimulation (active versus sham) and wavelength (1064nm
versus 852 nm) interaction (F1,41= 4.474, P=0.041, P2=0.095),
indicating that WM performance improved significantly only in
0.0
0.1
0.2
0.3
0.4
0.5
0.6
*
PO7/PO8
0.6
0.30–0.3
–0.6
0.3
0.2
0.1
0
–0.1
–0.2
∆Behavioral K value
(Active - sham)
Orientation r = 0.446 P < 0.040
Color r = 0.563 P < 0.020
∆CDA set-size effect
Active - sham
tPBM effects (1064 nm, right PFC)
Exp.1
Orientation task
Exp.2
Color task
K value
CDA set-size effect (µV)
Sham Active Active
Sham
PO7/PO8
Active vs. sham Active vs. sham
–1
1000
–1
1000
1000
–1
–1
1000 Sham Active
Low loadHigh load
Set-size effect µV
Set-size effect µV
CDA µV CDA µV
Time (ms)Time (ms)
Sham Active
Color taskOrientation task
0.0
0.1
0.2
0.3
0.4
0.5
0.6
**
5
–5 –5 5
t value
t value
Period of memory
AB
CD
Fig. 2. Grand average of ERPs and its link with behavior. (A) The orientation WM task in experiment 1 and (B) the color WM task in experiment 2. Shading indicates the
CDA set-size effect. The enlarged black dots on EEG topographies show PO7/PO8 electrodes. Bar plots represent the average CDA set-size effect. Error bars represent
SEM. Significant set-size effects are located in the IPS. Three-dimensional (3D) brain map (t-map; posterior view) of significant tPBM effect on CDA. (C) tPBM-effect. K values
and CDA set-size effect for the two tasks (orientation WM task and color WM task) and two sessions (active tPBM and sham tPBM). (D) Scatterplots of participants’
behavioral benefits (active minus sham) against the changes in the CDA set-size effect (active minus sham) for the orientation WM task (gray) and the color WM task
(black). *P < 0.05 and **P < 0.01.
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
5 of 12
active tPBM sessions applying 1064nm but not an 852nm. Similarly,
852-nm tPBM did not modulate the CDA set-size compared with sham
tPBM for the orientation WM task (t18=0. 129, P=0.899, Cohen’s
d=0.030, two-tailed; Fig.4C). A two-way mixed-model ANOVA on
CDA amplitudes further revealed a marginally significant tPBM stimu-
lation (active versus sham) and wavelength (1064nm versus 852 nm)
interaction (F1,41=3.623, P=0.064, P2=0.080). These results suggest
that the tPBM effect on WM is specific to the 1064-nm wavelength.
We conclude that the behavioral and electrophysiological findings
with 1064-nm tPBM are not explained by heating. This conclusion
should be interpreted with caution due to the marginal significance
observed in our electrophysiological results. Subjects also guessed
at the chance level (hit rate=47.8%), suggesting that they had no
awareness of the 852-nm tPBM over the right PFC.
Active vs. sham
CDA
set-size effect
Behavioral
K value
c = 0.433*
a = 0.370* b = 0.404**
c' = 0.300
4
20–2
–4
2
1
0
–1
–3
–2
Behavioral K value
(
Z
score)
(Z score)
r = 0.404 P < 0.00
1
CDA set-size effect
AB
Fig. 3. The CDA set-size effect mediated behavioral K values by 1064-nm tPBM. (A) Mediation model demonstrating the effect of 1064-nm tPBM on improved
K values via increases in the CDA set-size effect. a, b, and c denote standardized beta coefficients of the direct path strength. c′ denotes the beta coefficient of path
strength after controlling for changes in the CDA set-size effect. (B) Scatterplots of the behavioral K values (Z score) and the CDA set-size effect (Z sore) across all participants
(active and sham session) in experiment 1 and experiment 2. *P < 0.05 and **P < 0.01.
Visible light Infrared light
= 1064 nm
= 852 nm
0
3.5
Sham Active
K value
0
0.8
–0.8
tPBM effect on K value
AB
C
Behavioral results (852 nm, orientation task)
EEG (852 nm, orientation task) tPBM effect (right PFC, orientation task)
D
Stimulation protocol (852 nm vs. 1064 nm )
Sham
Active
Low load
High load
Time (ms)
Time (ms)
CDA (µV)CDA (µV)
1000
–1
–1
1000
Active vs. sham
0.00
0.12
0.24
0.36
0.48
0.60
Set-size effect µV
Sham Active
K value
CDA set-size effect (µV)
0.4 Sham Active Active
Sham
Exp.1
1064 nm
Exp.3
852 nm
5–5 t
value
Fig. 4. Results in experiment 3. (A) Stimulation protocol. Active tPBM was delivered by laser light with wavelength 852 nm over the right PFC in experiment 3 (black sine
wave) or wavelength 1064 nm in experiments 1, 2, and 4 (gray sine wave). (B) The K values for tPBM stimulation (active and sham) for the orientation WM task in experiment
3. The circles indicate individual performance. (C) Grand average of ERPs for active 852-nm and sham tPBM sessions. The shading indicates the CDA set-size effect. Bar
plots represent the average CDA set-size effect: blue, sham session; red, active session. Error bars represent SEM. 3D brain map (t-map) posterior view of the significant
tPBM effect on CDA. (D) K values and CDA set-size effect for the orientation WM task in experiment 3 (852-nm tPBM) and experiment 1 (1064-nm tPBM).
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
6 of 12
1064-nm tPBM on the left PFC did not enhance WM capacity
In experiment 4, we asked whether 1064-nm tPBM applied to the
left PFC could enhance WM capacity (Fig.5A). The task in experi-
ment 4 is the same orientation WM task as experiment 1. Figure4
shows that compared with sham tPBM, 1064-nm tPBM on the left
PFC did not enhance the K values (t18=0. 381, P=0.707, Cohen’s
d=0.085, two-tailed; Fig.5B) nor the corresponding CDA set-size
effect (t18=0. 129, P=0.899, Cohen’s d=0.030, two-tailed; Fig.5C).
The mean tPBM effect (active minus sham) on the K values for ex-
periment 4 was −0.032±0.058 (BF10=0.258 and Cohen’s d=0.030).
We compared the data between tPBM applied on the left PFC in
experiment 4 and tPBM applied on the right PFC in experiment 1
(Fig. 5D). A two-way mixed-model ANOVA revealed significant
interactions between tPBM stimulation (active versus sham) and
location (left versus right) on both K values (F1,41=4.474, P=0.041,
P2=0.095) and on the CDA set-size effect (F1,41=2.623, P=0.044,
P2=0.098). These findings demonstrate that WM performance
improved only when 1064-nm tPBM was applied to the right but
not to the left PFC. Subjects also guessed at the chance level (hit
rate=46.9%) whether they received sham versus tPBM, suggesting
that they had no awareness of the tPBM over the left PFC.
Time course of the enhancement in WM after tPBM
To investigate the behavioral enhancement across blocks, we calcu-
lated the K values of high-load conditions across the four blocks in
all four experiments (Fig.6). The paired t test showed that, relative
to sham sessions, significant behavioral enhancements were found
only during the late period in experiment 1 (block 3: t22=3.840,
P < 0.001, Cohen’s d = 0.768, two-tailed; block 4: t22 =2.155,
P=0.041, Cohen’s d=0.504, two-tailed) and experiment 2 (block 3:
t17=2.137, P=0.047, Cohen’s d=0.431, two-tailed) with 1064-nm
tPBM on the right PFC but not in other blocks or experiments
(Ps>0.250). We further compared the K values in block 3 across
experiment 1 and experiment 3 when applying tPBM over the right
PFC, 1064-nm stimulation relative to 852nm enhanced the K values
(t40=2.795, P=0.008, Cohen’s d=0.838, two-tailed). When apply-
ing tPBM at 1064-nm wavelength over the right PFC relative to the
left PFC, the K values were also enhanced (t40=1.959, P=0.056,
Cohen’s d= 0.580, two-tailed). No significant differences were
found in other blocks (Ps>0.351).
No evidence for event-related oscillatory activity after tPBM
We further analyzed the effects of tPBM on event-related de-
synchronization (ERD) or event-related synchronization (ERS) during
the retention of WM. As shown in fig. S1, we found that active
tPBM might amplify the set-size effect of theta ERS in experiment 1
(t22=2.487, P=0.048, Cohen’s d =0.403, two-tailed). This effect
was not found in experiments 2, 3, and 4 (Ps>0.256). We further
performed a correlation analysis to determine whether the set-size
effect of prefrontal theta power was correlated with behavioral bene-
fits on WM capacity in experiment 1. No significant correlation was
found between theta ERS and changed in K values (r= 0.232 and
P=0.351). As shown in figs. S2 and S3, no significant differences
in the set-size effect of alpha or beta ERD between the two tPBM
0
0.8
–0.8
tPBM effect on K value
0
4
Sham Active
K value
AB
C
Behavioral results (left, orientation task)
EEG (left, orientation task) tPBM effect (1064 nm, orientation task)
D
Stimulation protocol (left PFC)
Sham
Active
Low load
High load
Time (ms)
Time (ms)
CDA (µV)
CDA (µV)
1000
–1
–1
1000
–5 5
Active vs. sham
t value
Set-size effect µV
0.4
Sham Active
K value
Set-size effect (µV)
Sham Active Active
Sham
Exp.4
Left PFC
Exp.1
Right PFC
Fig. 5. Results in experiment 4. (A) Stimulation protocol of experiment 4. Active tPBM was delivered by the laser with wavelength 1064 nm at the left PFC for a total of
12 min. (B) K values for tPBM stimulation (active and sham) were applied on the left PFC in experiment 4. Each circle indicates individual performance. (C) Grand average
of ERPs for the 1064-nm and sham tPBM sessions in experiment 4. Shading indicates the CDA set-size effect. Bar plots represent the average CDA set-size effect: blue,
sham session; red, active session. Error bars represent SEM, 3D brain map (t-map) posterior view of a significant tPBM effect on CDA. (D) K values and CDA set-size effect
for the orientation WM task in experiment 4 (tPBM stimulation applied on the left PFC) and in experiment 1 (tPBM stimulation applied on the right PFC).
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
7 of 12
sessions were found in each experiment (Ps>0.188). No significant
relationships were found between the changes in the set-size effect
of alpha (or beta) ERD and behavioral benefits (Ps>0.150). The
ERD or ERS modulations were also not linked to the changes in
the CDA set-size effect with stimulation. The reason might be as
follows: (i) although both oscillatory activities and CDA are related
to WM capacity (27,30), some studies further identified that they
reflect dissociable processes of WM (31,32); (ii) poor statistical
power for the oscillatory analysis explained by a lower signal-to-
noise ratio. Recent findings proposed that cross-frequency coupling
(e.g., phase- amplitude coupling) and long-range connections be-
tween distant cortical areas (e.g., phase locking value) might be
related to network mechanisms supporting WM maintenance (2,3).
Further work is needed to examine the tPBM effect on network
changes during WM.
DISCUSSION
Across four complementary EEG experiments, we provided the
converging evidence that 1064-nm tPBM applied to the right PFC
could improve visual WM capacity. In the first two experiments
(experiments 1 and 2), the K values were enhanced for WM for
orientation and color when 1064-nm tPBM was applied to the right
PFC. Crucially, we found that the WM memory improvements
were tracked by individual changes in CDA set-size effects. A
mediation analysis revealed that the behavioral enhancements with
tPBM were mediated by CDA set-size effects. Further studies demon-
strated that effects on the capacity enhancement of visual WM were
absent for tPBM applied at 852-nm tPBM (experiment 3) and to the
left PFC (experiment 4).
Improvement of CDA-identified WM capacity by 1064-nm
tPBM on the right PFC
There has been recent excitement about tPBM as a safe, noninvasive,
and simple modality for neuromodulation. In particular, it seems to
be a promising tool for enhancing WM capacity in humans because
several studies have shown notable improvements in tPBM-treated
versus control groups as measured by behavioral responses in WM
tasks (22,23). Our results from experiments 1 and 2 do not only
complement those findings, but also the design and outcome of
these two experiments add novel insight.
First, WM involves multiple cognitive processes, including
perceptual encoding, selective attention, and motor execution. More
specific measures of WM capacity (e.g., K values and CDA set-size
effect) bring us closer to identifying specific enhancements by
tPBM. Thus, we designed an EEG study to investigate the post-tPBM
effects on the CDA reflecting the individual visual WM capacity.
Because the CDA set-size effect reflects the number of objects
online-held in visual WM (29), our ERP results help to establish
that 1064-nm tPBM on the right PFC boosts visual WM capacity. It
also corroborates and extends existing findings that active mainte-
nance of visual information in the occipitoparietal cortex could be
boosted by enhancing the contribution of the right PFC in visual
WM maintenance (33). We further established neurophysiological
links between 1064-nm tPBM and subsequent WM capacity, by
demonstrating that the CDA during the retention served as a media-
tor. Our findings suggest that increased WM from 1064-nm tPBM
might stem from the right PFC stronger engaging parietal areas as
reflected by the increased CDA set-size effect. However, given that
both hemodynamic effects (34) and EEG activity (35) from the IPS
are correlated with WM capacity, we could not pinpoint whether
the CDA set-size effect plays a causal role in enhancing WM capacity
or whether it is a byproduct of hemodynamic activity. Given that
the frontoparietal network (FPN), including the supplementary
motor area, PFC, and IPS are thought to be important for WM (36),
we inferred that the 1064-nm tPBM might increase metabolism
(e.g., providing more ATP) in the right PFC with positive benefits
for the FPN network.
Second, EEG signals in response to tPBM stimulation have been
reported recently from resting brain data, and they were analyzed to
identify frequency-specific changes in EEG power (16,37,38). Our
study demonstrates that WM-specific ERPs also are modulated by
tPBM stimulation. This contribution is important for researchers
working in the field of tPBM who wish to understand the underlying
electrophysiological effects of tPBM.
Possible changes in the brain network by 1064-nm tPBM
in the right PFC
The LORETA results showed an effect of ERPs from the IPS when
the stimulation was applied over the right PFC. There are several
explanations for this observation. A recent study (38) reported that
1064-nm tPBM on the right PFC enables notable increases in al-
pha power in several brain networks at rest (i.e., the default mode
network, executive control network, frontal-parietal network, and
A
B
1.8
2
2.2
2.4
1064 nm
Right PFC
Sham
*
K value
K value
Orientation task
Color task
1.6
1.8
2
2.2
Block 1 Block 2Block 3Block 4
Block 1 Block 2 Block 3 Block 4
Sham 1064 nm
Right PFC
852 nm
Right PFC
1064 nm
Left PFC
** *
**
*
Fig. 6. Time course of the behavioral results. (A) K values across the four blocks
in the orientation WM task in experiments 1, 3, and 4. The blue line represents the
sham session in experiment 1. (B) K values across the four blocks in the color WM
task in experiment 2. The blue line represents the sham session in experiment 2.
*P < 0.05 and **P < 0.01.
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
8 of 12
lateral visual network). Another independent set of studies from the
same group revealed that 1064-nm tPBM on the right PFC increased
hemodynamic activities across the entire cortical region and en-
hanced topographical functional connectivity between the right
PFC stimulation site and parietal regions (5,39). Together, these
studies show that 1064-nm tPBM on the right PFC of the resting
human brain modulates both regional-specific activity and func-
tional connectivity as observed in EEG and hemodynamic data. While
these results were derived from the intervention administered during
the resting state, it is reasonable to expect that tPBM would also alter
the task-related activity between the right PFC and the parietal regions
as we here report. We posit that the CDA set-size effect that we report is
related to connectivity changes between the right PFC stimulated by
tPBM and the IPS generating the CDA. In support, it has been sug-
gested that external interventions can result in increased functional
connectivity between the PFC and IPS during WM operations (3,40).
The tPBM applied in this study is likely to result in ch anges and en-
hancement in metabolism and hemodynamics (41,42). It has been
suggested that the neurovascular coupling between hemodynamic
activity and EEG is suggested to play a role in visual information
processing (43,44). Thus, further EEG-functional near-infrared
spectroscopy (fNIRS)/functional magnetic resonance imaging (fMRI)
studies would help to gain a better understanding of the underlying
mechanism of the beneficial effects resulting from tPBM.
Wavelength-dependent effect of right PFC tPBM on
visual WM capacity
Wavelength is a major illumination parameter of tPBM within an
“optical window” in the red–to–near-infrared optical region, as it
greatly determines the photon absorption of molecular target CCO
(10). Literature reviews of tPBM have exhibited a wide range of
wavelengths applied in both animals and humans (8–11). The most
common wavelengths used are in the range of 600 to 900 nm, par-
ticularly at 660, 810, and 850nm (45). The reason applying those
wavelengths in PBM or tPBM is because CCO has light absorption
peaks at 660 and 800 to 850nm (46). Thus, these specific wave-
lengths might promote metabolic effects. On the other hand, 1064-nm
laser stimulation has demonstrated effects on the enhancement of
human cognition (22,23), as also reported here. However, it is
unclear whether tPBM by other wavelengths from the laser would
create similar effects on CCO activity and vascular hemodynamics.
Such insight would be exceptionally helpful to researchers, clinicians,
and potential manufacturers in the PBM field. A better understand-
ing of the wavelength-specific tPBM effects on mitochondrial and
hemodynamic activities in the human brain would facilitate the
optimal selection of wavelengths to optimize the outcomes. Specifi-
cally, it is also crucial to further uncover if tPBM-induced behavioral
and CDA enhancements in visual WM capacity are wavelength
dependent.
Accordingly, we designed experiment 3 by switching only
the laser wavelength from 1064 to 852nm and thus to investigate
wavelength-specific effect in WM. The ideal laser light of tPBM
should have the theoretical advantage of traveling deeper into the
tissues of the human body and the best absorption of light by
CCO. In reality, there is a trade-off between the absorption of light
by CCO and the depth of penetration. In the absorption spectra, the
photon absorption peak of CCO is close to 852nm. However, light
at this wavelength is more scattered preventing light from travelling
deeply through tissue. In comparison, the longer 1064-nm wavelength
allows for deeper tissue penetration but less absorption of light by
CCO. Here, we used tPBM with 1064nm (good penetration) and
852nm (good absorption) to find the optimal wavelength for
tPBM. We found that tPBM with 1064nm specifically boosted the
behavioral performance of the participants and the CDA set-size
effect in the WM task. Such behavioral and EEG modulations were
not observed for tPBM at 852 nm. These results suggest that
1064nm is a better wavelength for tPBM with photon delivery into
the PFC due to its reduced tissue scattering (42). Note that tPBM
with 852nm had the same laser energy and the same stimulation
duration as tPBM with 1064nm and thus resulted in comparative
putative heating. It can therefore also be considered as an active-
controlled group to eliminate the exogenous thermal effect that
would bias or confound the observed changes. To our knowledge,
these results provide the first evidence of wavelength-specific WM
capacity improvement by tPBM. Meanwhile, uncertainty remains
about the photobiomodulation physiological mechanism with
respect to the choice of wavelength. Further research is needed to
determine how variation in illumination parameters, such as power
density, wavelength, treatment timing, and pulse structure, would
affect the memory-enhancing effects of tPBM.
Site-dependent effect of 1064-nm tPBM on visual
WM capacity
To examine site-dependent effect, we designed experiment 4 by
switching the stimulation site from the right PFC to the left PFC
while keeping the rest intervention parameters the same. The results
showed that the contribution of tPBM to improving WM capacity
is specific to the right PFC. It supports the notion that the right
PFC is more closely associated with information maintenance in
visuo-spatial WM (26). Given previous work showing that WM
capacity could be modulated by increasing the PFC excitability (47),
we here offer a new effective intervention to enhance visual WM
capacity and provide new evidence for a causal role of right PFC for
WM maintenance. In the past decade, other NIBS technologies
such as tDCS have been shown to enhance WM performance by
increasing cortical excitability. Our null effect of left PFC stimula-
tion with tPBM in experiment 4 is consistent with the observation
that applying anodal tDCS over the left PFC does not improve visual
WM capacity (48). However, other studies have shown that applying
anodal tDCS over the left PFC could improve the behavioral perform-
ance of verbal WM (49–51). Although both the left and right PFC
might have general beneficial effects from NIBS technology, as the
“central executive” is an important unit in the classic storage-
and- processing mode of WM maintenance (52), a recent review
noted out the distinct neural mechanisms between visual WM and
verbal WM (53).
Benefits of tPBM and potential applications
Our findings also showed differences between tPBM and other
NIBS technologies. Unlike the WM capacity–dependent changes
after tDCS (28,54), our results showed WM improvements in
participants with both low and high WM capacity after 1064-nm
tPBM. We suggest that tPBM is a useful tool for improving the
upper limits of WM by augmenting the neural metabolism of the
PFC. Recent studies have attempted to improve WM performance by
modulating brain activity through within-trial rhythmic entrainment
using alternating transcranial current stimulation (2) and repetitive
magnetic stimulation (3). These studies showed that these NIBS
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
9 of 12
technologies can bring the peak and online benefit of behavior gains
by modulating temporally neuronal activities. However, participants
subjected to 1064-nm tPBM performed better after the first two
blocks than those who received the sham, 852-nm, or left tPBM. It
seems that tPBM produces behavioral improvements after several
minutes of stimulation but not immediately. These observations
might stem from tPBM, which required the involvement of multi-
process of brain activity, unlike that tDCS induces the change of the
underlying cortex by causing the neuron’s resting membrane po-
tentials to depolarize or hyperpolarize. Consistently, previous research
on tPBM yielded an optimal impact on tasks when administered
over several minutes (55). Note that this effect decreased in
block 4, and it seems that 1064-nm tPBM has limited sustained
effects. The limitations of the present studies include the relatively
short follow-up period. Further study should be conducted to ex-
plore the time course of the behavioral effects of the stimulation. To
our knowledge, the reported literature demonstrates that behav-
ioral benefits of tPBM almost all applied stimulation to sites on
the forehead. Because of the absorption of light by the hair, other
areas such as the IPS may receive limited effects if being stimulated
directly by tPBM (45). Further work should break the barrier be-
tween hair and laser light to expand the applicability of tPBM to
the whole brain.
In conclusion, our study provides novel and compelling evidence
that 1064-nm tPBM applied to the right PFC enhances visual WM
capacity in humans. Considering that several disorders, such as
attention-deficit hyperactivity disorder (ADHD), schizophrenia,
and AD, show a decline in WM capacity (56–58), our observations
offers a safe, effective, cost-effective, and noninvasive brain inter-
vention tool for clinical applications. To date, there are no side
effects or harm associated with tPBM reported in the literature.
In addition, compared with other NIBS technologies that may pro-
duce a certain tingling sensation or acoustic noise, tPBM is silent.
Thus, tPBM is suitable for promoting clinical applications in indi-
viduals with memory dysfunction, such as patients with ADHD and
AD. However, tPBM will depend on stimulation parameters, such
as the power density, wavelength, dosage, and location. Further
research work is needed from biophysical and neurobiological
aspects to use the full potential of tPBM before it can be applied to
improve cognition in healthy and clinical populations.
MATERIALS AND METHODS
Participants
Neurologically normal college students (n=90) with normal or
corrected-to-normal vision participated in four experiments. Of
these, 27 participated in experiment 1 (five males, mean age=22). No
statistical methods were used to predetermine the sample size, but
the sample size was chosen to be adequate to obtain robust re-
sults as determined by preliminary experiments. Because the
identified tPBM effect in experiment 1 was robust, we set the sample
size to 21in experiments 2 to 4 (experiment 2: seven males, age
range=22.8±3.8; experiment 3: eight males, age range=22.7±4.1;
experiment 4: seven males, age range=22.8±4.0). Data from
11 participants (four in experiment 1, three in experiment 2, two in
experiment 3, and two in experiment 4) were excluded because of
incomplete data or low EEG quality. The Institutional Review Board
approved the experimental procedures of Beijing Normal University,
and informed consent was obtained from each participant.
Experimental protocol
Each participant only participated in one of four experiments. Each
experiment consisted of one active tPBM session and one sham
tPBM session completed on the first and the seventh day. The order
of the sessions was counterbalanced across participants (see Fig.1B).
On the eighth day, participants were required to report (or guess)
which session was the active tPBM session. Before EEG recordings,
all subjects participated in a training block to ensure that they could
perform the tasks above the chance level, and we checked for poten-
tial EEG artifacts.
tPBM protocol
Four experiments were administered using a diode-pumped solid-
state laser with a linewidth of ±1nm (Model JL-LS-100 developed
by Jieliang Medical Device Inc., Jiangxi, China). The measured uni-
form laser beam has an area of 13.57cm2 (4cm in diameter) and a
continuous power output of 2271 mW, resulting in an irradiance or
power density of 167 mW/cm2 (2271 mW/13.57cm2=167 mW/cm2).
At this power level, the energy emitted by the laser is one-fifth of the
skin’s maximum permissible exposure (the exposure not deemed
harmful to tissue and causing no detectable physical damage or im-
perceptible heat). The stimulation was handheld. The stimulation
site in our experiment was centered on the FP2 electrodes (experi-
ments 1, 2, and 3) or the FP1 electrode (experiment 4) based on the
10-20 system used for EEG electrode placement (Fig.1A, top). Each
subject was instructed to sit on a chair, which was adjusted to en-
sure comfort throughout the measurement. The ambient lighting of
the room was eliminated to ensure that it did not contaminate the
laser light. Participants were instructed to wear protective eyewear
and keep their eyes closed, as required by the laser manufacturer
and the Beijing Normal University Laser Safety Program. In the
active tPBM session, the area stimulated (a 60 s/cycle, total laser
energy per cycle=2.271 W ×60 s= 136.26 J/cycle) alternated
between sites medial and lateral to the FP2 or FP1 for 12min before
EEG recording. The sham tPBM session received two brief 0.5-min
stimulations (at the beginning and end of the 12-min period) to the
intended site on the forehead, separated by 11min of no stimula-
tion (the laser power was tuned down to 0 W; Fig.1A, bottom).
Thus, the sham tPBM session received approximately 1/12th of the
cumulative energy density compared to the active session. This
0.5-min treatment was a necessary part of the active placebo session
by providing a similar subjective experience to the active tPBM
session without producing physiological or cognitive effects (22,59).
For tPBM stimulation, 1064nm was used for experiments 1, 2, and
4; 852nm was used for experiment 3. The two wavelengths were
controlled to release equal optical energy. Each session lasted 45 to
60min (12-min tPBM stimulation, 8-min rest, and 25- to 30-min
EEG recording). The active and sham sessions were separated by at
least 1 week to avoid overlapping effects.
WM task
The stimuli were presented on a 48 × 27 cm2 liquid crystal display
monitor (1200×768 pixels, 120-Hz refresh rate) with a homogeneous
light gray background (12 cd/m2; Red Green Blue (RGB): 125, 125,
and 125) at a distance of 65cm. At the beginning of each trial, a
200-ms central arrow cue instructed the participants to remember
the left or the right hemifield objects. Next, the memory array was
presented for 100 ms, followed by a 900-ms interval. Then, the probe
array was presented for a maximum of 2000 ms or until response.
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
10 of 12
Participants were instructed to respond as quickly and accurately
as possible whether the orientation or color of the objects in the
cued hemifield had changed after the WM delay.
In the orientation WM task, all memory arrays were presented
within two 4°×7.3° rectangular regions that were centered 3° to the
left and right of a black central fixation cross (0.5 cd/m2, 0.4°×0.4°).
Each memory array consisted of two or four red bars (2° in length
and 0.5° in width) in each hemifield selected with a random orien-
tation between 0° and 180°, with the constraint that the orientations
among bars within a hemifield differed by at least 20°. The bars’
positions were randomized in each trial, with the constraint that the
distance between the bars within a hemifield was at least 2° (center
to center). The orientation of one bar in the probe array was different
from the corresponding object in the memory array in 50% of the
trials in each hemifield; the orientations of two arrays were identical
in the remaining trials. In the color WM task, each memory array
consisted of two or four colored squares (1°×1°) in each hemifield.
Each square was selected randomly from a set of nine colors (red,
green, blue, yellow, violet, pink, orange, black, and white). In the
low-load condition, one square was presented in each quadrant. In
the high-load condition, two squares were presented in each quad-
rant. In the probe array, the color of one square was different from
the corresponding object in the memory array in 50% of the trials in
each hemifield; the two arrays’ colors were identical in the remain-
ing trials. Each session involved eight blocks (four low-load blocks
and four high-load blocks, randomized across blocks). Each block
contained 60 trials, and an approximately ~1-min break separated
adjacent blocks. In total, we collected 960 trials within ~30min in
each experiment per participant. Experiments 3 and 4 were identical
to the same orientation WM task in experiment 1, except that the partici-
pants were assigned to active tPBM with different wavelengths (852 nm)
in experiment 3 and with another site (left PFC) in experiment 4.
EEG recording and analysis
The EEG signals were recorded while the participants performed
the tasks. The EEG data were acquired using a SynAmps EEG am-
plifier and the Curry 8.0 package (NeuroScan Inc.) from a Quick-cap
with 64 silver chloride electrodes arranged according to the inter-
national 10-20 system. To detect eye movements and blinks, vertical
eye movements were recorded from two vertical electrooculogram
electrodes placed 1cm above and below the left eye, while horizontal
eye movements were recorded from two horizontal electrooculogram
electrodes placed at the outer canthus of each eye. All electrodes,
except those for monitoring eye movements, were referenced online
to the left mastoid. Electrode impedance was kept below 5 kilohm.
The EEG was amplified at 0.01 to 200Hz and digitized online at a
sampling rate of 500Hz.
The data were processed in MATLAB (MathWorks Inc., Natick,
MA) using the ERPLAB toolbox and custom codes. They were
preprocessed by applying a 0.1- to 40-Hz bandpass filter and re-
referencing data offline to the average of all electrodes. The EEG data
were then segmented relative to memory array onset (from −200 to
1000 ms). Independent component analysis was performed to correct
eye-blink artifacts by semiautomatic routines for the segmented data.
Epochs were automatically excluded if the EEG exceeded ±100 V
at any electrode or if the horizontal Electro-Oculogram (EOG)
exceeded ±30 V from 0 to 500 ms around cue array onset. Epochs
that continued to show artifacts after this process were subsequently
detected and removed by the eye. Data from nine participants (three
in experiment 1, two in experiment 2, two in experiment 3, and two
in experiment 4) were discarded because of the high ratio of excluded
trials (>40% of trials). Among the participants’ final set, an average
of 12.3% of trials per participant (range, 0.4 to 27.6%) were rejected
because of artifacts.
We focused on the ERP triggered by the memory array. The
baseline correction was calculated for 200 ms before memory display
onset in each trial. The trials were then averaged for each condition
to create the ERP response. Contralateral waveforms were computed
by averaging the right electrode sites for trials on which to-be-
remembered objects occurred on the left side with the left electrode
sites for trials on which to-be-remembered objects occurred on the
right side. Ipsilateral waveforms were computed by averaging the
right electrode sites for trials on which to-be-remembered objects
occurred on the right side with the left electrode sites for trials on
which to-be-remembered objects occurred on the left side.
The CDA was measured at the posterior parietal sites (PO7/
PO8) as the difference in mean amplitude between the ipsilateral
and contralateral waveforms, with a measurement window of 500 to
1000 ms after the onset of the cue array. In this study, the memory
display could induce an N2pc before the CDA component (32). To
obtain a pure CDA measure without contamination of the N2pc
component, we began the CDA measurement period at 500 ms, by
which time the N2pc had ordinarily terminated. Note that the contra-
lateral waveform of the target was the average of the left-hemisphere
electrodes when the target was in the right visual field and the
right-hemisphere electrodes when the target was in the left visual
field. Similarly, the ipsilateral waveform for the target was the average
of the left-hemisphere electrodes when the target was in the left
visual field and the right-hemisphere electrodes when the target was
in the right visual field.
During ERP analysis in the visual WM task, we detected the
difference in CDA between groups in active and sham sessions with
t statistics analysis. For each comparison, a test was calculated for
time samples in ERP components with 5000 random permutations.
For the ERS or ERD analysis, artifact-free trials were decomposed
using a Morlet wavelet–based analysis from 4 to 30Hz in 1-Hz steps
implemented in the related package Fieldtrip in the MATLAB envi-
ronment. We subtracted the trial-average activity in the time do-
main from the EEG activity of every single trial to avoid the time
frequency of power being disturbed by the ERP in oscillatory signals.
Load-dependent power changes in theta (4 to 7 Hz), alpha (8 to
12 Hz), and beta (14 to 30 Hz) during retention were calculated to
examine the WM-related oscillatory activity between sham and
active tPBM sessions. Changes with respect to a baseline over time
were referred to as ERS or ERD. The percentage of ERS or ERD was
calculated by following the procedure reported elsewhere (30): (i)
the mean baseline (−400 to 0 ms before memory display onset)
amplitude was subtracted from the power estimates at each time
point, and (ii) the difference was divided by the mean baseline. Our
electrodes of interest were occipitoparietal electrodes (P3/4, PO3/4,
O1/2, PO7/8, P7/8, and POz) and middle frontal electrodes (FP1/2,
AF1/2, F3/4, F1/2, and FZ) based on previous findings (30,60). The
set-size effects were defined as the change in amplitudes from
set-size two to set-size four conditions.
Source locations
The three-dimensional (3D) distribution of the tPBM effect (active
minus sham) in electrical activity was analyzed for each subject
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
11 of 12
using LORETA software (61). Localization of the CDA set-size
effect between the active and the sham session was assessed by
voxel-by-voxel paired t tests of the LORETA estimates. To correct
for multiple comparisons, a nonparametric permutation test was
applied (P<0.050, determined by 5000 randomizations). Last, the
significant result values were projected on a 3D brain model.
Statistical analysis
Mediation analysis for multilevel data was performed in the SPSS
statistics package (version 20.0). Two models were built in the analysis:
(i) a linear model to test the relationship between the tPBM session
and the behavioral K values and (ii) a generalized linear model
established with “active versus sham” as the predictors, “CDA set-size
effect” as the mediator, and “behavioral K value” as the predicted vari abl e.
The direct and indirect effects were then obtained by contrasting
these two models. The null hypothesis was tested by examining
whether zero was within the 95% bootstrap confidence intervals.
We conducted a Bayesian analysis (conducted with JASP soft-
ware v.0.13.1.0) to test the null hypothesis. Bayes factor analyses
with default priors (r=0.707) were performed on the EEG data
(BF10>1: support for H1 over H0; BF10<0.333: substantial evidence
for the null hypothesis).
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at https://science.org/doi/10.1126/
sciadv.abq3211
View/request a protocol for this paper from Bio-protocol.
REFERENCES AND NOTES
1. S. M. Jaeggi, M. Buschkuehl, J. Jonides, W. J. Perrig, Improving fluid intelligence
with training on working memory. Proc. Natl. Acad. Sci. U.S.A. 105, 6829–6833 (2008).
2. R. M. G. Reinhart, J. A. Nguyen, Working memory revived in older adults by synchronizing
rhythmic brain circuits. Nat. Neurosci. 22, 820–827 (2019).
3. P. Albouy, A. Weiss, S. Baillet, R. J. Zatorre, Selective entrainment of theta oscillations
in the dorsal stream causally enhances auditory working memory performance. Neuron
94, 193–206.e5 (2017).
4. P. Tseng, T. Y. Hsu, C. F. Chang, O. J. Tzeng, D. L. Hung, N. G. Muggleton, V. Walsh,
W. K. Liang, S. K. Cheng, C. H. Juan, Unleashing potential: Transcranial direct current
stimulation over the right posterior parietal cortex improves change detection
in low-performing individuals. J. Neurosci. 32, 10554–10561 (2012).
5. E. L. Urquhart, H. Wanniarachchi, X. Wang, F. Gonzalez-Lima, G. Alexandrakis, H. Liu,
Transcranial photobiomodulation-induced changes in human brain functional
connectivity and network metrics mapped by whole-head functional near-infrared
spectroscopy in vivo. Biomed. Opt. Express 11, 5783–5799 (2020).
6. F. Tian, S. N. Hase, F. Gonzalez-Lima, H. Liu, Transcranial laser stimulation improves
human cerebral oxygenation. Lasers Surg. Med. 48, 343–349 (2016).
7. F. Salehpour, J. Mahmoudi, F. Kamari, S. Sadigh-Eteghad, S. H. Rasta, M. R. Hamblin,
Brain photobiomodulation therapy: A narrative review. Mol. Neurobiol. 55, 6601–6636
(2018).
8. Y. Lampl, J. A. Zivin, M. Fisher, R. Lew, L. Welin, B. Dahlof, P. Borenstein, B. Andersson,
J. Perez, C. Caparo, S. Ilic, U. Oron, Infrared laser therapy for ischemic stroke: A new
treatment strategy: Results of the NeuroThera effectiveness and safety trial-1 (NEST-1).
Stroke 38, 1843–1849 (2007).
9. H. Nawashiro, K. Wada, K. Nakai, S. Sato, Focal increase in cerebral blood flow after
treatment with near-infrared light to the forehead in a patient in a persistent vegetative
state. Photomed. Laser Surg. 30, 231–233 (2012).
10. J. C. Rojas, F. Gonzalez-Lima, Neurological and psychological applications of transcranial
lasers and LEDs. Biochem. Pharmacol. 86, 447–457 (2013).
11. C. L. Saucedo, E. C. Courtois, Z. S. Wade, M. N. Kelley, N. Kheradbin, D. W. Barrett,
F. Gonzalez-Lima, Transcranial laser stimulation: Mitochondrial and cerebrovascular
effects in younger and older healthy adults. Brain Stimul. 14, 440–449 (2021).
12. H. I. Lee, J. H. Park, M. Y. Park, N. G. Kim, K. J. Park, B. T. Choi, S. Yong-II, H. K. Shin,
Pre-conditioning with transcranial low-level light therapy reduces neuroinflammation
and protects blood-brain barrier after focal cerebral ischemia in mice. Restor. Neurol.
Neurosci. 34, 201–214 (2016).
13. T. Karu, Primary and secondary mechanisms of action of visible to near-IR radiation
on cells. J. Photochem. Photobiol. B 49, 1–17 (1999).
14. J. Liang, L. Liu, D. Xing, Photobiomodulation by low-power laser irradiation attenuates
A-induced cell apoptosis through the Akt/GSK3/-catenin pathway. Free Radic. Biol.
Med. 53, 1459–1467 (2012).
15. L. Tao, Q. Liu, F. Zhang, Y. Fu, X. Zhu, X. Weng, H. Han, Y. Huang, Y. Suo, L. Chen, X. Gao,
X. Wei, Microglia modulation with 1070-nm light attenuates A burden and cognitive
impairment in Alzheimer’s disease mouse model. Light Sci. Appl. 10, 1–14 (2021).
16. M. H. Berman, J. P. Halper, T. W. Nichols, H. Jarrett, A. Lundy, J. H. Huang,
Photobiomodulation with near infrared light helmet in a pilot, placebo controlled clinical
trial in dementia patients testing memory and cognition. J. Neurol. Neurosci. 8, 176 (2017).
17. D. Nizamutdinov, X. Qi, M. H. Berman, G. Dougal, S. Dayawansa, E. Wu, S. S. Yi,
A. B. Stevens, J. H. Huang, Transcranial near infrared light stimulations improve cognition
in patients with dementia. Aging Dis. 12, 954–963 (2021).
18. P. Cassano, S. R. Petrie, M. R. Hamblin, T. A. Henderson, D. V. Iosifescu, Review of tra nscranial
photobiomodulation for major depressive disorder: Targeting brain metabolism,
inflammation, oxidative stress, and neurogenesis. Neurophotonics 3, 031404 (2016).
19. M. A. Caldieraro, P. Cassano, Transcranial and systemic photobiomodulation for major
depressive disorder: A systematic review of efficacy, tolerability and biological
mechanisms. J. Affect. Disord. 243, 262–273 (2019).
20. J. C. Rojas, A. K. Bruchey, F. Gonzalez-Lima, Low-level light therapy improves cortical
metabolic capacity and memory retention. J. Alzheimers Dis. 32, 741–752 (2012).
21. S. Michalikova, A. Ennaceur, R. van Rensburg, P. L. Chazot, Emotional responses and
memory performance of middle-aged CD1 mice in a 3D maze: Effects of low infrared light.
Neurobiol. Learn. Mem. 89, 480–488 (2008).
22. D. W. Barrett, F. Gonzalez-Lima, Transcranial infrared laser stimulation produces
beneficial cognitive and emotional effects in humans. Neuroscience 230, 13–23 (2013).
23. E. Vargas, D. W. Barrett, C. L. Saucedo, L. D. Huang, J. A. Abraham, H. Tanaka, A. P. Haley,
F. Gonzalez-Lima, Beneficial neurocognitive effects of transcranial laser in older adults.
Laser. Med. Sci. 32, 1153–1162 (2017).
24. N. J. Blanco, W. T. Maddox, F. Gonzalez-Lima, Improving executive function using
transcranial infrared laser stimulation. J. Neuropsychol. 11, 14–25 (2017).
25. N. Cowan, The magical number 4 in short-term memory: A reconsideration of mental
storage capacity. Behav. Brain Sci. 24, 87–114 (2001).
26. E. Volle, J. B. Pochon, S. Lehericy, B. Pillon, B. Dubois, R. Levy, Specific cerebral networks
for maintenance and response organization within working memory as evidenced by
the 'double delay/double response' paradigm. Cereb. Cortex 15, 1064–1074 (2005).
27. E. K. Vogel, M. G. Machizawa, Neural activity predicts individual differences in visual
working memory capacity. Nature 428, 748–751 (2004).
28. T. Y. Hsu, P. Tseng, W. K. Liang, S. K. Cheng, C. H. Juan, Transcranial direct current
stimulation over right posterior parietal cortex changes prestimulus alpha oscillation
in visual short-term memory task. Neuroimage 98, 306–313 (2014).
29. R. Luria, H. Balaban, E. Awh, E. K. Vogel, The contralateral delay activity as a neural
measure of visual working memory. Neurosci. Biobehav. Rev. 62, 100–108 (2016).
30. K. Fukuda, I. Mance, E. K. Vogel, power modulation and event-related slow wave
provide dissociable correlates of visual working memory. J. Neurosci. 35, 14009–14016
(2015).
31. K. Fukuda, M. S. Kang, G. F. Woodman, Distinct neural mechanisms for spatially lateralized
and spatially global visual working memory representations. J. Neurophysiol. 116,
1715–1727 (2016).
32. D. Li, C. Zhao, J. Guo, Y. Kong, H. Li, B. Du, Y. Ding, Y. Song, Visual working memory guides
spatial attention: Evidence from alpha oscillations and sustained potentials.
Neuropsychologia 151, 107719 (2021).
33. F. Edin, T. Klingberg, P. Johansson, F. McNab, J. Tegnér, A. Compte, Mechanism
for top-down control of working memory capacity. Proc. Natl. Acad. Sci. U.S.A. 106,
6802–6807 (2009).
34. J. J. Todd, R. Marois, Capacity limit of visual short-term memory in human posterior
parietal cortex. Nature 428, 751–754 (2004).
35. E. K. Vogel, A. W. McCollough, M. G. Machizawa, Neural measures reveal individual
differences in controlling access to working memory. Nature 438, 500–503 (2005).
36. T. P. Zanto, A. Gazzaley, Fronto-parietal network: Flexible hub of cognitive control.
Trends Cogn. Sci. 17, 602–603 (2013).
37. X. Wang, H. Wanniarachchi, A. Wu, F. Gonzalez-Lima, H. Liu, Transcranial
photobiomodulation and thermal stimulation induce distinct topographies of EEG alpha
and beta power changes in healthy humans. Sci. Rep. 11, 18917 (2021).
38. X. Wang, H. Wanniarachchi, A. Wu, H. Liu, Combination of group singular value
decomposition and eLORETA identifies human EEG networks and responses
to transcranial photobiomodulation. Front. Hum. Neurosci. 16, 853909 (2022).
39. N. C. D. Truong, X. Wang, H. Wanniarachchi, H. Liu, Enhancement of frequency-specific
hemodynamic power and functional connectivity by transcranial photobiomodulation
in healthy humans. Front. Neurosci. 16, 896502 (2022).
Downloaded from https://www.science.org on December 02, 2022
Zhao et al., Sci. Adv. 8, eabq3211 (2022) 2 December 2022
SCIENCE ADVANCES | RESEARCH ARTICLE
12 of 12
40. I. Alekseichuk, Z. Turi, G. A. De Lara, A. Antal, W. Paulus, Spatial working memory
in humans depends on theta and high gamma synchronization in the prefrontal cortex.
Curr. Biol. 26, 1513–1521 (2016).
41. X. Wang, L. C. Ma, S. Shahdadian, A. Wu, N. C. D. Truong, H. Liu, Metabolic connectivity
and hemodynamic-metabolic coherence of human prefrontal cortex at rest and post
photobiomodulation assessed by dual-channel broadband NIRS. Metabolites 12, 42
(2022).
42. T. Pruitt, X. Wang, A. Wu, E. Kallioniemi, M. M. Husain, H. Liu, Transcranial photobi omodulat ion
(tPBM) with 1,064-nm laser to improve cerebral metabolism of the human brain in vivo.
Lasers Surg. Med. 52, 807–813 (2020).
43. C. Zhao, J. Guo, D. Li, Y. Tao, Y. Ding, H. Liu, Y. Song, Anticipatory alpha oscillation
predicts attentional selection and hemodynamic response. Hum. Brain Mapp. 40,
3606–3619 (2019).
44. C. Zhao, D. Li, J. Guo, B. Li, Y. Kong, Y. Hu, B. Du, Y. Ding, X. Li, H. Liu, Y. Song, The
neurovascular couplings between electrophysiological and hemodynamic activities
in anticipatory selective attention. Cereb. Cortex , bhab525 (2022).
45. A. R. Brunoni, M. A. Vanderhasselt, Working memory improvement with non-invasive
brain stimulation of the dorsolateral prefrontal cortex: A systematic review and meta-
analysis. Brain. Cognition 86, 1–9 (2014).
46. M. Hamblin, Y. Huang, Ed., Photobiomodulation in the Brain: Low-Level Laser (Light)
Therapy in Neurology and Neuroscience. (Elsevier Academic Press, Cambridge, MA, 2019).
47. T. Habekost, E. Rostrup, Visual attention capacity after right hemisphere lesions.
Neuropsychologia 45, 1474–1488 (2007).
48. S. Wang, S. Itthipuripat, Y. Ku, Electrical stimulation over human posterior parietal cortex
selectively enhances the capacity of visual short-term memory. J. Neurosci. 39, 528–536
(2019).
49. F. Fregni, P. S. Boggio, M. Nitsche, F. Bermpohl, A. Antal, E. Feredoes, M. A. Marcolin,
S. P. Rigonatti, M. T. A. S. W. Paulus, A. Pascual-Leone, Anodal transcranial direct current
stimulation of prefrontal cortex enhances working memory. Exp. Brain Res. 166, 23–30 (2005).
50. S. H. Ohn, C. I. Park, W. K. Yoo, M. H. Ko, K. P. Choi, G. M. Kim, Y. T. Lee, Y. H. Kim,
Time-dependent effect of transcranial direct current stimulation on the enhancement
of working memory. Neuroreport 19, 43–47 (2008).
51. S. C. Andrews, K. E. Hoy, P. G. Enticott, Z. J. Daskalakis, P. B. Fitzgerald, Improving working
memory: The effect of combining cognitive activity and anodal transcranial direct current
stimulation to the left dorsolateral prefrontal cortex. Brain Stimul. 4, 84–89 (2011).
52. G. J. Hitch, Short-term memory for spatial and temporal information. Q. J. Exp. Psychol. 26,
503–513 (1974).
53. F. van Ede, Mnemonic and attentional roles for states of attenuated alpha oscillations
in perceptual working memory: A review. Eur. J. Neurosci. 48, 2509–2515 (2018).
54. S. Assecondi, H. Rong, G. Eskes, X. Pan, J. Zhou, K. Shapiro, Impact of tDCS on working
memory training is enhanced by strategy instructions in individuals with low working
memory capacity. Sci. Rep. 11, 1–11 (2021).
55. N. J. Blanco, C. L. Saucedo, F. Gonzalez-Lima, Transcranial infrared laser stimulation
improves rule-based, but not information-integration, category learning in humans.
Neurobiol. Learn. Mem. 139, 69–75 (2017).
56. X. Luo, J. Guo, L. Liu, X. Zhao, D. Li, H. Li, Q. Zhao, Y. Wang, Q. Qian, Y. Wang, Y. Song,
L. Sun, The neural correlations of spatial attention and working memory deficits in adults
with ADHD. Neuroimage Clin. 22, 101728 (2019).
57. D. Li, X. Zhang, Y. Kong, W. Yin, K. Jiang, X. Guo, X. Dong, L. Fu, G. Zhao, H. Gao, J. Li,
J. Zhai, Z. Su, Y. Song, M. Chen, Lack of neural load modulation explains attention
and working memory deficits in first-episode schizophrenia. Clin. Neurophysiol. 136,
206–218 (2022).
58. A. M. Kirova, R. B. Bays, S. Lagalwar, Working memory and executive function decline
across normal aging, mild cognitive impairment, and Alzheimer’s disease. Biomed. Res.
Int. 2015, 748212 (2015).
59. X. Wang, F. Tian, D. D. Reddy, S. S. Nalawade, D. W. Barrett, F. Gonzalez-Lima, H. Liu,
Up-regulation of cerebral cytochrome-c-oxidase and hemodynamics by transcranial
infrared laser stimulation: A broadband near-infrared spectroscopy study. J. Cereb. Blood
Flow Metab. 37, 3789–3802 (2017).
60. W. Liang, P. Tseng, J. Yeh, N. E. Huang, C. Juan, Frontoparietal beta amplitude modulation
and its interareal cross-frequency coupling in visual working memory. Neuroscience 460,
69–87 (2021).
61. R. D. Pascual-Marqui, D. Lehmann, T. Koenig, K. Kochi, M. C. Merlo, D. Hell, M. Koukkou,
Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute,
neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Res. 90, 169–179
(1999).
Acknowledgments: We thank all of the participants who participated in the study. Funding:
The present research was supported by the National Natural Science Foundation of China no.
31871099 and no. 61761166003 (to Y.S.), the Ministry of Science and Technology of the People’s
Republic of China no. 2021ZD0204300 (to X.L. and C.Z.), and no. 2022ZD0211300 (to C.Z.), and
the National Defense Basic Scientific Research Program of China 2018110B011 (to Y.S.). Author
contributions: Conceptualization: C.Z., D.L., H.L.L., and Y.S. Methodology: C.Z., D.L., O.J., H.L.L.,
Y.S. Investigation: C.Z., D.L., Y.K., H.Y.L., and Y.H. Visualization: C.Z. and D.L. Funding acquisition:
Y.S., C.Z., and X.L. Supervision: H.N., O.J, X.L., H.L.L., and Y.S. Writing–original draft: C.Z., D.L.,
and Y.S. Writing—review and editing: C.Z., D.L., H.N., O.J., X.L., H.L.L., and Y.S. Competing
interests: The authors declare that they have no competing interests. Data and materials
availability: All data and study analytic codes needed to evaluate the conclusions in the
paper are available in the paper and on OSF: https://doi.org/10.17605/OSF.IO/67YUS.
Submitted 31 March 2022
Accepted 18 October 2022
Published 2 December 2022
10.1126/sciadv.abq3211
Downloaded from https://www.science.org on December 02, 2022
Use of this article is subject to the Terms of service
Science Advances (ISSN ) is published by the American Association for the Advancement of Science. 1200 New York Avenue NW,
Washington, DC 20005. The title Science Advances is a registered trademark of AAAS.
Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim
to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
Transcranial photobiomodulation enhances visual working memory capacity in
humans
Chenguang ZhaoDongwei LiYuanjun KongHongyu LiuYiqing HuHaijing NiuOle JensenXiaoli LiHanli LiuYan Song
Sci. Adv., 8 (48), eabq3211. • DOI: 10.1126/sciadv.abq3211
View the article online
https://www.science.org/doi/10.1126/sciadv.abq3211
Permissions
https://www.science.org/help/reprints-and-permissions
Downloaded from https://www.science.org on December 02, 2022
