Effects of somatosensory electrical stimulation on motor function and cortical oscillations
Tu-Chan et al. Journal of NeuroEngineering and Rehabilitation
Effects of somatosensory electrical stimulation on motor function and cortical oscillations
Adelyn P. Tu-Chan 0 1
Nikhilesh Natraj 0 1
Jason Godlove 0 1
Gary Abrams 0 1
Karunesh Ganguly 0 1
0 Department of Neurology, University of California , San Francisco , USA
1 Neurology & Rehabilitation Service, San Francisco VA Medical Center , 1700 Owens Street, San Francisco, California 94158 , USA
Background: Few patients recover full hand dexterity after an acquired brain injury such as stroke. Repetitive somatosensory electrical stimulation (SES) is a promising method to promote recovery of hand function. However, studies using SES have largely focused on gross motor function; it remains unclear if it can modulate distal hand functions such as finger individuation. Objective: The specific goal of this study was to monitor the effects of SES on individuation as well as on cortical oscillations measured using EEG, with the additional goal of identifying neurophysiological biomarkers. Methods: Eight participants with a history of acquired brain injury and distal upper limb motor impairments received a single two-hour session of SES using transcutaneous electrical nerve stimulation. Pre- and postintervention assessments consisted of the Action Research Arm Test (ARAT), finger fractionation, pinch force, and the modified Ashworth scale (MAS), along with resting-state EEG monitoring. Results: SES was associated with significant improvements in ARAT, MAS and finger fractionation. Moreover, SES was associated with a decrease in low frequency (0.9-4 Hz delta) ipsilesional parietomotor EEG power. Interestingly, changes in ipsilesional motor theta (4.8-7.9 Hz) and alpha (8.8-11.7 Hz) power were significantly correlated with finger fractionation improvements when using a multivariate model. Conclusions: We show the positive effects of SES on finger individuation and identify cortical oscillations that may be important electrophysiological biomarkers of individual responsiveness to SES. These biomarkers can be potential targets when customizing SES parameters to individuals with hand dexterity deficits. Trial registration: NCT03176550; retrospectively registered.
Transcutaneous electric nerve stimulation; Stroke; Rehabilitation; Brain injury; Electroencephalography; Upper extremity
Despite recent advances in rehabilitation, a substantial
fraction of stroke patients continue to experience
persistent upper-limb deficits [
]. At best, up to 1 out of 5
patients will recover full arm function, while 50% will
not recover any functional use of the affected arm. [
Improvement in upper limb function specifically
depends on sensorimotor recovery of the paretic hand [
Yet, there remains a lack of effective therapies readily
available to the patient with acquired brain injury for
recovery of hand and finger function; a systematic
review found that conventional repetitive task training
may not be consistently effective for the upper extremity
]. It is thus critical to explore inexpensive and scalable
approaches to restore hand and finger dexterity, reduce
disability and increase participation after stroke and
other acquired brain injuries.
Sensory threshold somatosensory electrical stimulation
(SES) is a promising therapeutic modality for targeting
hand motor recovery [
]. It is known to be a powerful tool
to focally modulate sensorimotor cortices in both healthy
and chronic stroke participants [
]. Devices such as
transcutaneous nerve stimulation (TENS) units can
deliver SES and are commercially available, inexpensive,
low risk, and easily applied in the home setting .
Previous studies have demonstrated short-term and long-term
improvements in hand function after SES [
However, the effect of SES on regaining the ability to
selectively move a given digit independently from other
digits (i.e. finger fractionation) has not been investigated.
Poor finger individualization is an important therapeutic
target because it is commonly present even after
substantial recovery and may account for chronic hand
]. Further, it is unclear if SES is associated
with compensatory or restorative mechanisms. Prior
studies have largely relied on relatively subjective clinical
evaluations of impairment, such as the Fugl-Meyer
Assessment, or timed and task-based assessments, such as
the Jebson-Taylor Hand Function Test. Biomechanical
analyses, on the other hand, can provide important
objective and quantitative evidence of improvement in
neurologic function and normative motor control [
Therefore, we aimed to determine not only the functional
effects, but also the kinematic effects, of SES on chronic
Simultaneously, it should be noted that although SES
can potentially be an effective therapy, not all individuals
who are administered SES experience positive effects.
While improvement levels as high as 31–36% compared
to baseline function have been reported, [
half of one cohort demonstrated minimal or no motor
performance improvement after a single session of SES
. One method to shed more light on this discrepancy
is to identify neurophysiological biomarkers associated
with motor responses to SES. Neurophysiological
biomarkers are increasingly used to predict treatment
]. Although some studies have examined
biomarkers associated with treatment-induced motor
recovery, to our knowledge none have been performed for
]. A recent study using
electroencephalography (EEG) found that changes in patterns of
connectivity predicted motor recovery after stroke [
present, little is known about the effect of peripheral
neuromodulation on EEG activity, how existing neural
dynamics interacts with peripheral stimulation, and
whether this interaction is associated with improvements
in motor function. Associating EEG activity with
treatment response may also provide mechanistic insight
regarding the effects of SES on neural plasticity. EEG
activity can also potentially be used as a cost-effective
real-time metric of the time-varying efficacy of SES.
This novel application of EEG information may help
tailor treatment efforts while reducing the variability
The main goal of this pilot study was to evaluate both
changes in finger fractionation in response to SES and
identify the associated neural biomarkers through
analyses of EEG dynamics. Outcomes from this study
have potential in designing targeted SES therapy based
on neural biomarkers to modulate and improve hand
function after acquired brain injury such as stroke (e.g.
enrollment in long-term studies of the efficacy of SES).
Ethics, consent and permissions
This research was conducted in accordance with and
approval of the University of California San Francisco
Institutional Review Board (IRB). All research participants
provided informed consent to participate in the study.
Inclusion criteria included participants between 18 and
80 years old, with a history of an acquired brain injury
resulting in residual hemiparesis or other motor deficits
of the arm/hand equal to or more than 6 months prior
to enrollment; and capacity to adhere with the schedule
of interventions and evaluations determined in the
protocol. Subjects were excluded if they met any of the
following criteria: currently pregnant; uncontrolled
medical conditions; significant cognitive impairment on the
Montreal Cognitive Assessment (MoCA ≤23); ≤ 10
degrees of active index finger range of motion; significant
hand joint deformity; severe active alcohol or drug
abuse; significant depression (PHQ-9 ≥ 15); baseline
spasticity score (MAS) >3 for any joint tested (wrist and
metacarpophalangeal joint flexion and extension);
apraxia screen of Tulia (AST) <5; absent light touch,
proprioception, pinprick and vibration sensation on the
modified Nottingham Sensory Assessment; no upper
limb strength against gravity; severe aphasia; or had an
implanted pacemaker. The NSA was used for both
exclusionary purposes as well as for reporting the presence
of baseline sensory deficits.
Participant baseline characteristics and clinical
assessments are shown in Table 1. Fourteen individuals were
screened, 9 were enrolled and received the intended
intervention, and 8 completed the study protocol, on
which the final outcome analyses were performed.
Reasons for exclusion of 5 individuals were significant
cognitive impairment (MoCA <23), less than 10 degrees of
active finger range of motion (two people), lack of
residual motor deficits, and active treatment for brain
tumor. One participant was unable to complete the
study protocol due to fatigue.
Clinical and kinematic assessments
The primary outcome measurements consisted of the
standardized Action Research Arm Test (ARAT) and a
kinematic measurement of finger individuation, the
finger coupling index (FCI). Participants performed
TBI traumatic brain injury, UE upper extremity, ARAT Action Research Arm Test, MAS Modified Ashworth Scale, FCI, finger coupling index
multiple repetitions of the ARAT and finger
individuation measurements during one familiarization session
prior to the beginning of the study to address potential
practice effects. The ARAT has been previously validated
and was selected for its ability to measure defined
domains of distal hand function (i.e. proximal, grasp, grip,
and pinch tasks) [
]. Digital video recordings were
obtained for kinematic motion analysis using a 30 Hz video
capture system. Videos files were analyzed using a
custom Matlab script to record beginning positions and end
positions of the required tasks. Virtual markers were
superimposed on top of recorded visual markers adhered
to the participant’s hand. The beginning and end
positions of each task were validated visually by video replay
frame by frame. FCI was measured from frames
exhibiting the maximum difference between the angle traversed
by the passive middle finger divided by the angle
traversed by the active index finger. (Fig. 1a-b). Three trials
were averaged to obtain the mean finger coupling index.
Given frequent rest breaks, participants did not have any
difficulty completing the required number of trials per
task. Trials that were interrupted or failed due to
technical errors were discarded, and an additional set of
trials would be repeated from the beginning. Secondary
outcome measurements included finger pinch force
(standardized dynamometer), and the Modified Ashworth
Scale (MAS) to assess spasticity affecting wrist and finger
flexion and extension. Outcome assessments were
measured immediately before and after the intervention.
Participants wore an EEG cap (Enobio, Neuroelectrics
Corp., Barcelona, Spain) consisting of pre-determined
electrode positions located anatomically according to the
International 10–20 EEG System. Resting state EEG data
with eyes open was acquired (Enobio, Neuroelectrics
Corp., Barcelona, Spain) for a duration of 10 min before
and after stimulation, using 8 electrodes over the Fp1,
Fp2, C3, C4, P3, P4, O1, O2 at 500 Hz with a mastoid
reference. Kinematic and functional outcome
measurements were performed without blinding. Participants
were aware of the research question regarding whether
somatosensory electrical stimulation had any effect on
hand motor function.
TENS was performed using a commercially available
device (ProStim, Alimed Inc., Dedham, Massachusetts,
USA). One pair of 2 × 3.5 in. rectangular electrodes
Fig. 1 a Schematic representation of the method used for calculating
the FCI. The participant is instructed to flex only the index finger as
much as possible without flexing the other digits. b FCI is defined
mathematically as the angle traversed by the middle finger (digit A)
divided by the angle tranversed by the index finger (digit B) relative to
the horizontal starting position. c Statistically significant change in
mean fractionation from baseline to immediately after peripheral nerve
stimulation. Fractionation improvement is indicated by a decrease in
finger coupling index (FCI)
(Vermed ChroniCare TENS Electrodes, Vermed, Buffalo,
NY, USA) were placed on one aspect of the forearm to
simultaneously stimulate both median and ulnar nerves,
while a second pair of round 2 in. diameter electrodes
were placed on the lateral aspect of the forearm to
stimulate the radial nerve. (Additional file 1: Figure S2)
Optimal positions to stimulate the ulnar, median and
radial nerves of the paretic hand were determined by using
standard localization technique [
thresholds (minimum intensity of stimulation) at which
subjects report paresthesias in each nerve territory were
determined. Stimulus intensity was further increased
and adjusted until subjects reported strong paresthesias
in the absence of pain and visible muscle contractions.
The mean stimulation intensity was 5.3 mA (19% above
mean sensory threshold) for the radial nerve and 5.8 mA
(29% above mean sensory threshold) for the median/
ulnar nerves. Bursts of electrical stimulation at 10 Hz
(100 microsecond pulse width duration) were delivered
to all nerves simultaneously for 2 h [
5, 10, 12–15, 18
During the stimulation period, the affected hand was at
rest while participants read or viewed a film.
Experimental data were collected immediately before
and after the intervention. Intervention effects were
determined using non-parametric bootstrap tests to assess
the difference between the pre- and post-intervention
]. Statistical significance was set at p < 0.05.
Continuous 10 min EEG resting state data were epoched
into non-overlapping 1000 ms time-voltage data
segments and mean-baselined, with the “right hemisphere”
as the common lesion hemisphere. In essence, this
involved flipping hemispheric cortical activity for left
hemispheric patients. Artifact correction on the epoched
data was performed using a combination of principle
component analysis (PCA) and the 3 S.D. voltage metric
] to reject epochs that had abnormally large voltage
values due to eye blinks, head-motion or extraneous
noise. Bilateral sensorimotor electrodes (C3-C4 and
P3P4) formed the regions of interest. Resting state power
was computed within each epoch across four frequency
bins (delta 0.9–3.9 Hz, theta 4.8–7.9 Hz, alpha 8.8–
11.7 Hz, beta 12.7–30.27 Hz) via averaging the absolute
values of short time Fourier transforms (STFT) on
nonoverlapping 256 ms snippets within each epoch.
Subsequently, the percentage change in mean resting state
power, pre to post intervention, was computed for each
subject at each frequency bin and electrode. A bootstrap
test was used to assess the null hypothesis of group-level
changes in mean resting state power being similar to a
distribution with mean zero. The percentage change in
mean FCI was regressed onto the percentage change in
mean resting state power across all 4 bins and 4
electrodes via multivariate regression. Given that there
were more predictors (changes across 4 channels X 4
frequency bins = 16 predictors) than measurements
(changes in 8 subjects’ FCI), penalized regression was
performed to counter effects of multicollinearity.
Specifically, we used ridge regression and the ridge parameter
was identified via leave-one-out cross validation [
should be noted that both simple and penalized
regression is susceptible to outliers given that the objective
functional to be minimized is quadratic (least squares
error minimization). Given the low sample size of 8
subjects, rather than reject data we used robust multivariate
regression that automatically corrects for outliers based
on a function of the least squares error. Specifically,
robustness was implemented via an iterative re-weighted
least squares algorithm based on Huber’s weighting
]. A permutation test was used to determine
significance of the ridge coefficients that are associated
with changes in mean resting state power with changes
in mean FCI [
]. Bonferroni corrections for multiple
comparisons were performed wherever appropriate.
Results of kinematic and clinical outcome measurements
are presented in Table 2. Mean scores were significantly
improved after peripheral nerve stimulation for
measures including ARAT total score, overall ARAT
completion time, ARAT pinch tasks subset completion time,
finger coupling index, and MAS. The mean change in
ARAT score was 1.5 points change (or 3.75%
improvement) after one session of SES (p < 0.05). ARAT domain
subsets were further analyzed to determine whether one
specific domain improved or a generalized effect in distal
upper limb function could be observed. Significant
improvement was noted for speed (overall time to
complete all tasks decreased by 1.72 s (13.31% change;
p < 0.05) and pinch tasks time which reduced by 7.26 s
(29% change; p < 0.05). Changes in proximal tasks time,
grasp tasks time, and grip tasks time were not
significant. Finger fractionation significantly improved; FCI
decreased from 0.68 to 0.53 (22% change). Of the
secondary outcome measurements, MAS decreased
significantly by 1.13 points (60% change) amongst those
who had baseline spasticity (p < 0.05), while mean pinch
force increased by 1.22 pounds of force (11.3% change).
Results of resting state EEG analyses are shown in
Fig. 2. At the group level, stimulation caused significant
decreases primarily in mean ipsilesional resting state
power at low frequencies (delta 0.9–3.9 Hz and theta
4.8–7.9 Hz bands, p < 0.05, Bonferroni corrected,
Fig. 2a-b). In contrast, no significant changes were found
for alpha and beta frequencies (Additional file 2: Figure
S1A, B). In addition, combined theta and alpha power
changes over the ipsilesional motor cortex were
significantly correlated with fractionation changes
(p < 0.05) when controlling for all other predictors in
the multivariate robust ridge regression model (Fig. 2c).
The ridge parameter value of 12.13 was obtained via
leave one out cross-validation (Additional file 2: Figure
S1C, D) and visual assessment of the quantile-quantile
plot from the regression showed normally distributed
residuals (Additional file 2: Fig. S1E). It should be noted
that ridge regression shares coefficient values amongst
correlated predictors (theta and alpha are closely related
frequencies) while shrinking coefficients of predictors
not correlated with the response variable.
Our primary results showed that a single two-hour
session of SES resulted in statistically significant
improvements in functional measurements as well as finger
kinematics in individuals with chronic acquired brain
injury. Improvements were found in the domains of
activity (i.e. ARAT) and impairment (i.e. pinch strength,
spasticity, and finger fractionation). A statistically
significant improvement was detected in the mean ARAT
score after only one session of SES. This finding is
broadly consistent with similar studies of the effects of
SES on hand function in stroke patients [
3, 5, 15, 19,
]. One particular study using the ARAT, however, did
not find any change after SES. It was determined to be
largely due to a ceiling effect [
]. For example, their
participants averaged a higher baseline ARAT score than
the participants in the present study. While the change
in ARAT score was small in magnitude, it may be of
clinical relevance; larger or additive effects have been
demonstrated with multiple stimulation sessions and in
combination with motor training [
The relationship between SES and recovery of
individuated finger movements has not been investigated in
previous studies. Past studies mainly focused on
functional measurements as outcomes, such as the
JebsonTaylor Hand Function Test, or on relatively subjective
evaluations of impairment, such as the Fugl-Meyer
Assessment, to determine the efficacy of SES [
5, 10, 15, 19,
]. Combining functional clinical evaluations with
kinematic measurements of finger fractionation is one
strategy we implemented to distinguish between functional
improvements solely related to compensatory changes
versus recovery of impairments. For the purpose of this
study, we defined treatment-induced motor recovery as
a relative improvement in finger fractionation ability
after peripheral nerve stimulation. Our finding here of
normalized finger fractionation kinematics suggests that
SES can modulate the neural control of finger dexterity.
This observation is consistent with a prior study
demonstrating immediate improvement in index finger and
hand tapping frequency after a single 2-h session of SES.
] Interestingly, the ARAT total score improvement
was specifically attributable to improved performance in
pinch tasks rather than performance of grip, grasp, or
proximal tasks. This indicates that SES may have a
highly specific or greater effect on tasks that require
relatively more finger individuation. However, findings
of improvements in peak velocity of the wrist during
reach-to-grasp tasks after SES have also been reported.
] Although the differential effect of SES on the
various aspects of upper limb function needs further
evaluation, the findings taken together underline the
importance of emphasizing recovery of finger dexterity
to facilitate meaningful and measurable functional
The specific mechanism for increased fractionation
ability after SES is unclear. Prior research suggests that
SES affects complex motor skill performance by
reorganization and altered excitability of the sensorimotor
cortex. Neuroanatomical, electrophysiological, and
imaging data revealed that unilateral electrical stimulation,
including SES, can activate the contralateral S1 and S2
]. Direct connections between
Brodmann areas 1 and 2 of S1 and M1, and S2 and M1 could
provide a neuroanatomical basis for the observed effects
]. Furthermore, when patients with pure motor
lacunar strokes have interrupted corticospinal
projections at a subcortical location, the remaining descending
pathways mediating voluntary movement are unable to
produce selective patterns of muscle activation required
for finger individuation tasks. [
] This underlines the
importance of motor cortex output for the orchestration
of individuated finger movements. Studies have shown
no effects on peripheral nerve M-wave and spinal cord
excitability (H waves) with SES, further suggesting that
the changes in excitability most likely occur at the level
of the cortex. [
It has been proposed that finger individuation is a result
of not only the voluntary movement of one digit but also
the inhibition of digits intended to remain stationary [
One study using high frequency SES found a reduction in
motor evoked potential (MEP) from the muscle
stimulated and an increased MEP from the antagonist muscle
]. A more recent study found increased MEP with
supramotor threshold stimulation and reduced MEPs with
]. Although these results cannot be directly
compared to our findings because the stimulation parameters
and conditions were dissimilar, they illustrate the
complexity of the parameter-dependent effects of SES that can
be both facilitatory as well as inhibitory. Therefore, it is
plausible that SES improves motor control during finger
individuation tasks by modulating cortical excitability and
inhibiting inappropriate antagonist and agonist muscle
cocontractions, a hypothesis in need of further exploration.
The plausible neural correlates underlying the proposed
corticomotor excitability changes are addressed in the
following paragraph based on our EEG results.
The EEG results suggest that the observed
improvements in motor kinematics and function after SES may
be primarily related to changes to ipsilesional cortical
oscillations. There were two results detailing the neural
plasticity induced by SES that are suggestive of the
aforementioned link. First, we observed a relative
decrease in ipsilesional resting state low frequency power
primarily in the delta band (and ipsilesional motor theta
band) immediately after SES when compared to the
baseline resting period. Secondarily, a decrease in
ipsilesional motor theta and alpha power (two closely coupled
frequency bands that were pooled together in the
multivariate ridge regression model) were significantly
correlated with fractionation changes with SES. Together, our
results highlight the importance of reductions in
lowfrequency, ipsilesional cortical oscillations in association
with improved behavioral responses to SES. It is thought
that the loss of functional outputs from injured or
damaged neurons in affected brain regions [
] can result
in an increased synchronous ‘idling’ state  of the
cortical pathways as a whole. The increased idling is
recorded at the surface EEG as a pathological increase in
low frequency power [
]. A potential reason as to why
lower frequency oscillations in particular are affected
could be due to the slow oscillatory nature of blood flow
and metabolism in neuronal tissue. [
] In any case,
an increase in low frequency ipsilesional oscillations can
be thought to correspond to increased inhibitory activity
in the underlying neural tissue . Indeed, a recent
study suggested that the reduction of resting-state
lowfrequency cortical oscillations are a predictor of
spontaneous recovery [
]. Here, we show that SES lowers
the aforementioned ipsilesional low-frequency
oscillations with correlated improvements in behavioral
outcomes. Mechanistically, SES could therefore serve to
induce cortical plasticity in ipsilesional neural tissue by
transitioning the affected region from a synchronous
idling state to motor-function related activation. [
While the low frequency power changes observed here
resulted in better motor behavior, further work (e.g.
corticomuscular coherence) is necessary to understand how
these power changes relate to individual components of
agonist and antagonist muscle activity underlying finger
fractionation. Overall, our data provide evidence that
neuromodulatory approaches that further reduce low
frequency oscillations may be critical to improving
motor function. This finding is broadly in line with
changes observed in low frequency dynamics during
recovery from stroke in a rodent model .
Our study also demonstrates how EEG features can be
used as biomarkers of SES-induced recovery. In general,
EEG has been correlated with motor skill acquisition in
healthy individuals and as a biomarker of motor system
function and improvements with physical interventions
in stroke patients [
]. EEG is a safe, inexpensive, and
wearable technology with the potential not only for
objectively stratifying candidates, but also for serving as a
biomarker of responsiveness to treatment in the
outpatient setting. These preliminary findings warrant
further exploration to advance our ability to select
appropriate candidates for longer-term studies of SES and
to customize rehabilitative treatments to individuals.
In summary, we demonstrated the feasibility of using a
wearable EEG system with 8 channels to monitor and
serve as a biomarker of treatment response. However,
using a higher resolution EEG system with a greater
number of channels may be more informative, albeit
more cumbersome to apply. Given the small sample size,
it is unclear whether inhomogeneity of baseline sensory
impairments would impact individual responses to SES.
Investigations into the impact of sensory deficits and
generalizability of findings in a larger patient population
is warranted. Future studies will also need to address
other potential limitations of this pilot study, including
the need for a randomized, controlled study design,
monitoring of long-term effects of SES, varying dosing
and stimulation parameters to determine their effects on
EEG, and explorations into the mechanisms for the
effects of SES on complex motor skills.
A single 2-h session of SES can improve finger
fractionation and hand function in participants with chronic
acquired brain injuries. We also identified cortical
oscillations using EEG that may be important
electrophysiological biomarkers of individual responsiveness to
SES. These biomarkers can be potential targets when
customizing SES parameters to optimize its effects on
individuals with residual hand dexterity deficits.
Additional file 1: Figure S2. (A) Placement of the rectangular
electrodes overlapping the stimulation sites of the median and ulnar
nerves. (B) Placement of the circular electrodes over the stimulation site
of the radial nerve. (TIFF 846 kb)
Additional file 2: Figure S1. Distribution of percentage change in
mean resting state EEG power across the eight subjects, pre to post
intervention, within the A) alpha frequency band and B) beta frequency
band with head plots depicting 1/coefficient of variation (mean/standard
deviation) of group level percentage changes. There were no significant
differences. C) Result from the leave-one-out cross validation (CV)
procedure to find the optimal ridge parameter (lambda) that produced
the lowest CV error given by the vertical dotted red line. D) Ridge trace
plotting the coefficient weights of the multivariate ridge model for
various values of the ridge parameter with the optimal lambda indicated
by the dotted red line. E) Quantile plots from the weighted residuals of
the Huber robust regression. M: electrodes over Motor cortex; P: electrodes
over Parietal cortex. (TIFF 362 kb)
ARAT: Action research arm test; AST: Apraxia screen of Tulia;
EEG: Electroencephalography; FCI: Finger coupling index; MAS: Modified
ashworth scale; MoCA: Montreal cognitive assessment; PCA: Principle
component analysis; SES: Somatosensory electrical stimulation; STFT: Short
time fourier transforms; TENS: Transcutaneous nerve stimulation
This study was supported by a fellowship award from the Department of
Veterans Affairs (A.T). The work was also supported by the Doris Duke
Charitable Foundation (Grant 2,013,101). The research reported in this
publication was also partially supported by the National Institute Of Mental
Health of the National Institutes of Health under Award Number R01MH111871.
The content is solely the responsibility of the authors and does not necessarily
represent the official views of the National Institutes of Health.
Availability of data and materials
The datasets used and/or analysed during the current study are available
from the corresponding author on reasonable request.
AT designed the study with KG and GA, collected and analyzed study data,
and was a major contributor in writing the manuscript. KG, GA and NN were
also major contributors in writing the manuscript. GA, NN and JG
contributed to data analysis and interpretation. All authors read and
approved the final manuscript.
Ethics approval and consent to participate
The University of California San Francisco committee for human research
protection approved the study, and all participants provided written consent.
Consent for publication
KG, NN, and AT have submitted a provisional patent application that is based
partially on the results reported here. The authors declare that they have no
other competing interests to report.
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