Enhancing Acupuncture Treatment Effect Through Non-invasive Neuromodulation
Phase: NA
Conditions: Chronic Pain
Sponsor: Massachusetts General Hospital

Enhancing Acupuncture Treatment Effect Through Non-invasive 
Neuromodulation 
NCT number: NCT03716830 
Date: 12/1/2023 

Study Protocol and Statistical Analysis Plan 
Study protocol 
We conducted a randomized clinical study at Massachusetts General Hospital (MGH), 
enrolling chronic low back pain patients and assessing the clinical and brain imaging 
outcome measures. The study involved treatment with real or sham acupuncture and real 
or sham tDCS using a 2 x 2 design. 
Participants 
Participants enrolled in the study were included if they had been experiencing chronic low 
back pain and had at least 4/10 clinical pain on the 11-point Low Back Pain Numeric Rating 
Scale (LBP-NRS). More details are presented in the supplementary material.  
Randomization 
Participants were randomized to four groups: real acupuncture plus real tDCS, real 
acupuncture plus sham tDCS, sham acupuncture plus real tDCS, and sham acupuncture 
plus sham tDCS. VGAIT-LBP study: Participants were randomized to either the AB-VGAIT or 
the AB-VGITT group. The randomization, designed by a biostatistician, was maintained with 
all keys secured in an excel ﬁle.  
Blinding/Masking 
In both studies, the principal investigator, outcome evaluators, and statisticians were 
blinded to the group assignments. Also, both tDCS and acupuncture assignments were 
blind to participants, though acupuncturists were not.  
Intervention 
Real acupuncture plus real tDCS group: Participants received acupuncture (about 30 
minutes) at Yaoyangguan (GV3), bilateral Shenshu (BL23), Weizhong (BL40) and Taixi (KI3), 
and 1-3 Ashi acupoints based on palpatory tenderness22,23 (Figure 1). Concurrently, real 
tDCS was applied for 20 minutes to bilateral primary motor cortices (see supplement for 
details). 
Real acupuncture plus sham tDCS group: The acupuncture procedure was identical to the 
real acupuncture plus real tDCS group. For sham tDCS, current was applied for 15 seconds 
at the beginning and end of the 20-minute session to mimic real tDCS stimulation. 
Sham acupuncture plus real tDCS group: Sham acupuncture used validated Streitberger 
non-penetrating needles24 placed on non-acupoints (Figure 1), mimicking the same 

manipulation of real acupuncture. The real tDCS procedure was administered as described 
above. 
Sham acupuncture plus sham tDCS group:Both sham acupuncture and sham tDCS 
procedures followed the same protocols as described in their respective combinations 
above. 
The total treatment period was four weeks, with two sessions per week for the ﬁrst two 
weeks and one session per week for the last two weeks. Participants reported their 
sensations using the MGH acupuncture sensation scale (MASS) after the treatment. 
Statistical plan  
General plan: For all outcomes in this proposal, we will (1) compare resting state functional 
connectivity (rsFC), cerebral blood ﬂow (CBF), and clinical assessment differences within 
each group using paired t-tests, and between treatment groups using Analysis of 
Covariance (ANCOVA); and (2) apply multiple regression analyses to examine the 
relationships between changes in rsFC/CBF and corresponding clinical outcomes. 
Additional variables, such as age and gender will be included as covariates to reduce 
subject heterogeneity. 
Resting state functional connectivity analysis  
Seed based functional connectivity analysis Seed based rsFC will be applied with CONN 
toolbox implemented in MATLAB. The pre-processing of fMRI data will include slice-timing 
correction, realignment, co-registration to subjects’ respective structural images, 
normalization of images to the standard Montreal Neurological Institute template, and 
smoothing with a 6 mm full width at half maximum (FWHM) kernel. In addition to these 
steps, we will employ segmentation of gray matter, white matter, and cerebrospinal ﬂuid 
(CSF) areas for the removal of temporal confounding factors (white matter and CSF). Band-
pass ﬁltering will be performed with a frequency window of 0.008-0.09Hz.  
To eliminate head motion and artifacts, we will identify outlier time points in the motion 
parameters and global signal intensity using ART. For each subject, we will treat images 
(time points) as outliers if composite movement from a preceding image exceeds 0.5 mm, 
or if the global mean intensity is greater than 3 standard deviations from the mean image 
intensity for the entire resting state scan. 
In addition, 6 rigid body motion parameters, as well as white matter and cerebrospinal ﬂuid 
(CSF) signals will be regressed out, and images will be normalized using structural image 
uniﬁed segmentation and then re-sampled to 2-mm cubic voxels. After linear de-trending, 
data will be ﬁltered using a temporal band pass (0.01-0.08 Hz) to remove low frequency 

noise and inﬂuences of higher frequencies reﬂecting cardiac and respiratory signals, and 
ﬁnally smoothed using a full width half maximum of 6 mm.  
In this study, we will use several seed regions. The ﬁrst seed region is the bilateral PAG 
(spheres with 2 mm radius on left and right side, peaks of MNI coordinates ± 4 -26 -14). It 
will allow us to elucidate the involvement of the descending pain modulation system. The 
second seed region will be bilateral M1. The reason we chose this location is based on 1) a 
previous study, where we found this area to show signiﬁcant differences in functional 
connectivity between cLBP patients and healthy controls and 2) its proximity to the center 
of the tDCS location in this study.  
Then, the averaged time course will be obtained from the seeds and correlation analysis 
will be performed in a voxel-wise way to generate the functional connectivity map. The 
correlation coefficient map will be converted into a Fisher-Z map using Fisher’s r-to-z 
transformation to improve the normality. 
Group analysis will be applied using the random effects model. A full factorial design 
module in SPM12 will be applied with four treatments and two time points (before and after 
treatments). Speciﬁcally, we will compare the pre- and post-treatment PAG rsFC (and the 
control seed) and M1 rsFC differences across different treatments. ACOVA analyses will be 
applied between PAG rsFC changes (pre- minus post-treatment) and the corresponding 
clinical assessments and QST changes respectively. A threshold of p < 0.005 and p < 0.05 
small volume corrected will be applied for the regions of interest (ROIs). A threshold of p < 
0.005 uncorrected and p < 0.05 Family-wise Error (FWE) corrected threshold will be used 
for non-ROIs. The ROIs include: ACC, MPFC, MCC, insula, S2, DLPFC, and parietal lobule. 
ASL data analysis  
ASL data will be collected before and after the ﬁrst and last treatments. Data analysis will 
be applied using a method applied in our previous studies. The pre-statistical processing 
for each subject will include head motion correction, skull stripping using FSL’s Brain 
Extraction Tool (BET), smoothing with a Gaussian 5 mm FWHM ﬁlter to reduce spatial 
variability, and a 100 s high-pass temporal ﬁltering. In order to quantify perfusion (CBF), 
even and odd images will be subtracted (60 pairs) and averaged to produce an average 
CBF-weighted image. Quantiﬁcation of perfusion will then be performed using a single 
compartment model that assumes a well-mixed single compartment model. Group 
analysis will be applied using ANOVA. The same threshold used for the rsFC data analysis 
mentioned above, will be applied.