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Supplementary Information
Methods
Functional MRI Tasks:
(a) N-back task: The N-back task was administered within the MRI scanner at baseline
and after rifaximin therapy after one training run in both instances. In this task, the
subject is required to monitor a series of letters that flash across a screen and to
respond whenever the letter presented is the same as the one presented n trials
previously, where n in this study was 0, 1 and 2. This task involves a randomized
presentation of target letters in 0-back (subject responds whenever a particular letter is
seen), 1 back (subject responds when a letter is repeated with one letter in between)
and 2-back (subject responds when a letter is repeated with two other letters in
between). The 2-back task presents the highest cognitive load. Three blocks of each Nback condition were presented with a total of 12 targets per condition.
(b). ICT task: The ability of a subject to correctly respond to targets and withhold
responses to lures was tested with the ICT, which was only presented in the scanner
after a brief pre-scan training session(Garavan et al. 1999). During the ICT, a stream of
letters was back-projected serially onto a MR compatible screen via a computer running
Presentation software (NeuroBehavioral Systems, California). Subjects viewed the
screen via a mirror placed over the head coil and responded using a button located near
their right hand. Each letter was presented for 500 ms, with no interstimulus interval.
Letters were classified as targets (x and y), to which the subject responded, lures (x and
y), to which the subject inhibited a response, and distractors (all other letters), which the
subject ignored. Subjects were instructed to respond to alternating x’s and y’s; i.e.
respond if the current x was preceded by a y, or if the current y was preceded by an x.
Subjects were instructed to inhibit their response if the pattern did not alternate; i.e.
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inhibit if the current x was preceded by an x, or if the current y was preceded by a y. On
average, targets were presented every 3.5 sec and lures every 20 sec. A minimum of
15 sec separated consecutive lures. For the entire experiment, there were 212 targets,
40 lures, and 1248 distractors presented. Response prepotency was maintained by
including many more targets than lures and by instructing subjects to respond quickly.
The ICT is well suited to the scanner environment in which subjects view a simple visual
stimulus and respond with a single button press.
fMRI Acquisition: All images were acquired on a 3T GE Signa (Milwaukee, WI) scanner
using a quadrature birdcage RF head coil. BOLD images for both ICT and n-back tasks
were acquired using a gradient echo, echo-planar pulse sequence
(TE=30ms;
TR=2000ms, FOV=240mm; 64 x 64 matrix; 3.75mm x 3.75mm in-plane resolution, Slice
Thickness=4mm). Slices were prescribed 30 degrees to the AC-PC line to reduce
susceptibility induced signal dropouts in the orbitofrontal areas of the brain (Deichmann
2003). Six runs of 83 volumes each were acquired for ICT and a single run of 272
volumes was acquired for the N-back task. A vacuum pillow was used to minimize head
motion. The task stimuli were back-projected onto a MR compatible screen. The screen
was viewed by a mirror on top of the head-coil. The task was delivered using
Presentation software (NeuroBehavioral Systems, California). Subjects viewed the
letters presented on a screen while lying down in the fMRI scanner through prism
glasses and responded using a button located near their right hand. A high resolution
structural brain image was acquired using a SPGR sequence (TE=3.2ms, TR =8.2ms,
TI=450ms, Slice thickness =1.2mm, FOV=240mm, 256 x 192 matrix) for anatomical
localization and registration of functional images.
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fMRI data analysis: fMRI data analysis for both tasks was carried out using FEAT (FMRI
Expert Analysis Tool) v 5.98 part of FSL (FMRIB’s Software Library,
www.fmrib.ox.ac.uk/fsl) (Jenkinson et al. 2002; Smith 2002; Woolrich et al. 2001a). The
following pre-statistics processing was applied; motion correction using MCFLIRT, nonbrain removal using BET spatial smoothing using a Gaussian kernel of FWHM 6.0mm;
grand-mean intensity normalization of the entire 4D dataset by a single multiplicative
factor; highpass temporal filtering (Gaussian-weighted least-squares straight line fitting,
with sigma=50.0s for ICT data and 128s for nback data). Subjects that showed >1.5mm
absolute motion were removed from further analysis. After preprocessing, a time-series
statistical analysis was carried out using FILM with local autocorrelation correction.
ICT analysis: First level time series statistical analysis was carried out on each of the six
ICT runs separately. Stimulus timings for Correct Response to Target , Correct Inhibition
to Lures (CIL), Incorrect Response to Lures were extracted from each individual
response file recorded by the Presentation software. Regressors were created by
convolving these events by three basis functions generated from FLOBS(Woolrich et al.
2004). This method avoids making a priori assumptions regarding the shape, delay or
magnitude of the hemodynamic response function. A general linear model was specified
that included CRT, CIL and INCRL as regressors of interest and motion parameters as
confound regressors. Contrast maps were created for each basis function for each
condition vs. Baseline (Fixation + Non-Target letters) and registered to high-resolution
structural and the 152 brain average Montreal Neurological Institute (MNI) standard
space template using linear (FLIRT) and nonlinear (FNIRT) registration
methods(Jenkinson et al. 2002; Jenkinson and Smith 2001). Only the contrast image for
the canonical basis function (first of the three basis functions) for CIL, CRT and INCRL
was passed on to the higher-level analysis. Higher-level analysis was performed in two
steps. The first step combined the 6 runs at the subject level using a standard weighted
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fixed effects model to form a single statistic image for the CIL condition per subject. A
paired group comparison was done at the second level to investigate brain areas with
significant changes in activation to correct inhibition following Rifaximin treatment. This
was done using FLAME (FMRIB's Local Analysis of Mixed Effects) stage 1(Beckmann et
al. 2003; Woolrich 2008). Pre>Post-Rifaximin and Post>Pre-Rifaximin contrasts were
generated and thresholded using a cluster-based threshold(Woolrich et al. 2001b).
N-back analysis: Regressors were created for 0-back, 1-back and 2-back conditions and
convolved with a gamma-variate hemodynamic response function and a multiple linear
regression analysis was performed that also included motion parameters and motion
outlier volumes(Jenkinson and Smith 2001) within the model. Contrast maps (1-back –
0-back) and (2-back – 0-back) were created to highlight the brain areas involved in
working memory and registered to standard space using the same method as described
above. Higher-level analysis involved a paired group comparison conducted using
FLAME stage 1 and Pre>Post-Rifaximin and Post>Pre-Rifaximin contrasts were
generated and thresholded using a cluster-based threshold. We also performed
psychophysiological Interaction Analysis (PPI) for N-back to investigate whether there is
a change in coupling or effective connectivity between a seed region in the working
memory network and any other areas between the pre-rifaximin and post-rifaximin
states. We defined seed regions within the working memory network based on the
overlap of group mean activation voxels in pre- and post-rifaximin conditions. Spherical
ROIs of 8mm radius were drawn bilaterally in the Inferior Frontal Gyrus (IFG) and the
Precentral Gyrus. We setup four separate GLM analyses; for each ROI per rifaximin
state. The first regressor in the GLM analysis represented the working memory load
(2back-0back), second regressor represented the mean time series from the seed ROIs
and third regressor represented the PPI. We computed paired-group difference maps
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between pre- and post-rifaximin states using FLAME stage1 and a cluster-based
threshold(Friston et al. 1997; Rogers et al. 2007).
DTI and MRS acquisition
Diffusion-weighted volumes were acquired using a single shot, spin-echo echo-planar
imaging sequence (FOV=240mm, slice thickness=2.5mm, 96 x 96 matrix, TR =6000ms,
TE=77.5ms, b-value=1000s/mm2, # b0 images = 4, # Diffusion Directions = 60). Highresolution T1-weighted images were reformatted in three planes and were used for
localization of spectroscopic volumes of interest (VOI). Spectroscopic volumes were
prescribed for Right Posterior White Matter (RPWM), Posterior Gray Matter (PGM) and
Anterior Cingulate Cortex (ACC). Spectra were acquired in the three areas using
PROBE (TE/TR/NS/Volume = 35/1500/128/8 cm3) with automated shimming and water
suppression. Outer-volume suppression bands contiguous with the selected volume
were manually placed in all three dimensions.
DTI analysis:
Diffusion images were corrected for eddy current related distortions and simple head
motion using affine registration to a reference volume. Fractional Anisotropy (FA) and
Mean Diffusivity (MD) maps were computed using the diffusion Toolbox in FSL. These
maps were then transformed to standard space using a combination of nonlinear and
affine registration tools. Twelve a priori ROIs for major white matter tracts were created
using the DTI-based probabilistic white matter atlases using a probability threshold of
40%: frontal white matter (FWM), anterior internal capsule (AIC), posterior internal
capsule (PIC), external capsule (EC), posterior white matter (PWM), uncinate fasciculus
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(UF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF),
Cingulum (Cing) and three sub-regions of the corpus callosum; genu (CC_Genu), body
(CC_body) and splenium (CC_Splen). Mean FA and MD values were extracted from
individual maps(Wakana et al. 2007; Hua et al. 2008).
Spectroscopy analysis:
The choline (Cho), creatine (Cr), myo-inositol (mI) and glutamate+glutamine (Glx)
complex peak areas were computed using a quantitative assessment of the metabolite
concentration by means of LCModel software (Provencher 1993, 2001). These
metabolites were chosen because of prior HE research showing changes in these
metabolites(Sarma et al. 2011). Concentration ratios were computed with respect to
creatine concentration. LCModel utilizes a basis set of reference in vitro MR spectra for
all major metabolites to deduce absolute concentrations of corresponding compounds
from in vivo MR brain spectra. The model corrects for residual eddy current and RF coil
loading effects and allows for an estimate and subtraction of the spectral baseline
nonlinearity which is normally present at the short TE used in this study (Provencher
1993, 2001).
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Supplementary Table 1: Cognitive performance at pre-baseline compared to baseline
Cognitive tests
Number connection-A (seconds)
Number connection-B (seconds)
Digit symbol (raw score)
Block design (raw score)
Line tracing time (seconds)
Line tracing errors (number)
Serial dotting (seconds)
ICT median responses
Lures responded to (n/% CIL)
Targets (% correct)
Pre-baseline
43.2±20.4
100.2±23.4
48.6±10.4
24.3±9.6
119.5±29.3
44.6±25.3
70.2±23.5
Baseline
42.3±13.4
97.2±31.9
50.0±12.3
25.9±11.9
121.7±32.1
41.2±28.3
69.6±25.7
19 / 53%
97.2%
18 / 56%
96.50%
There was no significant learning on cognitive tests at pre-baseline (2.0±0.9 months
prior) compared to the baseline visit on paired t-tests.
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Supplementary table 2: Localization and Z-scores of the main effect of working
memory and ICT Correct Inhibition to Lures condition in MHE patients at baseline.
2-BACK
Z-score
5.3
4.83
4.74
4.58
4.34
4.29
4.25
4.21
4.13
4.12
4.01
3.74
3.71
3.58
3.17
Localization
L Precentral
Gyrus
Paracingulate
Gyrus
R Post
Supramarginal
Gyrus
R Middle
Frontal Gyrus
L Frontal Pole
Supplementary
Motor Area
R Angular
Gyrus
L Middle
Frontal Gyrus
R Frontal
Orbital Cortex
R Inferior
Frontal Gyrus
R Sup. Lateral
Occipital
Cortex
R Frontal
Pole
R Insular
Cortex
R Occipital
Fusiform
Gyrus
L Post.
Supramarginal
Gyrus
MNI (x,y,z)
mm
-38, -4,36
ICT correct inhibition to lures
MNI (x,y,z)
Z-score
Localization
mm
Paracingulate
6.18
8, 18, 40
Gyrus
0,22,38
6.03
R Frontal Pole
40, 52, 12
48, -40,40
5.79
Juxtapositional
Lobule (SMA)
-4, 8, 52
38,6,34
5.44
-48,38,14
5.44
-4,8,52
5.43
40, -50,30
5.41
-38,22,30
7.05
48,20, -6
5.93
50,20, -2
5.83
28, -58,42
5.42
36,36,12
4.78
36,24, -2
4.17
-28, -80, 16
3.9
-40, -48,36
5.42
R Middle
Frontal Gyrus
L
Supramarginal
Gyrus,Posterior
L Superior
Parietal Lobule
L Angular
Gyrus
R
Supramarginal
Gyrus,
Posterior
R Angular
Gyrus
L Middle
Frontal Gyrus
L Frontal Pole
L Precuneous
Cortex
R Precuneous
Cortex
Cingulate
Gyrus,
Posterior
L Insular Cortex
38, 14, 42
-40, -48, 38
-30, -58, 40
-44, -52, 42
48, -40, 40
38, -48, 36
-42, 28, 26
-40, 52, 0
-8, -60, 12
14, -60, 18
0, -46, 30
-30, 18, 0
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Supplementary Table 3: Mean Diffusivity (x10-3 mm2/s) in pre and post-rifaximin states
ROIs
FWM_L
EC_L
ILF_R
ILF_L
Cing_L
Pre
0.875±0.056
0.828±0.040
0.861±0.044
0.845±0.051
0.826±0.055
Post
0.865±0.039
0.828±0.040
0.858±0.046
0.844±0.065
0.810±0.037
p-value
0.247
0.279
0.347
0.463
0.154
Left Frontal White Matter (FWM_L), Left External Capsule (EC_L), Right and Left Inferior
Longitudinal Fasciculus (ILF_R, ILF_L), Left Cingulum (Cing_L)
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Supplementary table 4 Pre- and Post-Rifaximin metabolite concentration ratios within
three brain voxels, RPWM, PGM and ACC, calculated from LCModel analysis of 1HMRS.
Choline/Cr
Pre
Post
0.317±0.08
0.313±0.06
Myoinositol/Cr
Pre
Post
0.645±0.33
0.66±0.35
Glutamate+Glutamine/Cr
Pre
Post
2.098±0.69
2.097±0.59
0.33
0.26
0.19
PGM
p-value
0.208±0.05
0.201±0.03
0.25
0.609±0.22
0.601±0.20
0.37
2.103±0.67
ACC
p-value
0.290±0.04
0.283±0.03
0.41
0.573±0.28
0.606±0.19
0.12
2.342±0.61
RPWM
p-value
2.020±0.32
0.21
2.322±0.50
0.3
RPWM: right parietal white matter, PGM: posterior/occipital gray matter, ACC: anterior
cingulate cortex
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Supplementary Figure 1: (A) Main effect of working memory task. (B) Main Effect of
Correct Inhibition to Lures; cluster-corrected Z=3.1, P<0.05 with a color scale (Z=3.1 to
4.5) representing Z-scores (Red-Yellow), R: right hemisphere.
A
B
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