Association of Body Mass and Brain Activation during
Gastric Distention: Implications for Obesity
Dardo Tomasi1*, Gene-Jack Wang2,3, Ruiliang Wang2, Walter Backus4, Allan Geliebter5, Frank Telang1,
Millar C. Jayne1, Christopher Wong1, Joanna S. Fowler2,3, Nora D. Volkow1,6
1National Institute on Alcoholism and Alcohol Abuse, National Institutes of Health, Bethesda, Maryland, United States of America, 2Medical Department, Brookhaven
National Laboratory, Upton, New York, United States of America, 3Department of Psychiatry, Mt Sinai School of Medicine, New York, New York, United States of America,
4Depertment of Anesthesiology, SUNY, Stony Brook, New York, United States of America, 5St. Luke’s/Roosevelt Hospital, Columbia University, New York, New York,
United States of America, 6National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland, United States of America
Background: Gastric distention (GD), as it occurs during meal ingestion, signals a full stomach and it is one of the key
mechanisms controlling food intake. Previous studies on GD showed lower activation of the amygdala for subjects with
higher body mass index (BMI). Since obese subjects have dopaminergic deficits that correlate negatively with BMI and the
amygdala is innervated by dopamine neurons, we hypothesized that BMI would correlate negatively with activation not just
in the amygdala but also in other dopaminergic brain regions (midbrain and hypothalamus).
Methodology/Principal Findings: We used functional magnetic resonance imaging (fMRI) to evaluate brain activation
during GD in 24 healthy subjects with BMI range of 20–39 kg/m2. Using multiple regression and cross-correlation analyses
based on a family-wise error corrected threshold P=0.05, we show that during slow GD to maximum volumes of 500 ml and
700 ml subjects with increased BMI had increased activation in cerebellum and left posterior insula, and decreased
activation of dopaminergic (amygdala, midbrain, hypothalamus, thalamus) and serotonergic (pons) brain regions and
anterior insula, regions that were functionally interconnected with one another.
Conclusions: The negative correlation between BMI and BOLD responses to gastric distention in dopaminergic (midbrain,
hypothalamus, amygdala, thalamus) and serotonergic (pons) brain regions is consistent with disruption of dopaminergic
and serotonergic signaling in obesity. In contrast the positive correlation between BMI and BOLD responses in posterior
insula and cerebellum suggests an opposing mechanism that promotes food intake in obese subjects that may underlie
their ability to consume at once large food volumes despite increasing gastric distention.
Citation: Tomasi D, Wang G-J, Wang R, Backus W, Geliebter A, et al. (2009) Association of Body Mass and Brain Activation during Gastric Distention: Implications
for Obesity. PLoS ONE 4(8): e6847. doi:10.1371/journal.pone.0006847
Editor: Silvana Gaetani, Sapienza University of Rome, Italy
Received June 18, 2009; Accepted August 5, 2009; Published August 31, 2009
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public
domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: This work was accomplished at Brookhaven National Laboratory under contract DE-AC-298CH10886 with the U.S. Department of Energy and supported
by its Office of Biological and Environmental Research, the National Institute on Drug Abuse (R01DA006891 and R01DA006278), the National Institutes on
Alcoholism and Alcohol Abuse (R01AA09481 and Y1AA3009) and NCRR (GCRC 5-M01-RR-10710). The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Why obese people continue to eat even when their stomach is
full? The role of the stomach in satiety is not well understood. The
human stomach can hold up to 1.5 liters of food  and the
distention of the mechanoreceptors in the stomach wall might
control food intake  by activating satiety brain regions .
Studies in animals have shown that gastric load suppresses food
intake as a function of caloric value and volume of food intake
(reviewed in ), and the human sensation of fullness after a meal
is associated with the volume of GD [4,5].
Neuroimaging studies that used a balloon placed in the human
stomach have shown that distention of the gastric wall activates
cortical and subcortical brain regions [3,5–7]. During fMRI,
gastric balloons with sudden  or gradual  expansions activate
a visceral network that include the somatosentory cortex and the
inferior dorsolateral prefrontal cortex as well as amygdala and
insula. However, the effects of body mass on brain activation
during GD are largely unknown. There is only one study that
assessed BMI effects on brain activation during GD that reported
lower amygdala activation for subjects with increased BMI ,
suggesting amygdala hypo-activation in obesity; though, the
sample did not include obese patients. Since the amygdala is
innervated by dopamine (DA) neurons  and obese subjects have
dopaminergic deficits that correlate negatively with BMI  we
hypothesized that the blunted amygdalar activation during
gradual GD in subjects with higher BMI  would reflect lower
activation in midbrain (the brain region were DA neurons are
located) and in DA innervated brain regions involved in feeding
behaviors (hypothalamus). Thus during gradual GD, BMI would
correlate negatively with activation not just in the amygdala but
also in midbrain and hypothalamus.
To test this hypothesis we used blood oxygenation level
dependent (BOLD) fMRI and a gradual GD paradigm . The
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larger sample size (N=24) used in this study includes published
data from 18 non-obese subjects using a GD volume of 500 ml,
and unpublished data from five obese patients. The present study
further expands on the prior study by including two different
balloon volumes (500 ml and 700 ml) to assess the sensitivity of
neuronal responses to volume differences during GD. Here we
show that subjects with higher BMI had lower activation in a
dopaminergic network (midbrain, hypothalamus, amygdala, and
thalamus) and higher activation in cerebellum and posterior
insula. The cross-correlation of time-varying fMRI signals among
these BMI-sensitive regions further support a blunted network
response, as opposed to isolated regional effects, in obesity.
Twenty-four non-smoking and right-handed healthy subjects
[20 men; age 32.267.0 years, education: 14.462.3 years;
BMI range 20–39 kg/m2, mean=26.865.8 kg/m2; 11 lean
subjects (BMI,25 kg/m2), 8 overweight subjects (25 kg/m2
,BMI,30 kg/m2), and 5 obese subjects (30 kg/m2,BMI)]
participated in the study. All participants provided written
informed consent approved by the local Institutional Review
Board (Stony Brook University’s Committee on Research
Involving Human Subjects, CORIHS). Subjects were screened
carefully with a detailed medical history, physical and neurological
examination and urine toxicology for psychotropic drugs to ensure
they were healthy at the time of the study. Subjects were included
in the study if they were (a) 18–65 years old and (b) able to
understand and give informed consent. Subjects were excluded if
they used (c) anorexic medications for weight loss in the past 6
months; had (d) positive urine results for psychoactive drugs or
pregnancy, history of (e) dependence on alcohol or other drugs of
abuse (caffeine.5 cups/day or nicotine.1 pack/day), (f) neuro-
logical disorders of central origin or major psychiatric disorders, (g)
esophageal reflux, (h) uncontrolled cardiovascular disease, (i)
diabetes or other uncontrolled endocrine disease, (j) acute or
chronic medical illness that may affect brain function or (k) head
trauma with loss of consciousness.30 min, any (l) medical
conditions that may alter cerebral function or (m) contraindica-
tions for MRI (metallic or electronic implants, metallic tattoos in
the neck/head, claustrophobia). Subjects were asked to have their
last meal at 7 PM the evening before the day of the study and were
scanned between 16 and 18 h after their last meal.
Balloon insertion in the stomach
The balloon assembly consisted of a double-lumen tube (Fr-10)
securely tied to a thin Latex non-lubricated condom with non-
waxed dental floss . The physician placed a small plastic
mouthpiece coated with about 3 ml of 2% lidocaine viscous gel in
the subject’s mouth. A short time later (3 min or less), the
mouthpiece was removed. Subjects were given a cup of lidocaine-
water solution and they were asked to rinse the back of the tongue
several times with it and spit it out. The deflated balloon assembly
was orally introduced in the stomach by advancing gently the
double-lumen tube. During this procedure, the subject was asked
to swallow to facilitate placement of the balloon into the stomach.
Then the balloon was filled with 100 ml of water at body
temperature (,37uC), using an electric pump (Masterflex L/S,
Cole-Parmer Instrument Co., Vernon Hills, IL), and the tube was
gently pulled out until resistance was met at the gastro esophageal
junction. The tube was then pushed down another 2 cm to avoid
obstructing esophageal flow (Fig. 1). The exterior end of the tube
was taped to the cheek and shoulder to fix the balloon’s position,
and the water in the balloon was removed by using the electric
pump with reversed water flow. The balloon insertion procedure
was initiated between 15 and 17 h after the last meal.
Gastric distention paradigm
After the balloon insertion, the subject was positioned in the
MRI scanner. There were two different GD paradigms, GD1 and
GD2, with different maximum distention volumes. Two filling-
emptying cycles were used for both paradigms; after a 30 s-long
resting baseline, the electric pump started a constant flow of water
Figure 1. Gradual gastric distention (GD) paradigm. The deflated
balloon was inserted orally and positioned in the stomach 2-cm above
the gastro-esophageal junction. The solid and dashed lines depict the
emptied (0 ml) and filled (500 ml for GD1 or 700 ml for GD2) balloon
conditions. The balloon was filled and emptied with constant flow
(5 ml/s) of tap water (warmed at 37uC) in either 90s to 500 ml (GD1) or
in 140 s to 700 ml (GD2). The vagus nerve transmits the signal of a full
stomach to the solitary and parabrachial nuclei in the brain stem that
project to dopaminergic and serotonergic nuclei in midbrain and pons.
Other regions implicated in the control of food intake are additionally
highlighted (hypothalamus, amygdala, and cerebellum).
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at body temperature (,37uC) to fill up the balloon to either
500 ml in 90 seconds (GD1) or 700 ml in 140 s (GD2). The water
flow was interrupted for 30 s when the volume reached maximum
value (500 ml for GD1 and 700 ml for GD2). Then the balloon
was emptied with reversed water flow. When the balloon reached
null volume there was a pause for 30 s and the filling-emptying
cycle was repeated to increase statistical power. The GD1 and
GD2 fMRI runs were repeated once after a 1-minute resting
interval to further increase statistical power. The total time of this
fMRI paradigm was 45 minutes.
The subjects rated their fullness, discomfort, hunger, and desire
for food during the last 16 s of the pauses . These rating
questions were displayed to the subjects using MRI-compatible
goggles (MRVision2000, Resonance Technology Inc., Northridge,
CA). The subjects responded by pressing one of the four available
buttons in an MRI-compatible pad (Lumila LP-400, Cedrus
Corporation, San Pedro, CA) . The subjects were trained on
how to use the response pad and how to respond to the
questionnaire outside the MRI scanner and prior to the placement
of the balloon in the stomach. The task was developed in E-prime
(Psychology Software Tools, Inc., Pittsburgh, PA) and used a
trigger pulse from the MRI console for precise synchronization
with fMRI acquisition; subjects’ responses were recorded in a
standard PC using E-prime.
Functional MRI: data acquisition
Subjects underwent MRI in a 4-Tesla whole-body Varian/
Siemens MRI scanner. A T2*-weighted single-shot gradient-echo
planar imaging (EPI) pulse sequence (TE/TR=20/2000 ms, 4-
mm slice thickness, 1-mm gap, 35 coronal slices, 64664 matrix
size, 3.163.1 mm in-plane resolution, 90u-flip angle, time points:
255 for GD1 or 355 for GD2, 200.00 kHz bandwidth) with ramp-
sampling and whole brain coverage was used to collect functional
images with BOLD contrast. Padding was used to minimize
motion. Subject motion was monitored immediately after each
fMRI run using a k-space motion detection algorithm  written
in IDL (ITT Visual Information Solutions, Boulder, CO).
Earplugs (228 dB sound pressure level attenuation; Aearo Ear
TaperFit 2; Aearo Company), headphones (230 dB sound
pressure level attenuation; Commander XG MRI Audio System,
Resonance Technology inc.), and a ‘‘quiet’’ acquisition approach
were used to minimize the interference effect of scanner noise
during fMRI . Anatomical images were collected using a T1-
0.9460.9461.00 mm3spatial resolution, axial orientation, 256
readout and 192696 phase-encoding steps, 16 minutes scan time)
and a modified T2-weighted Hyperecho sequence  (TE/
TR=0.042/10 seconds, echo train length=16, 2566256 matrix
size, 30 coronal slices, 0.8660.86 mm2in-plane resolution, 5 mm
thickness, no gap, 2 min scan time), and reviewed by the
neurologist to rule out gross morphological abnormalities of the
Image reconstruction was performed using an iterative phase
correction method in IDL that minimizes signal-loss artifacts in
EPI . The first four imaging time points were discarded to
avoid non-equilibrium effects in the fMRI signal. The statistical
parametric mapping package SPM2 (Welcome Department of
Cognitive Neurology, London UK) was used for subsequent
analyses. A 4thdegree B-spline function without weighting and
without warping was used for image realignment (head motion
was less than 1-mm translations and 1u-rotations for all scans for
all fMRI runs). Spatial normalization to the Talairach frame of
reference was performed using a 12-parameters affine transfor-
mation with medium regularization, 16-nonlinear iterations and
voxel size of 36363 mm3, and a modified version of the standard
SPM2 EPI template, which was modulated by the average EPI
signal intensity across subjects to minimize the effect of brain
regions exhibiting strong susceptibility-related signal-loss artifacts.
Note that this customized EPI template minimizes spurious
geometric distortions during spatial normalization of EPI datasets
collected at 4-Tesla and TE=20 ms. An 8-mm full-with-half-
maximum (FWHM) Gaussian kernel was used for spatial
BOLD-fMRI responses during GD1 and GD2 were estimated
using a general linear model  with two independent castle
designs. As in our previous work , six conditions were used for
GD1: (1) ratings; (2) flow in and volume , 250 ml; (3) flow in and
250 ml , volume ,500 ml; (4) null flow and volume=500 ml; (5)
flow out and 250 ml, volume ,500 ml; and (6) flow out and
volume ,250 ml. Paralleling GD1, the design matrix for GD2
had 8 conditions: (1) ratings; (2) flow in and volume ,250 ml; (3)
flow in and 250 ml, volume ,500 ml; (4) flow in and 500 ml,
volume ,700 ml; (5) null flow and volume=700 ml; (6) flow out
and 500 ml , volume ,700 ml; (7) flow out and 250 ml, volume
,500 ml; and (8) flow out and volume , 250 ml. These design
matrices were convolved with a canonical hemodynamic response
function (HRF) and high-pass filtered with frequency cut-offs of 1/
500 and 1/700 Hz for GD1 and GD2, respectively. The BOLD
signal strength was estimated without the removal of global effects
(global normalization) to minimize false deactivation signals
We evaluated the effect of BMI on BOLD-fMRI signals in the
whole brain with a multiple regression analysis in SPM2.
Specifically, the estimated BOLD signal maps for all volumetric
conditions (0–250 ml, 250–500 ml, 500 ml, 500–700 ml, and
700 ml), subjects, and sessions (GD1 and GD2; first and second
repetition) were included in a multiple regression (with constant)
random-effects model in SPM2 with two regressors; (1) a zero-
mean regressor reflecting the average volume of each condition
(125 ml, 375 ml, 500 ml, 600 ml, and 700 ml), and (2) a zero-
mean regressor reflecting the subjects’ BMI. These multiple
regression analyses were conducted using 309 images. Brain
activation clusters were corrected for multiple comparisons using
the continuous random field calculation implemented in SPM2. A
family-wise error (FWE) threshold Pcorr,0.05, corrected for
multiple comparisons at the voxel-level, was used to display
statistical maps reflecting correlations of brain activation and BMI.
Clusters with at least 5 voxels and Pcorr,0.05, corrected for
multiple comparisons, were considered significant in group
analyses of brain activation.
Brain activation clusters were further evaluated with region-of-
interest (ROI) analyses to identify potential outliers that might
influence linear regressions, and to report average values in a
volume comparable to the image smoothness (e.g. resolution
elements, or ‘‘resels’’ ) rather than single-voxel peak values.
The volume of the resels was estimated using the random field
calculation in SPM2 as a near cubic volume with Cartesian
FWHM=13.1 mm, 12.7 mm, 12.7 mm. Thus, 9-mm isotropic
masks containing 27 imaging voxels (0.73 ml) were defined at the
centers of relevant activation clusters to extract the average %
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BOLD signal from individual contrast maps. These masks were
created and centered at the precise coordinates listed in Table 1;
the coordinates of the ROI masks were kept fixed across subjects
and conditions. The average and standard deviation values of
BOLD signals within these ROIs were computed for each subject
and fMRI run using a custom program written in IDL.
For each subject and fMRI run,
the realigned, normalized, and smoothed time-varying MRI
signals within the ROIs previously defined and listed in Table 1
were band-pass filtered (0.01–0.1 Hz frequency bandwidth); then,
the Pearson product-moment correlation coefficient was used to
calculate the cross-correlation between signals in different ROIs.
The Fisher transform was used to normalize the step distributed
correlation coefficients, Rij, of the functional connectivity matrix.
Thus, the probability density function of the normalized cross-
correlation coefficients, rij, was approximately Gaussian, allowing
us to analyze the statistical significance of the functional
connectivity matrix across subjects using standard statistics (t-tests).
Normalized cross-correlation coefficients with Pc,0.05, corrected
formultiple comparisons (Bonferroni
comparisons), were considered significant in group analyses of
Ratings of fullness, discomfort, hunger, and desire for food,
collected during the empty and full balloon conditions were
averaged across fMRI runs, independently for GD1 and GD2
and for each subject; the rating datasets corresponding to 4 subjects
were lost due to data acquisition problems. For GD1 and GD2, the
rating of fullness was significantly higher for the full condition than
for the empty condition (P,0.0002; paired t-test; Fig. 2). There
were no statistically significant differences in other rating variables
between full and empty conditions. There were no significant
correlations between BMI and none of the rating variable.
Table 1. Location of major activation clusters in the Talairach frame of reference and statistical significance of brain activation for
gastric distention (GD: GD1 and GD2, conjunctive analysis), body mass index (BMI), as well as for the cross-correlation, r, of fMRI
signals in these ROIs with signals in hypothalamus.
ROI Brain regionBAx [mm]y [mm]z [mm] GD T-scoreBMI T-score
Activated cortical regions
1 Inferior parietal cortex 40
248 519.3 NSNS
2 Precentral gyrus4
224 669.7NS NS
3 Posterior insula13
23 5.2 6.3
4 Posterior insula1336
230 4.8 NSNS
5 Supramarginal gyrus40
23936 3.86.1 NS
257 364.8 7.3NS
7 Paracentral Lobule5
242 544.5 6.1NS
BMI sensitive brain regions
8 Cerebellum (Declive)
9 Cerebellum (Tonsil)30
224 NS 8.0
11 Thalamus (Ventral Lateral)
13Anterior Insula 45–4748 33
Deactivated brain regions
19 Precentral gyrus6
20Inferior Frontal gyrus9 339 27
21Superior Frontal Gyrus615963
27.5 NS 2.8
24Anterior Cingulate Gyrus240270
25 Parahippocampal gyrus3015
26Lingual gyrus 19
Average values in isotropic cubic ROIs (27 voxels; 0.73 cc) centered at the (x, y, z) coordinates. Sample size=24 healthy subjects.
Brain Activation GD & BMI
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fMRI: Brain activation
Two subjects did not tolerate the 700 ml balloon distention
paradigm(2GD2 runspersubject);onlythe GD1runs were included
in the analysis for these subjects. One GD1 dataset and one GD2
dataset were lost due to technical problems. Therefore, a total of six
fMRI runs were not completed; thus, 47-GD1 and 43-GD2 fMRI
runs were included in the analyses. The GD paradigm (conjunctive
analysis of GD1 and GD2) caused bilateral activation in insula, left
inferior and superior parietal, and left prefrontal cortices, left
precuneus, left amygdala and right cerebellum [Pcorr,0.001,
corrected for multiple comparisons using the family-wise error
(FWE) threshold p=0.05; Fig. 3 top panel]. This activation pattern is
similar to that reported in our prior study for GD1 and a smaller
sample that did not include obese subjects . Furthermore, the
conjunctive analysis of GD1 and GD2 revealed negative BOLD
responses (brain deactivation) to GD bilaterally in caudate, anterior
cingulate, parahippocampal, and lingual gyri, as well as inferior and
superior frontal gyri and left cerebellum (culmen). Brain activation/
deactivation changes resulting from increased balloon volume (from
500 to 700 ml) were not statistically significant in any brain region.
BMI vs. brain activation
Increased BMI was linearly associated with higher bilateral
activation in cerebellum (declive, tonsil and uvula) and left
posterior insula and lower bilateral activation in thalamus (dorsal
lateral nucleus), anterior insula, midbrain (substantia nigra),
amygdala, pons (dorsal raphe), and in a sub-thalamic gray matter
region at the location of the posterior hypothalamus 
(Pcorr,0.001; Fig. 3: bottom panel). As shown in Table 1, the
positive correlation patterns overlapped GD-activation in left
parietal and temporal regions including supramarginal gyrus (BA
40), precuneus (BA 31), and paracentral lobule (BA 5). The
negative correlation pattern overlapped GD-activation in amyg-
dala and GD-deactivation in inferior frontal (BA 9) and
parahippocampal (BA 24) gyri and left cerebellum (culmen).
The ROI analyses were consistent with the voxel-wise SPM
results in all regions (Table 1). Figures 4 and 5 exemplify the
positive and negative correlations of brain activation with BMI
across subjects. Indeed, BOLD responses in left posterior insula,
Figure 2. Behavioral responses during gradual GD. Ratings of fullness, discomfort, hunger, and desire for food, collected during the empty and
full balloon conditions for GD1 (500 ml) and GD2 (700 ml). (*) P,0.0002.
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Figure 3. Brain activation during gradual gastric distention (GD). Top panel: Statistical maps showing regions with significant brain
activation (red-yellow) and deactivation (blue-green) during gradual GD. Bottom panel: correlations between BMI and BOLD-fMRI responses in the
brain during gradual GD. Threshold for statistical significance: Pcorr,0.05 corrected for multiple comparisons using the family-wise error (FWE)
correction. Sample: 24 healthy controls. Data from all 47 GD1 and 43 GD2 fMRI runs were included in SPM2 multiple regression analyses. Activation/
correlation patterns reflecting the effect of volume were not significant in any brain region.
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supramarginal gyrus, paracentral lobule, and cerebellum (declive,
tonsil, and uvula) were significantly higher for obese than for lean
subjects (P,0.04; Fig. 4). Conversely, BOLD responses in anterior
insula, thalamus, hypothalamus, midbrain, amygdala and pons
were significantly higher for lean (N=11) than for obese (N=5)
subjects (P,0.05; Fig. 5). Averaged across all volumetric
conditions, the BOLD-fMRI responses in the hypothalamus were
positively correlated across subjects with those in amygdala, and
midbrain and were negatively correlated with those in cerebellum
(|R|.0.55; P,0.005; Fig. 6); similarly, responses in cerebellum
(uvula) were positively correlated across subjects with those in
posterior insula as well as in other regions of the cerebellum (tonsil
and declive) (R.0.70; P,0.0002).
Figure 7 shows the statistical significance of normalized cross-
correlations of time-varying fMRI signals among the 27 ROIs listed
in Table 1 (t-test across subjects and fMRI runs). Four major cross-
correlation patterns can be highlighted: First, BOLD-fMRI signals
in brain areas that exhibited negative BMI-BOLD correlation
(thalamus, anterior insula, hypothalamus, midbrain, amygdala and
pons) were positively cross-correlated; signals in these brain regions
had positive cross-correlations with those in deactivated brain
regions (left precentral and lingual gyri, right parahippocampal and
well as inferior and superior frontal gyri, and caudate), and negative
cross-correlations with those in cerebellum (uvula), left posterior
insula, and anterior cingulate gyrus. Second, signals in amygdala
and pons had negative cross-correlation with those in most
deactivated regions (superior prefrontal cortex, caudate, and
anterior cingulate gyrus). Third, signals in left parietal, temporal,
and prefrontal regions had negative cross-correlation with those in
most deactivated brain regions. Fourth, signals in caudate had
negative cross-correlation with those in cerebellum.
GD is thought to play an important role in the regulation of food
intake. Here we document that during GD, brain activation
responses in cerebellum and left posterior insula had a positive
correlation with BMI whereas those in anterior insula, thalamus,
amygdala, posterior hypothalamus, midbrain, and pons had a
negative correlation with BMI. Inasmuch as BMI reflects in part
individual’s eating behaviors, the opposite BMI-BOLD correlations
suggest that activation in these regions reflects opposing processes
involved in the modulation of food intake as a function of GD.
Negative correlations with BMI: midbrain, hypothalamus,
amygdala, thalamus, anterior insula and pons
Here we report that GD-activation in midbrain (where most
dopamine neurons are located ), pons (where serotonergic nuclei
involved inthe modulationofGDare located ),hypothalamus (a
brain region involved in the control of food intake ), amygdala
(limbic region implicated in emotional reactions to food [23,24]) and
thalamus (brain region implicated in arousal [25,26]) was negatively
correlated with BMI. Furthermore, BOLD-fMRI signals in these
regions were highly cross-correlated with one another. The GD
paradigm activated midbrain, pons, amygdala, thalamus, and
hypothalamus in lean subjects but not in obese subjects. The
negative correlation of BMI with BOLD responses in midbrain, is
compatible with the role of dopaminergic modulation of food intake
whereas that in the pons with the role of serotonin in the regulation
of food intake [27,28]. Indeed appetite suppressants such as
phentermine  increase both DA as well as serotonin in rats,
and there is evidence of both dopaminergic  and serotonergic
deficits [30,31] in obese subjects. Thus the decreased GD-activation
in midbrain and pons for obese subjects could reflect decreased
sensitivity of dopaminergic and serotonergic neurons to vagal
stimulation. The hypothalamus, thalamus and amygdala, which
were other brain regions showing a negative correlation with BMI,
receive dopaminergic [32–38] and serotonergic innervations [39–
41] and thus their decreased activation in obese subjects could also
reflect decreased dopaminergic and serotonergic neurotransmission
in obese subjects. Moreover the positive correlations between the
BOLD signals in pons and midbrain with those in hypothalamus,
thalamus and amygdala support the notion that activation in these
regions reflects an interconnected pathway, the response of which is
modulated in part by DA and serotonin neurotransmission. We had
previously reported activation of the left amygdala during gradual
GD  and here we expand to uncover a negative association
betweenBMIand activation inamygdala, anda correlation between
activation signals in amygdala with those in hypothalamus and
cerebellum. The latter is likely to reflect the connection of the
amygdala with the cerebellum via the hypothalamus . The
amygdala, is considered the ‘‘sensory gateway to emotions’’, and
receives sensory information from the forebrain  and from the
gut . The amygdala plays an important role in feeding behavior
[45,46] and amygdala lesions can result in hyperphagia, and
Figure 4. BOLD signals in hyper-activated regions vs. BMI.
Scatter plots exemplifying the positive correlations between the body
mass index (BMI) and the average BOLD-fMRI response across all
volumetric conditions (125, 375, 500, 600, and 700 ml) in cerebellum
and posterior insula during gradual GD (N=24).
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excessive weight gain [23,24]. The negative association of BMI with
activation in hypothalamus is also consistent with the preeminent
body weight . Indeed, a prior study showed that hypothalamic
deactivation after oral glucose administration was markedly
attenuated in obese subjects  and our findings further suggest
abnormal hypothalamic sensitivity to vagal stimulation in obesity.
Activation in pons in an area that included the superior gray
matter subnucleus of the dorsal raphe was also negatively associated
with BMI; obese subjects deactivated this region whereas lean
subjects activated it. Serotonergic neurons in the dorsal raphe are
recognized to play an important role in food intake . Dorsal
raphe neurons project to the dorsal vagal complex, which mediates
vagal stimulation of gastric motor function in rats [48,49]. Thus, the
increased deactivation of the pons with increased BMI during
gradual GD, and its positive functional connectivity with midbrain,
hypothalamus, anterior and posterior insula, and thalamus suggests
functional interactions between dopaminergic and serotonergic
signals in the modulation of food intake with GD.
In addition we also show that activation of the anterior insula
was negatively correlated with BMI. The anterior insular cortex is
implicated in awareness . Thus, the negative correlation
between BMI and BOLD-fMRI responses in anterior insula
suggests that subjects with higher BMI had lower awareness of the
balloon in the stomach than those with lower BMI. Our findings
differ from those that showed anterior insula deactivation in lean
subject but activation in obese subjects after food intake  or
during high-calorie food stimulation . This opposite correla-
tion pattern with BMI (negative in our study with GD and positive
with exposure to food stimuli) is likely to reflect the differences in
stimulation paradigms. The anterior insula is activated by taste
perception [53,54] and food stimulation  and the enhanced
activation in obese subjects may reflect increased sensitivity to food
stimuli whereas the blunted activation to GD may reflect their
decreased sensitivity to vagal signaling from GD.
Positive correlations with BMI: cerebellum and posterior
The GD paradigm activated cerebellum and posterior insula,
consistently with previous studies on GD that used sudden [5–7] or
gradual  balloon volume changes. Here we document for the
Figure 5. BOLD signals in hypo-activated regions vs. BMI. Scatter plots exemplifying the negative correlations between BMI and the average
BOLD-fMRI responses across all volumetric conditions (125, 375, 500, 600, and 700 ml) in dopaminergic brain regions (hypothalamus, midbrain, and
amygdala) during gradual GD (N=24).
Brain Activation GD & BMI
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first time that activation of cerebellum and posterior insula was
proportional to BMI. Moreover, GD activated these regions in
obese subjects but not in lean subjects. The cerebellum has an
important role for numerous nonsomatic functions other than
motor control, and there is growing evidence implicating the
cerebellum in the regulation of visceral functions and feeding
control . The cerebellum is directly connected to the
hypothalamus, a brain region that regulates food intake, energy
homeostasis, and body weight , and there is evidence of
cerebellar modulation of feeding-related hypothalamic neurons
. Therefore, it has been suggested that the cerebellar-
hypothalamic pathway has an important role in food intake
. Moreover, cerebellectomy in rodents resulted in reduced
food intake when compared with sham-operated animals, which
supports a feeding promoting role of the cerebellum in food
control . Previous fMRI studies have shown that the
cerebellum activates in response to gustatory or olfactory
stimulation [61,62], during high-calorie vs. low-calorie visual
stimulation , and 1-minute after oral glucose intake .
Positron emission tomography (PET) studies have shown that
increased regional cerebral blood flow (rCBF) in cerebellum was
associated with hunger and appetite , whereas decreased rCBF
has been linked with satiation [66,67]. Prior imaging studies have
also documented differences in rCBF in cerebellum after food
intake between obese and lean men , thus supporting the
importance of this brain region in the neuropathology of obesity.
The posterior insula, which was the other brain regions showing
a positive correlation with BMI, is activated by taste perception
Figure 6. Association of BOLD signals in hyper- and hypo-activated regions. Regression plots exemplifying positive and negative cross-
correlations of BOLD-fMRI signals (averaged across all volumetric conditions; 125, 375, 500, 600, and 700 ml) in different ROIs (listed in Table 1).
Brain Activation GD & BMI
PLoS ONE | www.plosone.org9 August 2009 | Volume 4 | Issue 8 | e6847
[53,54], food stimulation  and somatic and visceral-sensory
processing (reviewed in ). The posterior insula is connected
with primary and secondary somatosensory cortices and receives
inputs from the hypothalamus  and the amygdala . Its
differential activation as a function of BMI is consistent with a
prior study that showed that whereas obese men activated the
posterior and middle insula (region overlapping the area where we
show positive correlations with BMI) upon exposure to a meal,
lean subjects deactivated the middle insula and showed no
responses in the posterior insula .
Activation of left cortical regions by GD
Activation responses to GD in ventral parietal cortex and
posterior insula as well as somatosensory and motor areas were left
lateralized. Previous studies on painful gastric fundus distention
have shown either larger activation responses in the left
somatosensory cortex than those in the right somatosensory cortex
, or right lateralized responses in somatosensory and prefrontal
cortices  and parietal areas (BAs 5, 7, 39, and 40) . The left
lateralized response with GD is supportive of a model that
proposes lateralization of autonomic functions in the forebrain
with left representation of parasympathetic activity (including
vagal stimulation) and right representation of sympathetic
autonomic activity . However further work is required to
properly map the lateralization of autonomic functions in the
human brain including those involved with GD.
Here we studied brain activation to GD as a
functionof BMI.We show
dopaminergic (midbrain, hypothalamus, amygdala, thalamus)
and serotonergic (pons) brain regions that correlated negatively
with BMI, which is consistent with disruption of dopaminergic and
serotonergic signaling in obesity. In contrast BOLD responses in
posterior insula and cerebellum correlated positively with BMI,
which suggests an opposing mechanism that promotes food intake
in obese subjects that my underlie their ability to consume at once
large food volumes despite increasing GD.
The authors thank Barbara Hubbard for subject care and assistance during
the balloon insertion procedure, Lisa Cottone for software development
(rating questionnaire), and Tiffany Gagnon for graphic illustration (Fig. 1).
Conceived and designed the experiments: DT GJW AG NDV. Performed
the experiments: DT RW WB FT MJ. Analyzed the data: DT.
Contributed reagents/materials/analysis tools: DT CW. Wrote the paper:
DT GJW JSF NDV.
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