Neuropsychologia 81 (2016) 22–30
Contents lists available at ScienceDirect
Neuropsychologia
journal homepage: www.elsevier.com/locate/neuropsychologia
Altered frontal inter-hemispheric resting state functional connectivity
is associated with bulimic symptoms among restrained eaters
Shuaiyu Chen a,b,1, Debo Dong a,b,1, Todd Jackson a,b, Yanhua Su a,b, Hong Chen a,b,n
a
b
Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China
School of Psychology, Southwest University, Chongqing 400715, China
art ic l e i nf o
a b s t r a c t
Article history:
Received 8 February 2015
Received in revised form
22 June 2015
Accepted 27 June 2015
Available online 6 July 2015
Theory and research have indicated that restrained eating (RE) increases risk for binge-eating and eating
disorder symptoms. According to the goal conflict model, such risk may result from disrupted hedonicfeeding control and its interaction with reward-driven eating. However, RE-related alterations in functional interactions among associated underlying brain regions, especially between the cerebral hemispheres, have rarely been examined directly. Therefore, we investigated inter-hemispheric resting-state
functional connectivity (RSFC) among female restrained eaters (REs) (n=23) and unrestrained eaters
(UREs) (n=24) following food deprivation as well as its relation to overall bulimia nervosa (BN) symptoms using voxel-mirrored homotopic connectivity (VMHC). Seed-based RSFC associated with areas
exhibiting significant VMHC differences was also assessed. Compared to UREs, REs showed reduced
VMHC in the dorsal-lateral prefrontal cortex (DLPFC), an area involved in inhibiting hedonic overeating.
REs also displayed decreased RSFC between the right DLPFC and regions associated with reward estimation – the ventromedial prefrontal cortex (VMPFC) and posterior cingulate cortex (PCC). Finally, bulimic tendencies had a negative correlation with VMHC in the DLPFC and a positive correlation with
functional connectivity (DLPFC and VMPFC) among REs but not UREs. Findings suggested that reduced
inter-hemispheric functional connectivity in appetite inhibition regions and altered functional connectivity in reward related regions may help to explain why some REs fail to control hedonically-motivated feeding and experience higher associated levels of BN symptomatology.
& 2015 Elsevier Ltd. All rights reserved.
Keywords:
Restrained eating
Resting-state fMRI
Inhibitory control
Reward
Bulimia nervosa symptoms
VMHC
1. Introduction
Restrained eaters (REs) pay excessive attention to their body
weight and attempt to lose or maintain weight by stringently restricting caloric intake (Herman and Mack, 1975; Lindroos et al.,
1997). In obesogenic environments, an increasing number of
people use restrained eating (RE) as a weight loss strategy (Andreyeva et al., 2010), even though few ultimately maintain weight
loss successfully. Furthermore, hedonic eating urges increase
among REs exposed to food cues and often result in over-eating,
binge-eating and inappropriate compensatory behavior (Fedoroff
et al., 2003; Harris et al., 2009).
Some theory and research (Fairburn and Brownell, 2005; Polivy
n
Corresponding author at: Key Laboratory of Cognition and Personality (Ministry
of
Education),
Southwest
University,
Chongqing
400715,
China.
Fax: þ86 23 6836 3625.
E-mail addresses: chenshuaiyu3@gmail.com (S. Chen),
debo.dong@gmail.com (D. Dong), toddjackson@hotmail.com (T. Jackson),
huatian@swu.edu.cn (Y. Su), chenhg@swu.edu.cn (H. Chen).
1
Contributed equally to this work.
http://dx.doi.org/10.1016/j.neuropsychologia.2015.06.036
0028-3932/& 2015 Elsevier Ltd. All rights reserved.
and Herman, 1985), has indicated that chronic hunger among REs
results in an over-reliance on cognitive resources to control eating
and increases risk for future binge-eating onset within settings
that have abundant external food cues and readily available food
(Lowe, 2002; Stice and Agras., 1998). Hence, RE is a potentially
important influence on the development of binge eating disorder
(BED) and bulimia nervosa (BN). To date, however, few researchers
have examined specific neural mechanisms related to RE. For example, neuroimaging studies have not directly evaluated neural
correlates of RE and overall BN symptom levels; doing so may
clarify why RE contributes to binge-eating and inappropriate
compensatory behaviors that can arise upon encountering
tempting foods (Allison, 2009; Stice, 2002; Stroebe et al., 2013).
Although an increasing number of people engage in chronical
dieting (Andreyeva et al., 2010), REs have higher rates of disinhibited eating and are vulnerable to uncontrolled binge-eating in
food-rich environments (Van Strien et al., 2005). According to a
recent goal conflict model of eating (Stroebe et al., 2013), REs often
fail to lose weight because caloric restriction corresponds to increased hedonic-eating reward motivation and reduced capacity to
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
control weight. Supporting these assumptions, REs shown inhibitory control deficits and high impulsivity levels, characteristics
that have been implicated in unhealthy eating (Jasinska et al.,
2012; van Koningsbruggen et al., 2013). Furthermore, reduced
spontaneous activity in the dorsal–lateral prefrontal cortex
(DLPFC) corresponds to lowered inhibitory control among REs
(Dong et al., 2014). Finally, REs may experience more pleasure
from feeding because they are generally more sensitive to reinforcing stimuli (Ahern et al., 2010).
In relation to dietary failures, the hedonic-inhibitory model of
feeding posits that overconsumption of palatable food results from
the disrupted balance between appetitive motivation mediated by
the mesolimbic reward system and active inhibitory control
mediated by the DLPFC, whereby appetitive motivation may override inhibitory control (Appelhans, 2009). REs also display hyperresponsivity in reward-related regions (Burger and Stice, 2011;
Coletta et al., 2009; Ely et al., 2014), especially after dietary violations (Demos et al., 2011). On this basis, high food reward sensitivity and low inhibitory control may interact as influences on
disinhibited overeating and weight regain among REs (Appelhans,
2009; Appelhans et al., 2011; Nederkoorn et al., 2010). Despite
evidence that RE is related to hyperactivity of reward and motivation circuitry and reduced inhibitory control, research assessing
functional coordination between inhibitory control and reward
systems among REs is sparse.
In addition, substance abuse and non-substance addiction are
closely associated with cognitive control deficits accompanied by
altered prefrontal cortex (PFC) function (Noel et al., 2013; Volkow
et al., 2008). For example, hypo-activation of frontal control regions has been found among adolescent girls with obesity (Batterink et al., 2010), in line with evidence that groups with drug
addiction or obesity have altered circuits reflecting disrupted inhibitory control (Volkow et al., 2012). Some researchers have hypothesized frontal asymmetry might be a latent mechanism underlying cognitive control deficits (Kelly et al., 2011; Wang et al.,
2013). For example, Silva et al. (2002) found that REs show more
right-sided prefrontal asymmetry than UREs do (Silva et al., 2002).
Nonetheless, it is still unclear which specific regions are related to
such asymmetry and whether prefrontal asymmetry is linked to
altered functional interactions, between reward and inhibitory
control regions. Furthermore, neuroimaging studies have not been
undertaken to assess relations between prefrontal asymmetry and
BN tendencies among REs, despite robust behavioral evidence for
links between RE and BN symptomatology.
Instead, task-based fMRI studies have been the main thrust of
RE neuroimaging studies (Burger and Stice, 2011; Coletta et al.,
2009; Schur et al., 2012; van der Laan et al., 2014). Given that
intrinsic resting-state activity may consume 95% of the brain's
energy (Raichle and Mintun, 2006), resting-state fMRI (RS-fMRI) is
a promising avenue for elucidating patterns of coherent spontaneous fMRI signal fluctuations (Biswal et al., 1995). Recently,
neuroscientists have proposed voxel-mirrored homotopic connectivity (VMHC) as an effective way to examine inter-hemispheric functional interactions (Anderson et al., 2011; Wang et al.,
2014; Zuo et al., 2010). VMHC involves the assessment of correlations of the time series for each voxel in one hemisphere with its
mirrored counterpart in the other hemisphere and provides a reliable approach to exploring prefrontal cognitive control circuitry
in REs (Kelly et al., 2011; Wang et al., 2013; Zuo et al., 2010). By
extension, VMHC may be a useful in examining the relations between prefrontal asymmetry and BN tendencies of REs.
Self-imposed food deprivation (i.e., reduced energy intake) is
one of the most common strategies for REs to prevent weight gain
or achieve weight reduction (Jeffery et al., 2000). Unfortunately,
prolonged dieting increases susceptibility to external food cues
and disinhibited eating (Mills and Palandra, 2008; Westenhoefer
23
et al., 1994). Food deprivation has a negative impact on cognitive
processing of food cues (Benau et al., 2014). For example, skipping
breakfast increases activity in reward value and motivational regions of the brain (Goldstone et al., 2009; Leidy et al., 2011) and
predicts poor long-term success among caloric restrictors (Dansinger et al., 2007). Arguably, given that skipping meals (e.g.,
breakfast) is as common weight control strategy among REs, assessing spontaneous brain activity such conditions may be an
ecologically-valid approach to studying the issue in this group.
Based on this overview and research implicating the DLPFC in
eating control and successful dieting (DelParigi, 2010; DelParigi
et al., 2007; Schonberg et al., 2014), we hypothesized that VMHC
might indicate REs have relatively impaired inter-hemispheric
functional interactions in the DLPFC. Furthermore, we reasoned
that interactions between altered inhibitory control (DLPFC) and
reward valued-computational regions (e.g., VMPFC) can be examined via resting-state functional connectivity (RSFC) (Grabenhorst and Rolls, 2011). Hence, based on goal conflict and hedonic-inhibitory models of eating, REs were expected to show
comparatively reduced functional connectivity between the DLPFC
and reward-based value regions (e.g., VMPFC). Finally, in light of
links between RE and BN symptomatology, we hypothesized that
areas having lower inter-hemispheric functional connectivity and
altered functional connectivity, especially within inhibitory control
regions (i.e., DLPFC) would correspond to overall BN symptom
elevations among REs relative to UREs.
2. Methods and materials
2.1. Participants
Participants were 47 undergraduate women from Southwest
University (SWU), Chongqing, who comprised URE (n ¼24) and RE
(n ¼23) subgroups. Participants were selected from Restraint Scale
(RS) scores in a prescreening procedure administered to 107 volunteers two weeks before the imaging study. Only women were
recruited because men and women differ in how and why they
gain and lose their weight (Holm-Denoma et al., 2008), and
average weight women are much more likely to diet than are
average weight men (van Strien et al., 2014). RE and URE group
membership was based on RS scores above 15 or below 12, respectively, following other published accounts (Demos et al., 2011;
Dong et al., 2014). Open-ended queries assessed exclusion criteria
including current neurological disease (i.e., central and peripheral
nervous system diseases such as epilepsy, migraine and other
headache disorders, multiple sclerosis or brain trauma), and a
formal eating disorder diagnosis or a history of such concerns.
Obese participants (BMIZ 30 kg/m2) were also excluded because
of potential neuro-anatomical differences based on BMI (Gunstad
et al., 2008). Smokers, alcohol users and women taking medication
known to affect fMRI signals were also excluded.
2.2. Procedure
The research was approved by the SWU human research ethics
committee. All participants provided written, informed consent
prior to participation. Eligible women were instructed to fast
overnight following their evening meal and to refrain from eating
and drinking any liquids except water before their scan.
All scans were conducted between 9:30 and 11:30 a.m. Upon
arrival, fasting status and hunger were confirmed by self-reports.
During the scan, participants were instructed to keep their eyes
closed, not to think of anything in particular and not to fall asleep.
Participants completed a demographics questionnaire following
the scan, as well as measures of BN symptoms and mood. This
24
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
sequence was followed because RS-fMRI signals can be modulated
by antecedent events (Albert et al., 2009). After finishing all tasks,
each volunteer received 60 Yuan (equivalent to about $10 USD) for
participation.
2.3. Measures
Demographics items included age, height, weight, smoking
status, alcohol using, current medications, and phase of menstrual
cycle. Dieting and weight history items included “What is the most
you have ever weighed since reaching your current height?” and
“In your lifetime, how many times have you lost 5 kg or more?” as
well as queries about having been diagnosed with a clinical eating
disorder (Coletta et al., 2009). Participants also completed selfreport measures of affect, restrained eating, and bulimic symptomatology that had been back-translated and validated for use in
Chinese samples.
Hunger ratings. Participants were asked to rate the current
feelings of hunger on 100-mm visual analog scale (VAS), ranging
from 0 (‘not at all hungry’) to 100 (‘very hungry’).
Body Mass Index. BMI (kg/m2) was calculated from objective
measures of height and weight. Specifically, after the removal of
shoes and coats, height was measured to the nearest millimeter
using a stadiometer and weight was assessed to nearest 0.1 kg
using a digital scale.
Restraint Scale (RS; Herman and Polivy, 1980). The 10-item
Revised RS measures degree of eating restraint and reflects chronic
concerns with weight and food intake restriction as a means of
weight control. RS items were rated on four- (0–3) and 5-point (0–
4) frequency scales with higher ratings reflecting more RE (Polivy
and Herman, 1999). In Chinese college student samples, support
has been found for the original RS factor structure, internal consistency and convergent validity (Kong et al., 2013). In this study,
alpha coefficients were α ¼0.81 for REs and α ¼0.78 for UREs.
Positive and Negative Affect Scale – Chinese (Jackson and Chen,
2008). The Positive Affect and Negative Affect Schedule (PANAS)
assesses 20 affective states (e.g., distressed, excited) experienced
in the last month from 1 ¼never or little of the time to 4 ¼most of
the time. The original PANAS factor structure (Watson et al., 1988)
was replicated in Chinese samples (Jackson and Chen, 2008), except that “Alert” loaded with the 10 original NA items, not Positive
Affect. Studies of Chinese males and females reported internal
consistency coefficients above α ¼ 0.80 and high levels of convergent validity with related constructs (e.g., Jackson and Chen,
2008, 2010, 2014). For REs, alpha coefficients were α ¼0.92 for PA
and, α ¼0.91 for NA. Among UREs, alphas were α ¼0.86 and
α ¼0.92 for PA and NA, respectively.
Eating Disorder Inventory-Bulimia (EDI-B; Garner et al., 1983).
This 7 item scale assess BN symptoms including binge eating
episodes accompanied by a perceived loss of control and inappropriate compensatory behaviors (i.e., laxatives, diet pills, excessive exercise) between 1 (never) and 6 (always), with higher
scores reflecting more overall BN symptomatology. Previous research with Chinese samples replicated the original factor structure and reported the scale has adequate reliability and construct
validity (Lee et al., 1997). In this study, alphas were α ¼0.82 for REs
and α ¼0.84 for UREs.
2.4. MRI data acquisition
RS-fMRI data were acquired with a 3.0 T Siemens Tim Trio
whole-body MRI system (Siemens Medical Solutions, Erlangen,
Germany). Foam pads were used to reduce head movements and
scanner noise. To optimize functional sensitivity in the VMPFC, a
key region of interest, images were acquired using an oblique orientation of 30° to the anterior commissure–posterior commissure
line (Hutcherson et al., 2012). In addition, we used an eightchannel phased array coil which yields a 40% increase in VMPFC
signals over a standard head coil. Scans were performed with an
echo-planar imaging (EPI) sequence using the following parameters: repetition time ¼2000 ms, echo time ¼30 ms, flip
angle ¼90°,
field
of
view¼192 192 mm2,
acquisition
matrix¼ 64 64, in-plane resolution ¼3 3 mm2, 32 interleaved
3 mm-thick slices, inter-slice skip ¼0.99 mm. For each participant,
242 EPI functional volumes were collected. Subsequently, highresolution T1-weighted anatomical images were obtained with the
application of a magnetization-prepared rapid gradient echo
(MPRAGE) sequence (repetition time (TR) ¼1900 ms; echo time
(TE)¼ 2.52 ms; inversion time (TI) ¼900 ms; flip angle ¼ 9°; resolution matrix ¼256 256; slices ¼176; thickness ¼1.0 mm;
voxel size ¼1 1 1 mm3).
2.5. Data preprocessing
Data preprocessing was conducted using the Data Processing
Assistant for Resting-State fMRI V2.0 (DPARSF, ⟨http://www.rest
fmri.net/forum/DPARSF⟩) (Chao-Gan and Yu-Feng, 2010). Following other published accounts (Tian et al., 2012), preprocessing
included discarding the first ten volumes, slice timing, head motion correction, spatial normalization in Montreal Neurological
Institute (MNI) space using the transformation parameters estimated using unified segmentation algorithm and a re-sampled
voxel size of 3 3 3 mm3, smoothing with a Gaussian kernel of
6 mm at full-width at half-maximum (FWHM), linear trend removal, and filtering (e.g., 0.01–0.08 Hz). Finally, 6 head motion
parameters, white matter, CSF and whole brain signals were regressed out before VMHC and FC computation.
2.6. Voxel-mirrored homotopic connectivity
To account for geometric differences between hemispheres, we
refined the registration from individual anatomic to Montreal
Neurological Institute (MNI) 152 template space using a groupspecific symmetrical template. All 47 registered structural images
were averaged to create a mean image, which was then averaged
with its left–right mirror to generate a group-specific symmetrical
template. Nonlinear registration to this symmetrical template was
performed for each participant, and the resultant transformation
was applied to each participant's preprocessed functional data
based on Tian, et al. (2012).
VMHC computation was performed using DPARSF software. For
each woman, homotopic RSFC was computed as the Fisher Ztransformed Pearson correlation between the time series of every
pair of symmetrical inter-hemispheric voxels. Resultant values
constituted the VMHC and were used for analyses. To test for regional group differences in VMHC, individual-level VHMC maps
were entered into a group-level voxel-wise t test. The T-map was
set at a corrected significance level of p o0.05 (combined individual voxel height threshold of po 0.005 and a cluster
size41215 mm3 (45 voxels)), using AlphaSim in REST software
(REST, ⟨www.restfmri.net⟩) (Song et al., 2011). The following
parameters were used: individual voxel p-Value ¼ 0.001, 10,000
simulations, 2-sided, FWHM estimated by 3dFWHMx, with a
unilateral hemispheric brain mask. Age, BMI, fasting time, hunger
level and menstrual cycle phase (Table 1) were treated as covariates given that each may induce potentially subtle effects on image
data.
2.7. Structural image analysis
To exclude the influence of structural damage to VMHC measures, possible abnormalities in gray-matter volume (GMV) of
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
mask including 70,831 re-sampled voxels). Covariates were identical to those used in the VMHC section.
Table 1
Demographic, physiological and psychological data.
Variable
REs (N ¼23)
UREs (N ¼ 24)
M SD
M SD
2.9. Brain–behavior relationships
t-value
12.39***
Restraint Scale
19.01(3.50)
7.96(2.56)
20.74(1.51)
21.04(1.85)
21.09(2.38)
21.01(2.50)
14.00(2.29)
14.00(2.36)
70.58(13.57)
65.80(15.78)
21.83(10.45)
21.42(9.46)
24.82(7.52)
25.48(5.44)
22.96(7.76)
21.50(7.81)
Age (years)
0.61
0.11
Body Mass Index
Fasting time (h)
0.001
Hunger
1.11
Menstrual Phase
0.14
Positive Affect
Correlations between mean VMHC, RSFC of identified regions
and EDI-B scores were computed within each group to evaluate
relations between differences in spontaneous brain activity and
bulimic tendencies. First, we explored relations between EDI-B
scores and mean prefrontal inter-hemispheric RSFC within a 4mm-radius sphere centered on the peak of the VMHC group difference, after regressing out covariates above from both RSFC data
and EDI-B scores. Second, we examined cluster-wise association
between EDI-B and mean RSFC within a 4-mm-radius sphere
centered on the peak of FC group differences.
0.35
Negative Affect
0.64
Binge eating
5.46
19.39(5.06)
12.67(3.22)
p o 0.001.
regions showing altered VMHC were assessed. Voxel-based morphometry (VBM) was performed to compute GMV of each participant using DPARSF software. Briefly, individual T1 images were
co-registered to mean functional images after head-motion correction. Transformed images were segmented into gray matter,
white matter, and cerebrospinal fluid and were then normalized to
the MNI space using a unified segmentation algorithm. Next registered gray-matter images were modulated and smoothed with
a 10-mm Gaussian kernel. We extracted mean GMV values in regions showing RE versus URE group differences in VMHC.
2.8. Seed-Based RSFC
RSFC associated with areas exhibiting significantly different
VMHC between groups was assessed by computing whole-brain
voxel-wise correlations associated with mean time series derived
separately for two regions of interest (ROIs), comprising all voxels
within the left and right seed DLPFC exhibiting lower VMHC for
REs than UREs. Fisher Z-transformed correlation maps were entered into a group-level voxel-wise t-test analysis. The T-map was
set at a corrected significance level of p o0.05 (combined individual voxel height threshold of p o0.005 and a cluster
size4 972 mm3 (36 voxels)), using AlphaSim in REST software
(parameters were: individual voxel p-value ¼0.01, 10,000 simulations, 2 sided, FWHM estimated by 3dFWHMx, with a whole brain
L
3. Results
***
REs: restrained eaters; UREs: unrestrained eaters.
nnn
25
3.1. Sample characteristics
REs and UREs did not differ on age, BMI, fasting time, menstrual
cycle phase, or affect levels (Table 1). However, supporting the
integrity of the samples, REs had significantly higher RS and EDI-B
scores than UREs did.
3.2. Voxel-mirrored homotopic connectivity
RE group differences in VMHC, controlling for age, BMI, fasting
time, menstrual cycle phase and affect were revealed, as shown in
Fig. 1 and Table 2. Specifically, REs showed reduced VMHC in the
DLPFC compared to UREs. However, no significant GMV changes
were found among REs in regions showing altered VMHC (t¼ 1.14,
df ¼45, p ¼0.26).
Given controversy regarding treatment of the global signal as a
covariate (e.g., Murphy et al., 2009), we recalculated the VMHC
without controlling for the global signal. Previously significant
VMHC differences in the DLPFC remained, although there was
minimal change in the peak point (t ¼62, df ¼ 45, p o0.005, Peak ¼
(718, 51, 24)).
3.3. Seed-Based RSFC
Whole-brain RSFC associated with two ROIs (i.e., the right and
left hemisphere DLPFC shown in Fig. 1) having significant VMHC
differences were also assessed. Consistent with VMHC results, REs
exhibited lower RSFC between the right DLPFC seed and left
DLPFC, VMPFC, and posterior cingulate cortex (PCC) as reported in
Fig. 2 and Table 2. Conversely, group differences in the left DLPFC
T value
R
DLPFC
MNI (X=-17, Y=51, Z=29)
Fig. 1. VMHC map of significant differences between REs and UREs (po 0.05, AlphaSim corrected). Letters L and R correspond to left and right sides of brain, respectively.
DLPFC: dorsolateral prefrontal cortex; VMHC: voxel-mirrored homotopic connectivity; REs: restrained eaters; UREs: unrestrained eaters.
26
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
Table 2
Significant group differences in VMHC and Seed-Based RSFC.
Cluster location
Peak (MNI)
x
VMHC
REso UREs
DLPFC
Seed-Based RSFC:
Right DLPFC
REso UREs
Left VMPFC
Left PCC
Right PCC
Left DLPFC
y
z
Cluster size (mm3)
T
718
51
27
1620
4.78
3
3
4
9
42
57
57
54
12
23
24
27
3348
1620
1539
4887
5.23
4.06
3.98
4.88
p o0.05 corrected.
VMHC: voxel-mirrored homotopic connectivity; RSFC: resting-state functional
connectivity; DLPFC: dorsolateral prefrontal cortex; PCC: posterior cingulate cortex;
VMPFC: ventromedial prefrontal cortex.
RSFC were not significant based on AlphaSim multiple correlations
(with a combined individual voxel height threshold of p o0.005
and a cluster size 4972 mm3). However, when the analysis was rerun without covarying the global signal RSFC results for each ROI
did not attain statistical significance.
3.4. Brain–behavior relationships
Correlations between mean VMHC, RSFC of identified regions,
and EDI-B scores were computed within each group to evaluate
relations between spontaneous brain activity differences and BN
symptoms. Among the REs, a significant negative correlation
R
(A)
between EDI-B scores and mean VMHC was found within a
spherical 4-mm-radius ROI centered on the VMHC group difference peak (reported in Fig. 2, Table 2, r¼ 0.583, n¼ 23, p ¼0.018);
this relation suggested that weaker prefrontal inter-hemispheric
RSFC was associated with higher BN symptom levels (Fig. 3). In
contrast, corresponding correlations above were not significant
among UREs (r ¼0.053, n ¼24, p ¼0.80). Fig. 3 indicates that EDI-B
scores also had a significant positive association (r¼ 0.658, n ¼23,
p¼ 0.006) with RSFC between the right DLPFC and left VMPFC (4mm-radius, ROI) in the RE group but not the URE group (r ¼0.276,
n¼ 24, p ¼0.202).
To test group differences in associations between EDI-B scores
and brain activity further, multiple regression analyses with
“group”, “EDI-B scores” and “group ╳EDI-B scores” as predictors
were performed using “Interaction!” ⟨http://www.danielsoper.
com/Interaction/⟩. Fig. 4 illustrates the significant interaction effect
for the correlation between EDI-B scores and VMHC in the DLPFC
[F(43) ¼11.08, po 0.001, effect size ¼0.77, 95% CI ¼0.0078–0.0405]
and highlights the moderating effect of RE subgroup membership
on the relation between EDI-B scores and brain activity.
4. Discussion
To the best of our knowledge, this study is the first to explore
relations of inter-hemispheric functional connectivity measured
by VMHC with BN symptomatology among REs. By and large,
findings were in line with hypotheses. Specifically, REs had reduced inter-hemispheric RSFC in the DLPFC region associated with
inhibitory control (Cohen and Lieberman., 2010; Heatherton and
Wagner., 2011) as well as reduced functional connectivity strength
L
50.53
T
3.79
-3.79
-11.36
43.35
(B)
T
3.77
-3.77
-16.99
(C)
-2.95
T
-5.35
Fig. 2. (A, B) Group-level voxel-mirrored homotopic connectivity (VMHC) for REs (A) and UREs (B) (po 0.05, clusters450, FDR corrected). (C) Seed-Based RSFC map of
significant differences between REs and UREs (p o 0.05, corrected). Letters L and R correspond to left and right sides of brain, respectively. RSFC: resting-state function
connectivity.
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
r=-0.583
p=0.018
27
r=0.658
p=0.006
Fig. 3. Scatterplots showed the correlation of VMHC and Seed-Based RSFC values with bulimic tendencies among REs group. The left plot illustrates a significant negative
correlation between VMHC values in the DLPFC and the binge eating tendencies (EDI-B scores). The right plot illustrates a significant positive correlation of FC between the
right DLPFC and the left VMPFC with bulimic tendencies (EDI-B scores). FC: functional connectivity.
Fig. 4. Moderating effect of group on relations between EDI-B scores and brain activity.
between the DLPFC and areas implicated in attention and reward
valuation, i.e., the VMPFC and PCC (Hare et al., 2011; Marsh et al.,
2011). Most crucially, among REs the lower inter-hemispheric
functional connectivity in the DLPFC was associated with elevated
BN symptoms while no such relation was observed among UREs.
Finally, REs having higher levels of BN symptomatology displayed
stronger functional connectivity between the DLPFC and VMPFC.
Consistent with previous studies (e.g., Stice, 2001), RE had a
positive association with BN symptoms in this study. Among REs,
this link corresponded to a significant reduction of inter-hemispheric functional connectivity in the DLPFC. The DLPFC has a vital
role in inhibiting hedonic eating and top-down control of appetite,
which facilitates flexible pursuit of long-term goals, such being
thin and maintaining health (Brooks et al., 2013; Jasinska et al.,
2011). Past studies have found prefrontal asymmetry is linked with
reward approach motivation, increased sensitivity to food reward,
and disinhibited eating among overweight and obese binge eating
groups (Ely et al., 2013; Karhunen et al., 2000; Ochner et al., 2009;
Sutton and Davidson, 1997). Moreover, REs who have lower DLPFC
inter-hemispheric functional connectivity reported more BN
pathology.
According to the goal conflict model (Stroebe et al., 2013), difficulties REs have in regulating food intake result from incompatible goals of eating enjoyment versus weight control.
Consequently, inter-hemispheric functional connectivity deficits in
prefrontal inhibitory circuits might reflect a stronger drive towards
the immediate feeding reward goal that portends to appetite
disinhibition and binge-eating and overrides less immediate
weight control goals.
In this study, REs showed weaker functional connectivity between the right DLPFC and left VMPFC relative to UREs. Numerous
neuroimaging studies have suggested that VMPFC signals are involved in reward-value computations reflecting immediate outcomes that influence subsequent goal-directed decision-making
related to appetite stimulation (Buckley et al., 2014; Grabenhorst
and Rolls, 2011; Jasinska et al., 2011). In fact, researchers have
posited that the DLPFC modulates stimulus value in the VMPFC via
the inferior frontal gyrus (IFG), especially for successful dieters
(Hare et al., 2009; Jasinska et al., 2011). Based on the hedonic-inhibitory model (Appelhans, 2009), overeating of palatable food
could arise when the VMPFC-mediated appetitive motivation
system overrides the DLPFC-mediated inhibitory system, particularly among REs who may be more sensitive to taste and hedonic
attributes of food cues. Thus, reduced functional connectivity
among REs may reflect impaired top-down inhibitory processing
as well as enhanced bottom-up processing that corresponds to a
reduced capacity to resist appetizing immediate rewards and BN
symptoms including binge-eating accompanied by a perceived loss
of control.
Regarding reduced right DLPFC-PCC functional connectivity
among REs compared to UREs, the PCC has been implicated in
attention to internal and external cues in addition to computation
of values related to appetite cues (Kullmann et al., 2012; Lavagnino
et al., 2014; Lips et al., 2014). The PCC may also serve as a neural
28
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
interface between motivation and spatial attention, and is involved in allocating neural resources based on the input of other
systems including the motivational salience of visual targets such
as, food stimuli (Mohanty et al., 2008; Tregellas et al., 2011). This
finding may highlight one neural basis of attention biases toward
food cues among REs (e.g., Hollitt et al., 2010; Veenstra et al.,
2010).
A recent meta-analysis concluded PCC regions show co-activation with different VMPFC sub-regions implicated in subjective
value computations (Clithero and Rangel, 2013) and appetite
processing in groups having obesity or BN (Joos et al., 2011; Marsh
et al., 2011). Therefore, altered functional connectivity between
the right DLPFC and PCC may correspond to BN symptomatology
owing to an imbalance between inhibitory control and attentionbased value processing among REs.
Given controversies about the global signal as predominantly
neuronal or non-neuronal, VMHC and Seed-Based RSFC were recalculated without controlling for the global signal. Significant
VMHC differences were retained, suggesting these results were
robust under different analysis conditions. However, Seed-Based
RSFC did not attain statistical significance when the global signal
was not covaried. The global signal comprises (1) a component
that reflects the inter-regional correlations between resting-state
vectors and (2) an additive global signal confound component (He
and Liu, 2012) that may correspond to respiration-induced fMRI
signals and should be controlled (potentially noise signal) (Birn
et al., 2006). An ideal global signal correction method would remove the additive signal confound while preserving the desired
component. Some authors contend that global signal regression
may bias correlation patterns and should not be used when
studying RS-fMRI (Murphy et al., 2009; Saad et al., 2012). Others
contend that biased correlation patterns cannot be explained solely as a consequence of using global signal correction within
preprocessing and may have a biological basis (Chai et al., 2012;
Fox et al., 2009). In recently published research identifying the
quality of the outcome data based on different processing pipelines, global signal regression significantly increased signal–noise
separation and did not affect the classification of clinical patients
versus controls (Shirer et al., 2015). While this research was not
designed to address the controversy, variable RSFC finding highlighted how treatment of the global signal in analyses can influence findings and warrant consideration in interpretations of
variable results.
Together, findings suggested that REs have altered functional
coordination in prefrontal inhibitory regions between the two
hemispheres and their functional coupling with reward valuedcomputational regions (i.e, VMPFC, PCC) compared with UREs,
independent of GMV changes. More critically, altered VMHC in
inhibitory regions and disrupted functional connectivity between
the DLPFC and VMPFC are associated with elevations in BN
symptoms among REs but not UREs. These results are consistent
with previous studies reporting reduced activation in frontal inhibitory regions and increased activation within reward and attention areas (Batterink et al., 2010; Dong et al., 2014; Stice et al.,
2013). Results underscore how homotopic functional coordination
in appetite inhibitory regions and its functional connectivity with
reward related regions has a vital role in controlling appetite impulsivity and integrating health-based and taste value signals in
the process of dietary decision-making. Possibly, impaired DLPFC
inter-hemispheric functional connectivity and disrupted functional interactions with reward valued-computational regions
among REs increases risk for BN pathology (Stice and Agras, 1998),
impulsive eating (van Koningsbruggen et al., 2013) and weight
gain and help to explain why prolonged food deprivation is typically an unsuccessful weight loss strategy (Stice, 2002; Stroebe
et al., 2013).
Owing to the brain's neural plasticity (Pascual-Leone et al.,
2005), RS-fMRI has strong associations with individual difference
characteristics and provides possible insights for assessing intervention effects. For example, effects of altering neural mechanisms
and behavior on food-intake regulation have been considered in
recent work (Cornier et al., 2012; Evero et al., 2012). McFadden
et al. (2013) found a 6-month exercise intervention simultaneously
decreased both default mode network activity and hunger ratings.
RS-fMRI research may also clarify potential benefits of enhancing
DLPFC activity in concert with neuro-modulation interventions. In
one recent study, repeated transcranial direct current stimulation
(tDCS) effectively reduced overall caloric intake and appetite
among healthy young men (Jauch-Chara et al., 2014). Extensions
that assess effects of cognitive behavioral interventions and/or
neuro-modulation on spontaneous activity patterns and behavior
outcomes in at-risk clinical groups over extended follow-up intervals is an important focus for future work.
Notwithstanding its implications, the current study has several
limitations that should be noted. First, the sample size was quite
small, though larger than samples in some RS-fMRI studies (García‐García et al. 2013; Kullmann et al., 2012); recruitment of larger
samples would increase statistical power and the capacity to detect more subtle relations. Second, the extent to which findings
generalize to samples in other countries such as the U.S. is unclear,
given that Chinese BN samples are comparatively less likely than
their U.S. counterparts to report vomiting and laxative use as
compensatory strategies following binge-eating episodes (Chen
and Jackson, 2008; Jackson and Chen, 2010). Third, although the
assessment of VMHC differences between general RE and URE
subgroups was a sensible initial line of investigation, REs have
been divided further into successful and unsuccessful subgroups
(Meule et al., 2012; van Koningsbruggen et al., 2011). Given clear
differences between general groups of REs and UREs, a logical
focus of extensions is assessment of VMHC differences between
these RE subgroups and UREs. Finally, findings provide important
foundations for justifying more costly longitudinal studies that
evaluate the development and course of neural changes underlying RE (Dong et al., 2015).
5. Conclusion
In conclusion, this study identified neural links between RE and
homotopic RSFC following food deprivation. Reduced DLPFC interhemispheric functional connectivity was found among REs and
accompanied by decreased functional connectivity with the
VMPFC and PCC. Moreover, a significant negative correlation between BN symptoms and VMHC in the DLPFC suggests that reduced inter-hemispheric functional connectivity in inhibitory regions is a possible risk factor for bulimic pathology among REs.
Disrupted functional connectivity between the DLPFC and VMPFC
also corresponded to BN symptoms among REs. Following from
goal conflict and hedonic-inhibitory models of feeding, impaired
inter-hemispheric functional coordination in inhibitory control
regions and decreased functional connectivity with reward value
regions are potential neural bases related to hedonically-motivated overeating and BN pathology among food-deprived REs.
Conflict of interest
The authors report no conflicts of interest related to this
research.
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
Acknowledgments
This research was supported by National Natural Science
Foundation of China Grants (31170981 and 31371037), the Science
and Technology Innovation Fund for SWU graduate students
(kb2011001), and BaYu Scholar and Chongqing 100 Persons Fellowship Programs of the Chongqing Government.
References
Ahern, A.L., Field, M., Yokum, S., Bohon, C., Stice, E., 2010. Relation of dietary restraint scores to cognitive biases and reward sensitivity. Appetite 55, 61–68.
Albert, N.B., Robertson, E.M., Miall, R.C., 2009. The resting human brain and motor
learning. Curr. Biol. 19, 1023–1027.
Allison, D.B., 2009. Handbook of Assessment Methods for Eating Behaviors and
Weight-related Problems: Measures, Theory, and Research. Sage, USA.
Anderson, J.S., Druzgal, T.J., Froehlich, A., DuBray, M.B., Lange, N., Alexander, A.L.,
Abildskov, T., Nielsen, J.A., Cariello, A.N., Cooperrider, J.R., Bigler, E.D., Lainhart, J.
E., 2011. Decreased interhemispheric functional connectivity in autism. Cereb.
Cortex 21, 1134–1146.
Andreyeva, T., Long, M.W., Henderson, K.E., Grode, G.M., 2010. Trying to lose
weight: diet strategies among Americans with overweight or obesity in 1996
and 2003. J. Am. Diet. Assoc. 110, 535–542.
Appelhans, B.M., 2009. Neurobehavioral Inhibition of Reward‐driven Feeding: Implications for Dieting and Obesity. Obesity 17, 640–647.
Appelhans, B.M., Woolf, K., Pagoto, S.L., Schneider, K.L., Whited, M.C., Liebman, R.,
2011. Inhibiting food reward: delay discounting, food reward sensitivity, and
palatable food intake in overweight and obese women. Obesity 19, 2175–2182.
Batterink, L., Yokum, S., Stice, E., 2010. Body mass correlates inversely with inhibitory control in response to food among adolescent girls: an fMRI study.
Neuroimage 52, 1696–1703.
Benau, E.M., Orloff, N.C., Janke, E.A., Serpell, L., Timko, C.A., 2014. A systematic review of the effects of experimental fasting on cognition. Appetite 77, 52–61.
Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity
in the motor cortex of resting human brain using echo‐planar MRI. Magn. Reson. Med. 34, 537–541.
Brooks, S.J., Cedernaes, J., Schioth, H.B., 2013. Increased prefrontal and parahippocampal activation with reduced dorsolateral prefrontal and insular cortex
activation to food images in obesity: a meta-analysis of fMRI studies. PLoS One
8, e60393.
Buckley, J., Cohen, J.D., Kramer, A.F., McAuley, E., Mullen, S.P., 2014. Cognitive
control in the self-regulation of physical activity and sedentary behavior. Front.
Hum. Neurosci. 8, 747.
Burger, K.S., Stice, E., 2011. Relation of dietary restraint scores to activation of reward-related brain regions in response to food intake, anticipated intake, and
food pictures. Neuroimage 55, 233–239.
Chai, X.J., Castañón, A.N., Öngür, D., Whitfield-Gabrieli, S., 2012. Anticorrelations in
resting state networks without global signal regression. Neuroimage 59,
1420–1428.
Chao-Gan, Y., Yu-Feng, Z., 2010. DPARSF: a MATLAB toolbox for “pipeline” data
analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 13.
Chen, H., Jackson, T., 2008. Prevalence and sociodemographic correlates of eating
disorder endorsements among adolescents and young adults from China. Eur.
Eat. Disord. Rev. 16, 375–385.
Clithero, J.A., Rangel, A., 2013. Informatic parcellation of the network involved in
the computation of subjective value. Soc. Cogn. Affect. Neurosci. 9, 1289–1302.
Cohen, J.R., Lieberman, M.D., 2010. The Common Neural Basis of Exerting Selfcontrol in Multiple Domains. Self Control in Society, Mind, and Brain, pp.
141–162.
Coletta, M., Platek, S., Mohamed, F.B., van Steenburgh, J.J., Green, D., Lowe, M.R.,
2009. Brain activation in restrained and unrestrained eaters: an fMRI study. J.
Abnorm. Psychol. 118, 598–609.
Cornier, M.-A., Melanson, E.L., Salzberg, A.K., Bechtell, J.L., Tregellas, J.R., 2012. The
effects of exercise on the neuronal response to food cues. Physiol. Behav. 105
(4), 1028–1034.
Dansinger, M.L., Tatsioni, A., Wong, J.B., Chung, M., Balk, E.M., 2007. Meta-analysis:
the effect of dietary counseling for weight loss. Ann. Intern. Med. 147, 41–50.
DelParigi, A., 2010. Neurofunctional markers of successful dieting. Brain Res. Bull.
82, 1–2.
DelParigi, A., Chen, K., Salbe, A.D., Hill, J.O., Wing, R.R., Reiman, E.M., Tataranni, P.A.,
2007. Successful dieters have increased neural activity in cortical areas involved
in the control of behavior. Int. J. Obes. 31, 440–448.
Demos, K.E., Kelley, W.M., Heatherton, T.F., 2011. Dietary restraint violations influence reward responses in nucleus accumbens and amygdala. J. Cogn. Neurosci. 23, 1952–1963.
Dong, D., Lei, X., Jackson, T., Wang, Y., Su, Y., Chen, H., 2014. Altered regional
homogeneity and efficient response inhibition in restrained eaters. Neuroscience 266, 116–126.
Dong, D., Jackson, T., Wang, Y., Chen, H., 2015. Spontaneous regional brain activity
links restrained eating to later weight gain among young women. Biol. Psychol.
109, 176–183.
29
Ely, A.V., Childress, A.R., Jagannathan, K., Lowe, M.R., 2014. Differential reward response to palatable food cues in past and current dieters: A fMRI study. Obesity
22, E38–E45.
Ely, A.V., Winter, S., Lowe, M.R., 2013. The generation and inhibition of hedonicallydriven food intake: behavioral and neurophysiological determinants in healthy
weight individuals. Physiol. Behav. 121, 25–34.
Evero, N., Hackett, L.C., Clark, R.D., Phelan, S., Hagobian, T.A., 2012. Aerobic exercise
reduces neuronal responses in food reward brain regions. J. Appl. Physiol 112
(9), 1612–1619.
Fairburn, C.G., Brownell, K.D., 2005. Eating Disorders and Obesity: A Comprehensive
Handbook. Guilford Press.
Fedoroff, I., Polivy, J., Peter Herman, C., 2003. The specificity of restrained versus
unrestrained eaters’ responses to food cues: general desire to eat, or craving for
the cued food? Appetite 41, 7–13.
Fox, M.D., Zhang, D., Snyder, A.Z., Raichle, M.E., 2009. The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101,
3270–3283.
García‐García, I., Jurado, M.Á., Garolera, M., Segura, B., Sala‐Llonch, R., Marqués‐
Iturria, I., Pueyo, R., Sender‐Palacios, M.J., Vernet‐Vernet, M., Narberhaus, A.,
2013. Alterations of the salience network in obesity: a resting‐state fMRI study.
Hum. Brain Mapp. 34, 2786–2797.
Garner, D.M., Olmstead, M.P., Polivy, J., 1983. Development and validation of a
multidimensional eating disorder inventory for anorexia nervosa and bulimia.
Int. J. Eat. Disord. 2, 15–34.
Goldstone, A.P., Prechtl de Hernandez, C.G., Beaver, J.D., Muhammed, K., Croese, C.,
Bell, G., Durighel, G., Hughes, E., Waldman, A.D., Frost, G., 2009. Fasting biases
brain reward systems towards high‐calorie foods. Eur. J. Neurosci. 30,
1625–1635.
Grabenhorst, F., Rolls, E.T., 2011. Value, pleasure and choice in the ventral prefrontal
cortex. Trends Cogn. Sci. 15, 56–67.
Gunstad, J., Paul, R.H., Cohen, R.A., Tate, D.F., Spitznagel, M.B., Grieve, S., Gordon, E.,
2008. Relationship between body mass index and brain volume in healthy
adults. Int. J. Neurosci. 118, 1582–1593.
Hare, T.A., Camerer, C.F., Rangel, A., 2009. Self-control in decision-making involves
modulation of the vmPFC valuation system. Science 324, 646–648.
Hare, T.A., Malmaud, J., Rangel, A., 2011. Focusing attention on the health aspects of
foods changes value signals in vmPFC and improves dietary choice. J. Neurosci.
31, 11077–11087.
Harris, J.L., Bargh, J.A., Brownell, K.D., 2009. Priming effects of television food advertising on eating behavior. Health Psychol. 28, 404.
He, H., Liu, T.T., 2012. A geometric view of global signal confounds in resting-state
functional MRI. Neuroimage 59, 2339–2348.
Heatherton, T.F., Wagner, D.D., 2011. Cognitive neuroscience of self-regulation
failure. Trends Cogn. Sci. 15, 132–139.
Herman, C.P., Mack, D., 1975. Restrained and unrestrained eating. J. Pers. 43,
647–660.
Herman, C.P., Polivy, J., 1980. Restrained eating. In: Stunkard, A., Stellar, E. (Eds.),
Obesity. Saunders, Philadelphia, pp. 208–225.
Hollitt, S., Kemps, E., Tiggemann, M., Smeets, E., Mills, J.S., 2010. Components of
attentional bias for food cues among restrained eaters. Appetite 54, 309–313.
Holm-Denoma, J.M., Joiner Jr, T.E., Vohs, K.D., Heatherton, T.F., 2008. The” freshman
fifteen”(the” freshman five” actually): predictors and possible explanations.
Health Psychol. 27, S3.
Hutcherson, C.A., Plassmann, H., Gross, J.J., Rangel, A., 2012. Cognitive regulation
during decision making shifts behavioral control between ventromedial and
dorsolateral prefrontal value systems. J. Neurosci. 32, 13543–13554.
Jackson, T., Chen, H., 2014. Risk factors for disordered eating during early and
middle adolescence: a two year longitudinal study of Mainland Chinese girls
and boys. J. Abnorm. Child Psychol. 42, 791–802.
Jackson, T., Chen, H., 2010. Sociocultural experiences of bulimic and non-bulimic
adolescents in a school-based Chinese sample. J. Abnorm. Child Psychol. 38,
69–76.
Jackson, T., Chen, H., 2008. Predicting changes in eating disorder symptoms among
Chinese adolescents: a 9-month prospective study. J. Psychosom. Res. 64,
87–95.
Jasinska, A.J., Ramamoorthy, A., Crew, C.M., 2011. Toward a neurobiological model
of cue-induced self-control in decision making: relevance to addiction and
obesity. J. Neurosci. 31, 16139–16141.
Jasinska, A.J., Yasuda, M., Burant, C.F., Gregor, N., Khatri, S., Sweet, M., Falk, E.B.,
2012. Impulsivity and inhibitory control deficits are associated with unhealthy
eating in young adults. Appetite 59, 738–747.
Jauch-Chara, K., Kistenmacher, A., Herzog, N., Schwarz, M., Schweiger, U., Oltmanns,
K.M., 2014. Repetitive electric brain stimulation reduces food intake in humans.
Am. J. Clin. Nutr. 100, 1003–1009.
Jeffery, R.W., Epstein, L.H., Wilson, G.T., Drewnowski, A., Stunkard, A.J., Wing, R.R.,
2000. Long-term maintenance of weight loss: current status. Health Psychol.
19, 5.
Joos, A.A., Saum, B., Zeeck, A., Perlov, E., Glauche, V., Hartmann, A., Freyer, T.,
Sandholz, A., Unterbrink, T., Elst, L.T., 2011. Frontocingular dysfunction in bulimia nervosa when confronted with disease‐specific stimuli. Eur. Eat. Disord.
Rev. 19, 447–453.
Karhunen, L.J., Vanninen, E.J., Kuikka, J.T., Lappalainen, R.I., Tiihonen, J., Uusitupa, M.
I., 2000. Regional cerebral blood flow during exposure to food in obese binge
eating women. Psychiatry Res. 99, 29–42.
Kelly, C., Zuo, X.N., Gotimer, K., Cox, C.L., Lynch, L., Brock, D., Imperati, D., Garavan,
H., Rotrosen, J., Castellanos, F.X., Milham, M.P., 2011. Reduced interhemispheric
30
S. Chen et al. / Neuropsychologia 81 (2016) 22–30
resting state functional connectivity in cocaine addiction. Biol. Psychiatry 69,
684–692.
Kong, F., Zhang, Y., Chen, H., 2013. The construct validity of the Restraint Scale
among mainland Chinese women. Eat. Behav. 14, 356–360.
Kullmann, S., Heni, M., Veit, R., Ketterer, C., Schick, F., Haring, H.U., Fritsche, A.,
Preissl, H., 2012. The obese brain: association of body mass index and insulin
sensitivity with resting state network functional connectivity. Hum. Brain
Mapp. 33, 1052–1061.
Lavagnino, L., Amianto, F., D’Agata, F., Huang, Z., Mortara, P., Abbate-Daga, G.,
Marzola, E., Spalatro, A., Fassino, S., Northoff, G., 2014. Reduced resting-state
functional connectivity of the somatosensory cortex predicts psychopathological symptoms in women with bulimia nervosa. Front. Behav. Neurosci. 8, 270.
Lee, S., Lee, A.M., Leung, T., Yu, H., 1997. Psychometric properties of the eating
disorders inventory (EDI‐1) in a nonclinical Chinese population in Hong Kong.
Int. J. Eat. Disord. 21, 187–194.
Leidy, H.J., Lepping, R.J., Savage, C.R., Harris, C.T., 2011. Neural responses to visual
food stimuli after a normal vs. higher protein breakfast in breakfast‐skipping
teens: a pilot fMRI study. Obesity 19, 2019–2025.
Lindroos, A.K., Lissner, L., Mathiassen, M.E., Karlsson, J., Sullivan, M., Bengtsson, C.,
Sjöström, L., 1997. Dietary intake in relation to restrained eating, disinhibition,
and hunger in obese and nonobese Swedish women. Obes. Res. 5, 175–182.
Lips, M.A., Wijngaarden, M.A., van der Grond, J., van Buchem, M.A., de Groot, G.H.,
Rombouts, S.A., Pijl, H., Veer, I.M., 2014. Resting-state functional connectivity of
brain regions involved in cognitive control, motivation, and reward is enhanced
in obese females. Am. J. Clin. Nutr. 100, 524–531.
Lowe, M.R., 2002. Dietary Restraint and Overeating. Eating Disorders and Obesity: A
Comprehensive Handbook p. 88.
Marsh, R., Horga, G., Wang, Z., Wang, P., Klahr, K.W., Berner, L.A., Walsh, B.T., Peterson, B.S., 2011. An fMRI study of self-regulatory control and conflict resolution in adolescents with bulimia nervosa. Am. J. Psychiatry 168, 1210–1220.
McFadden, K.L., Cornier, M.A., Melanson, E.L., Bechtell, J.L., Tregellas, J.R., 2013. Effects of exercise on resting-state default mode and salience network activity in
overweight/obese adults. Neuroreport 24, 866–871.
Meule, A., Papies, E.K., Kubler, A., 2012. Differentiating between successful and
unsuccessful dieters. Validity and reliability of the Perceived Self-Regulatory
Success in Dieting Scale. Appetite 58, 822–826.
Mills, J.S., Palandra, A., 2008. Perceived caloric content of a preload and disinhibition among restrained eaters. Appetite 50, 240–245.
Mohanty, A., Gitelman, D.R., Small, D.M., Mesulam, M.M., 2008. The spatial attention network interacts with limbic and monoaminergic systems to modulate
motivation-induced attention shifts. Cereb. Cortex 18, 2604–2613.
Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., Bandettini, P.A., 2009. The
impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44, 893–905.
Nederkoorn, C., Houben, K., Hofmann, W., Roefs, A., Jansen, A., 2010. Control
yourself or just eat what you like? Weight gain over a year is predicted by an
interactive effect of response inhibition and implicit preference for snack foods.
Health Psychol. 29, 389.
Noel, X., Brevers, D., Bechara, A., 2013. A neurocognitive approach to understanding
the neurobiology of addiction. Curr. Opin. Neurobiol. 23, 632–638.
Ochner, C.N., Green, D., van Steenburgh, J.J., Kounios, J., Lowe, M.R., 2009. Asymmetric prefrontal cortex activation in relation to markers of overeating in obese
humans. Appetite 53, 44–49.
Pascual-Leone, A., Amedi, A., Fregni, F., Merabet, L.B., 2005. The plastic human brain
cortex. Annu. Rev. Neurosci. 28, 377–401.
Polivy, J., Herman, C.P., 1985. Dieting and binging: a causal analysis. Am. Psychol.
40, 193.
Polivy, J., Herman, C.P., 1999. Distress and eating: why do dieters overeat? Int. J. Eat.
Disorder. 26, 153–164.
Raichle, M.E., Mintun, M.A., 2006. Brain work and brain imaging. Annu. Rev. Neurosci. 29, 449–476.
Saad, Z.S., Gotts, S.J., Murphy, K., Chen, G., Jo, H.J., Martin, A., Cox, R.W., 2012.
Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2, 25–32.
Schonberg, T., Bakkour, A., Hover, A.M., Mumford, J.A., Poldrack, R.A., 2014. Influencing food choices by training: evidence for modulation of frontoparietal
control signals. J. Cogn. Neurosci. 26, 247–268.
Schur, E.A., Kleinhans, N.M., Goldberg, J., Buchwald, D.S., Polivy, J., Del Parigi, A.,
Maravilla, K.R., 2012. Acquired differences in brain responses among monozygotic twins discordant for restrained eating. Physiol. Behav. 105, 560–567.
Shirer, W.R., Jiang, H., Price, C.M., Ng, B., Greicius, M.D., 2015. Optimization of rsfMRI pre-processing for enhanced signal–noise separation, test–retest reliability, and group discrimination. Neuroimage 117, 67–79.
Silva, J.R., Pizzagalli, D.A., Larson, C.L., Jackson, D.C., Davidson, R.J., 2002. Frontal
brain asymmetry in restrained eaters. J. Abnorm. Psychol. 111, 676–681.
Song, X.-W., Dong, Z.-Y., Long, X.-Y., Li, S.-F., Zuo, X.-N., Zhu, C.-Z., He, Y., Yan, C.-G.,
Zang, Y.-F., 2011. REST: a toolkit for resting-state functional magnetic resonance
imaging data processing. PLoS One 6, e25031.
Stice, E., 2002. Risk and maintenance factors for eating pathology: a meta-analytic
review. Psychol. Bull. 128 (5), 825.
Stice, E., 2001. A prospective test of the dual-pathway model of bulimic pathology:
mediating effects of dieting and negative affect. J. Abnorm. Psychol. 110,
124–130.
Stice, E., Agras, W.S., 1998. Predicting onset and cessation of bulimic behaviors
during adolescence: a longitudinal grouping analysis. Behav. Ther. 29, 257–276.
Stice, E., Burger, K., Yokum, S., 2013. Caloric deprivation increases responsivity of
attention and reward brain regions to intake, anticipated intake, and images of
palatable foods. Neuroimage 67, 322–330.
Stroebe, W., van Koningsbruggen, G.M., Papies, E.K., Aarts, H., 2013. Why most
dieters fail but some succeed: a goal conflict model of eating behavior. Psychol.
Rev. 120, 110.
Sutton, S.K., Davidson, R.J., 1997. Prefrontal brain asymmetry: a biological substrate
of the behavioral approach and inhibition systems. Psychol. Sci. 8, 204–210.
Tian, L., Ren, J., Zang, Y., 2012. Regional homogeneity of resting state fMRI signals
predicts Stop signal task performance. Neuroimage 60, 539–544.
Tregellas, J.R., Wylie, K.P., Rojas, D.C., Tanabe, J., Martin, J., Kronberg, E., Cordes, D.,
Cornier, M.A., 2011. Altered default network activity in obesity. Obesity 19,
2316–2321.
van der Laan, L.N., de Ridder, D.T., Charbonnier, L., Viergever, M.A., Smeets, P.A.,
2014. Sweet lies: neural, visual, and behavioral measures reveal a lack of selfcontrol conflict during food choice in weight-concerned women. Front. Behav.
Neurosci. 8, 184.
van Koningsbruggen, G.M., Stroebe, W., Aarts, H., 2013. Successful restrained eating
and trait impulsiveness. Appetite 60, 81–84.
van Koningsbruggen, G.M., Stroebe, W., Papies, E.K., Aarts, H., 2011. Implementation
intentions as goal primes: Boosting self‐control in tempting environments. Eur.
J. Soc. Psychol. 41, 551–557.
Van Strien, T., Engels, R.C., Van Leeuwe, J., Snoek, H.M., 2005. The Stice model of
overeating: tests in clinical and non-clinical samples. Appetite 45, 205–213.
van Strien, T., Herman, C.P., Verheijden, M.W., 2014. Dietary restraint and body mass
change. A 3-year follow up study in a representative Dutch sample. Appetite 76,
44–49.
Veenstra, E.M., de Jong, P.J., Koster, E.H., Roefs, A., 2010. Attentional avoidance of
high-fat food in unsuccessful dieters. J. Behav. Ther. Exp. Psychiatry 41,
282–288.
Volkow, N., Wang, G., Fowler, J., Tomasi, D., Baler, R., 2012. Food and drug reward:
overlapping circuits in human obesity and addiction. Curr. Top. Behav. Neurosci., 1–24.
Volkow, N.D., Wang, G.-J., Telang, F., Fowler, J.S., Thanos, P.K., Logan, J., Alexoff, D.,
Ding, Y.-S., Wong, C., Ma, Y., 2008. Low dopamine striatal D2 receptors are
associated with prefrontal metabolism in obese subjects: possible contributing
factors. Neuroimage 42, 1537–1543.
Wang, D., Buckner, R.L., Liu, H., 2014. Functional specialization in the human brain
estimated by intrinsic hemispheric interaction. J. Neurosci. 34, 12341–12352.
Wang, L., Li, K., Zhang, Q.E., Zeng, Y.W., Jin, Z., Dai, W.J., Su, Y.A., Wang, G., Tan, Y.L.,
Yu, X., Si, T.M., 2013. Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting
state fMRI study. PLoS One 8, e60191.
Watson, D., Clark, L.A., Tellegen, A., 1988. Development and validation of brief
measures of positive and negative affect: The PANAS scales. J. Pers. Soc. Psychol.
54, 1063–1070.
Westenhoefer, J., Broeckmann, P., Münch, A.-K., Pudel, V., 1994. Cognitive control of
eating behavior and the disinhibition effect. Appetite 23, 27–41.
Zuo, X.N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D.S., Bangaru, S.,
Grzadzinski, R., Evans, A.C., Zang, Y.F., Castellanos, F.X., Milham, M.P., 2010.
Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J. Neurosci. 30,
15034–15043.