Neuroscience 266 (2014) 116–126

ALTERED REGIONAL HOMOGENEITY AND EFFICIENT RESPONSE INHIBITION IN RESTRAINED EATERS D. DONG, X. LEI, T. JACKSON, * Y. WANG, Y. SU AND H. CHEN *

among REs. Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China School of Psychology, Southwest University, Chongqing 400715, China

Key words: restrained eating, resting-state fMRI, dieting, reward, inhibition, attention.

Abstract—Restrained eaters (REs) characterized by less efficient response inhibition are at risk for future onset of binge eating and bulimic pathology. Previous imaging studies investigating REs have been based on task-related functional magnetic resonance imaging (fMRI) and little is known about resting-state neural activity underlying restrained eating. To illuminate this issue, we investigated resting-state fMRI differences between REs (n = 22) and unrestrained eaters (UREs) (n = 30) using regional homogeneity (ReHo) analysis, which measures the temporal synchronization of spontaneous fluctuations. Samples were equated on body mass index (BMI) and caloric deprivation levels (i.e., 14 ± 2.1 h since last evening meal) before undergoing fMRI. Correlation analyses were performed between the ReHo index of identified regions and response inhibition based on stop-signal reaction time (SSRT) within each sample. Compared with UREs, REs showed more ReHo in brain regions associated with food reward (i.e., orbitofrontal cortex (OFC), dorsal-lateral prefrontal cortex (dlPFC)), attention (i.e., lingual gyrus, cuneus, inferior parietal lobule) and somatosensory functioning (i.e., paracentral lobule, anterior insula). In addition, ReHo values for the left dlPFC and left anterior insula, respectively, were negatively and positively correlated with SSRT among REs but not UREs. In concert with previous studies, these results suggest altered local synchronization may help to explain why dieting to maintain or lose weight often fails or increases risk for binge eating

INTRODUCTION Restrained eating (RE) refers to intentional, sustained restriction of caloric intake to lose or maintain body weight (Herman and Mack, 1975; Wadden et al., 2002). Recent studies show that restrained eaters (REs) characterized by less efficient response inhibition or impulsivity are particularly vulnerable to overeating (Guerrieri et al., 2008, 2009; Jansen et al., 2009; Jasinska et al., 2012). Furthermore, theory and associated research (Polivy and Herman, 1985; Stice, 2001; Wertheim et al., 2001; Johnson and Wardle, 2005; Neumark-Sztainer et al., 2006; Stice et al., 2008a), have linked RE to increased risk for the onset and maintenance of binge eating and bulimia nervosa, disturbances that have significant negative implications for health and functioning (e.g., Dingemans et al., 2002; Araujo et al., 2010; Wade et al., 2012; Kessler et al., 2013). Because approximately 50% of adolescent girls and young women report engaging in dieting behaviors (Field et al., 2010), investigating potential neural bases of RE may help to clarify why dieting to lose or maintain weight so often fails (Hetherington et al., 2000; Sysko et al., 2007). Functional magnetic resonance imaging (fMRI) studies have revealed differences in the processing of food stimuli between REs and unrestrained eaters (UREs). Coletta et al. (2009) found that compared to UREs, REs show stronger activation in the orbitofrontal cortex (OFC) and dorsolateral prefrontal cortex (dlPFC) in response to food pictures when calorically deprived, although groups have similar activation patterns after consuming a meal. Burger and Stice (2011) found high scorers on the Restraint Scale (RS; Herman and Polivy, 1980) are also hyper-responsive in motivational and reward regions (right OFC, bilateral dlPFC) during food intake. Moreover, images of high-calorie, ‘‘fattening’’ foods may elicit stronger activation in the left amygdala, right thalamus, and occipital lobe (cuneus, lingual gyrus) in monozygotic RE twin pairs compared to URE identical twin pairs (Schur et al., 2012). Although

*Correspondence to: H. Chen and T. Jackson, Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing 400715, China. Tel: +86-23-6825-2234; fax: +8623-6836-3625 (T. Jackson). Tel: +86-23-6825-7975; fax: +86-236836-3625 (H. Chen). E-mail addresses: [email protected] (D. Dong), [email protected]. cn (X. Lei), [email protected] (T. Jackson), lingzhen1314gogo@ 163.com (Y. Wang), [email protected] (Y. Su), chenhg@swu. edu.cn (H. Chen). Abbreviations: BMI, body mass index; dlPFC, dorsolateral prefrontal cortex; EPI, echo-planar imaging; FDR, false discovery rate; fMRI, functional magnetic resonance imaging; MNI, Montreal Neurological Institute; MTG, middle temporal gyrus; NA, Negative Affect; OFC, orbitofrontal cortex; PA, Positive Affect; RE, restrained eater; ReHo, regional homogeneity; ROI, region of interest; rsfMRI, resting-state functional magnetic resonance imaging; RT, reaction time; SDT, stop delay time; SSD, stop-signal delay; SSRT, stop-signal reaction time; SST, stop-signal task; URE, unrestrained eater. http://dx.doi.org/10.1016/j.neuroscience.2014.01.062 0306-4522/Ó 2014 IBRO. Published by Elsevier Ltd. All rights reserved. 116

D. Dong et al. / Neuroscience 266 (2014) 116–126

neuroimaging research on RE is still in its infancy, these findings suggest RE is related to heightened responsiveness of brain reward circuitry to food cues that increases risk for binge eating and overeating (Stice et al., 2010; Stice and Presnell, 2010). To date, associated imaging studies have relied on task-based fMRI. Given that brain activity in a resting state consuming 95% of total brain energy reflects the brain’s baseline status (Biswal et al., 1995; Fox and Raichle, 2007), resting-state fMRI (rsfMRI) is a promising alternative route for identifying neural correlates of RE. Regional homogeneity (ReHo) analysis, a robust and reliable index (Zuo et al., 2013), can effectively evaluate resting-state brain activity (see Zang et al., 2004). Based on an underlying assumption that brain activity is more likely to occur in clusters than single voxels, ReHo is calculated using Kendall’s coefficient of concordance (KCC) (Kendall and Gibbons, 1990), which evaluates similarities between the time series of a given voxel and its nearest neighbors. As such, ReHo reflects the local coherence of spontaneous neuronal activity (Wu et al., 2009). Additionally, increased ReHo corresponds to increased local synchronization of local field potential signaling and vice versa (Wang et al., 2012). Although rsfMRI studies have examined intrinsic activity in related groups including the obese (Garcia-Garcia et al., 2012; Kullmann et al., 2012), to our knowledge, extensions have yet to investigate RE. Since task-based studies have consistently found increased activation differences in reward regions between RE and URE groups, differential activity within these areas in a resting state seems plausible. Furthermore, because RE is associated with attention biases toward food cues in some behavioral studies (Boon et al., 2000; Hollitt et al., 2010; Veenstra et al., 2010), it is possible that increased spontaneous activity differences between RE and URE groups are found in attention areas, such as cuneus, lingual gyrus, middle temporal gyrus (MTG) (Schur et al., 2012; Stice et al., 2013). Food deprivation correlates with activation in regions implicated in attention, reward, and motivation in response to food cues, such as the OFC, thalamus, parahippocampal gyrus, caudate, dlPFC, cerebellum, middle temporal gyrus (MTG), cuneus, lingual gyrus (Tataranni et al., 1999; Porubska et al., 2006; Goldstone et al., 2009; Siep et al., 2009; Stice et al., 2013). In particular, skipping breakfast increases brain activity that drives eating (Goldstone et al., 2009; Leidy et al., 2011, 2013) and leads to poor long-term success among those who want to control weight through caloric restriction (Dansinger et al., 2007). Given that RE is related to differences in reward processing and desire to maintain or lose weight through caloric restriction, evaluation of differences in spontaneous brain activity between REs and UREs following food deprivation would be an especially salient condition for examining rsfMRI. Inhibitory control – the ability to stop or suppress responses that are no longer required, inappropriate, or in conflict with current goals (Verbruggen and Logan, 2008) – may have a key role in maintaining a healthy

117

diet (Jasinska et al., 2012). Accumulating evidence suggests deficits in inhibitory control predict binge eating and overeating (see Waxman, 2009 for a review). Consistent with this view, several experiments indicate RE is associated with less efficient response inhibition (measured by the stop-signal task (SST)) and vulnerability to overeating (Guerrieri et al., 2008, 2009; Jansen et al., 2009; Jasinska et al., 2012). However, little is known about response inhibition capacities of RE and URE after skipping consecutive meals. Researchers have implicated the inferior frontal gyrus, dlPFC, medial prefrontal cortex (MPFC), insula in inhibition control (Verbruggen and Logan, 2008; Chambers et al., 2009; Swick et al., 2011; Tian et al., 2012), but relations between altered resting-state spontaneous activity and response inhibition have not been examined among REs. Consequently, the current study had two main goals. First, possible alterations of regional spontaneous activity patterns among REs and UREs were assessed using ReHo analysis within a food deprivation condition. Based on the literature reviewed above, we hypothesized that ReHo indices would differ between these groups in brain regions associated with food reward and attention. Specifically, REs were expected to show increased ReHo values in the thalamus, parahippocampal gyrus, caudate, OFC, dlPFC, MTG, and occipital lobe (cuneus, lingual gyrus). Second, we examine whether REs would also display altered regional spontaneous activity related to response inhibition relative to UREs based on SST performance. We expected response inhibition would show positive correlation with regional spontaneous activity of dlPFC and inferior frontal gyrus, and negative correlation with insula among REs but not UREs.

EXPERIMENTAL PROCEDURES Participants Participants were 52 undergraduate women from Southwest University (SWU), Chongqing, who comprised URE (n = 30) and RE (n = 22) groups. Respondents were selected on the basis of RS scores in a prescreening procedure administered to 107 volunteers two to three 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). Volunteers in the RE and URE groups were eligible to participate if they scored above 15 or lower than 12, respectively, on the RS following other published accounts (Trottier et al., 2005; Jarry et al., 2006; Demos et al., 2011). 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), eating disorder diagnosis, or binge eating disturbances or a history of these concerns. Obese participants (body mass index (BMI) P 30 kg/m2) were also excluded because of potential neuro-anatomical differences that vary as a

118

D. Dong et al. / Neuroscience 266 (2014) 116–126

Table 1. Restrained eating group differences on demographic, physiological and psychological indices

***

Variable

REs (N = 22)

UREs (N = 30)

t-Value

Restraint Scale Age (years) Body mass index Fasting time (h) Hunger1 Hunger2 Hunger3 Hunger4 Menstrual phase Positive Affect Negative Affect

18.86 (3.24) 20.82 (1.62) 21.05 (2.35) 14.00 (2.28) 71.00 (13.20) 71.23 (21.25) 118.64 (81.84) 32.55 (22.60) 1.77 (0.43) 24.64 (8.12) 22.31 (7.63)

7.90 (2.94) 21.03 (1.73) 20.90 (2.28) 14.00 (2.17) 65.30 (16.95) 62.73 (23.30) 106.67 (85.14) 33.23 (18.52) 1.90 (0.31) 26.62 (5.32) 21.60 (8.03)

12.72*** 0.45 0.23 0.001 1.31 1.35 0.51 0.12 1.84 1.05 1.34

p < .001.

function of BMI (Gunstad et al., 2008). Smokers and women taking medication known to affect fMRI signals were also excluded. Table 1 summarizes characteristics of each group. Supporting the integrity of the samples, REs and UREs differed on RS scores but not measures of demographics, mood, hunger, or pre-imaging fasting duration. Procedure The research was approved by the human research ethics committee at SWU. All volunteers 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 all liquids except water before their scan. All scans were conducted between 9:30–11:30 AM. Fasting status was confirmed by self-reports upon arrival. Participants completed a demographics questionnaire, and then rated their hunger and mood on associated measures. Following rsfMRI data acquisition, the women were required to perform a SST described below. This sequence was followed because rsfMRI signals can be modulated by antecedent events (Albert et al., 2009; Yan et al., 2009). After finishing all tasks, each volunteer received 60 Yuan (equivalent to about $10USD) for participation. Measures Demographics assessed included age, height, weight, smoking status, 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 10 lbs or more?’’ as well as queries about having been diagnosed with an eating disorder. Binge eating was assessed with two items: ‘‘During the past 3 months, did you often eat an unusually large amount of food’’ and ‘‘During the times when you ate an unusually large amount of food, did you often feel you could not stop eating’’ (Coletta et al., 2009). Participants also completed a four-item measure of hunger and fullness (Lowe et al., 2000; Coletta et al., 2009). With the exception of the third item, responses were rated on 0–100 scale and included, (1) ‘‘How hungry do you feel right now?’’(ranging from ‘‘not at all’’

to ‘‘extremely’’); (2) ‘‘How strong is your desire to eat right now?’’ (ranging from ‘‘very weak’’ to ‘‘very strong’’); (3) ‘‘How much food do you think you could eat right now, taking rice as reference and grams as unit?’’; and (4) ‘‘How full does your stomach feel right now?’’ (ranging from ‘‘not at all full’’ to ‘‘very full’’). Positive and Negative Affect Scale – Chinese version (PANAS; Jackson and Chen, 2008). This scale includes 11 Negative Affect (NA) items and nine Positive Affect (PA) items. Respondents indicated the extent to which they experienced each emotion during the past 2 weeks on a five-point Likert scale between 1 = not at all and 5 = very much. For REs alpha coefficients were a = 0.90 for PA and, a = 0.93 for NA. Among UREs, alphas were a = 0.84 and a = 0.93 for PA and NA, respectively. Restraint Scale (RS; Herman and Polivy, 1980). The RS measured degree of eating restraint and identified women who were chronically concerned with their weight and restricting food intake as a means of weight control. Higher RS scores reflect more restrained eating and predict disinhibited eating in laboratory settings (Polivy and Herman, 1999). In this study, alpha coefficients were a = 0.79 for REs and a = 0.76 for UREs. Behavioral data acquisition The SST (Logan et al. (1997)) is a behavioral task used to measure impulsive behavior in the form of insufficient response inhibition. In this study, the task was adapted from ‘‘STOP-IT’’ (http://ccal-exeter.org/stop-it.html), a free program developed by Verbruggen et al. (2008). The SST consists of GO and STOP trials. Participants completed four blocks of stop-signal trials (48 Go and 16 Stop trials per block), preceded by a training session of 32 trials (24 Go and eight Stop trials). The order of GO and STOP trials was randomized. After each block, participants were given a 20-s break. During Go trials, a square or circle was presented for 1000 ms on the center of a computer screen, preceded by a 500-ms fixation point (‘‘+’’). Participants were instructed to respond as quickly as possible without sacrificing accuracy, pressing the ‘‘Z’’ button on the left side of a standard English keyboard with the left index finger when squares were presented and the ‘‘/’’ button on the right side of the keyboard with the right index finger when circles were presented. During stop trials, a 75-ms 750-Hz tone, appeared shortly after the Go signal onset and participants were instructed to stop their response upon hearing the tone. Between each trial, the screen was blank for 1000 ms. The time interval between Stop and Go signals (stopsignal delay, SSD) directly influences the difficulty level of a SST (Verbruggen and Logan, 2009). Therefore, the SST was designed to enable participants to successfully inhibit 50% of the stop trials. Specifically, the SSD was initially set at 250 ms after the presentation of Go signals (the square or the circle) and then adjusted dynamically depending on responses. When a participant succeeded in inhibiting her response, the SSD was increased by 50 ms until reaching a 550-ms duration, thereby making it more difficult to inhibit the

119

D. Dong et al. / Neuroscience 266 (2014) 116–126

next trial. When a participant failed to inhibit responding, the subsequent Stop trial was made easier by decreasing the SSD by 50 ms until reaching a minimum of 50 ms. The mean success rate (respond | signal) in this study was 50.56% (SD = 4.3%), very close to the 50% ideal and indicative of appropriate execution by participants. Task-based behavior responses of interest were reaction time (RT) and stop delay time (SDT). The dependent variable, stop-signal reaction time (SSRT), provides an index of ability to inhibit pre-potent responses, and is calculated using the horse-race model which describes the probability of responding on a stopsignal trial, p(respond | signal), the latency of go RTs that escape inhibition and SSRT, and assumes stochastic independence between the go and stop course (Logan and Cowan, 1984), by subtracting SDT from RT (see Band et al., 2003). Longer SSRTs indicate poorer response inhibition and are considered to reflect more impulsivity. rsfMRI data acquisition rsfMRI data were acquired using a 3-T Siemens Trio scanner in the SWU Imaging Center for Brain Research. Foam pads were used to reduce head movements and scanner noise. Scans were performed by an echo-planar imaging (EPI) sequence with the following scan parameters: repetition time = 2000 ms, echo time = 30 ms, flip angle = 90°, field of view = 192  192 mm2, acquisition matrix = 64  64, in-plane resolution = 3  3 mm2, 32 interleaved 3-mm-thick slices, inter-slice skip = 0.99 mm. For each participant, 242 EPI functional volumes were collected. During the scan, participants were instructed to keep their eyes closed, not to think of anything in particular and not to fall asleep. Data preprocessing All preprocessing steps were carried out using the Data Processing Assistant for Resting-State fMRI V2.0 (DPARSF, http://www.restfmri.net/forum/DPARSF) (Yan and Zang, 2010). Preprocessing included discarding the first ten volumes, slice timing, head motion correction, spatially normalizing to the standard Montreal Neurological Institute (MNI) EPI template with a resampled voxel size of 3  3  3 mm3, linear trend removing, and filtering (e.g., 0.01–0.08 Hz). Finally, six head motion parameters, white matter and CSF signals were regressing out before ReHo computation based on protocols of previous studies (Albert et al., 2009; Zuo et al., 2013). ReHo analysis KCC (Kendall and Gibbons, 1990) values were calculated to the similarity of the time series of a given voxel to its nearest 26 voxels based on the ReHo hypothesis (Zang et al., 2004). KCC can be computed from the following formula: P W¼

2

 ðRi Þ2  nðRÞ 2 1 3 K ðn  nÞ 12

where W is the KCC of a given voxel, ranging from 0 to 1;  ¼ ðn þ 1ÞK=2 is Ri is the sum rank of the ‘‘i’’th time point; R the mean of the Ri; K is the number of time series within a measured cluster (K = 27, one given voxel plus the number of its neighbors); and n is the number of ranks (i.e., 232 in this study). The free rsfMRI data analysis toolkit v1.8 (REST, www.restfmri.net) (Song et al., 2011) generated individual ReHo maps in a voxel-wise manner. Next, a mask (made from the MNI template to assure matching with the normalization step), in the REST software, removed non-brain tissue. To reduce the impact of individual variations in KCC values, the individual ReHo map was divided by its own mean KCC value within the mask. Finally, ReHo maps were spatially smoothed (4 mm FWHM Gaussian kernel). Statistical analysis Following other published work (e.g., Qiu et al., 2011; Hu et al., 2013; Xiao et al., 2013), one-sample t-tests were used to identify regions significantly greater than the whole brain average of one within each group. Herein, these within-group maps are merely for visualizing ReHo. The significance threshold was set at p < 0.01 (multiple comparison using the false discovery rate (FDR) criterion). To further determine ReHo differences between REs and UREs, a two-sample t-test was performed on individual normalized ReHo maps of the two groups. Given that this was a preliminary investigation of resting-state fMRI within REs, a relatively liberal correction (AlphaSim corrected) was selected to control for Type I errors (e.g., Qiu et al., 2011; Hu et al., 2013; Xiao et al., 2013). Monte Carlo simulations were performed (parameters: individual voxel p-value = 0.01, 10,000 simulations, FWHM = 4 mm, two sided, with a whole brain mask including 70,831 re-sampled voxels) using AlphaSim program in REST software. According to the simulations, a corrected significance level of p < 0.05 could be obtained with a cluster size >432 mm3 (16 voxels) and individual voxel (p < 0.01). Although no significant differences emerged between REs and UREs on age, BMI, fasting time or menstrual cycle phase (Table 1), these were treated as covariates to reduce potentially subtle effects on imaging data. Based on ReHo findings, we identified several brain regions that demonstrated significant between-group differences; these were classified as regions of interest (ROIs; 6 mm sphere), saved as masks separately in REST. For each ROI, the mean ReHo value was extracted by averaging ReHo values over all voxels for each participant. Finally, mean ReHo values were entered into SPSS 16.0 (http://www.spss.com.cn/), to calculate their correlations with SSRTs.

RESULTS Behavioral results for the SST There was no significant difference in mean RT between REs (M = 498.46, SD = 75.23) and UREs (M = 496.58, SD = 72.51), t (50) = 0.09, p = 0.93. However, average SSRT, characteristic of decreased

120

D. Dong et al. / Neuroscience 266 (2014) 116–126

response inhibition capacities, was significantly longer for REs (M = 289.56, SD = 56.91) than UREs (M = 261.40, SD = 36.13), t (50) = 2.04, p = 0.049. Consistent with the race-model assumption of the independence for the Go and Stop course (Logan and Cowan, 1984; Verbruggen et al., 2008), the Go RT-SSRT correlation was not significant, r = 0.05, p = 0.92. ReHo results Fig. 1 shows one-sample t-test results (corrected by FDR; p < 0.01) for the ReHo analysis of UREs (Fig. 1 top plane)

and REs (Fig. 1 bottom plane). Visual inspection indicated that the posterior cingulate cortex, precuneus, MPFC and bilateral angular gyrus (AG) had significantly higher ReHo values than other brain regions. The ReHo pattern was very similar to the default-mode network (Lei et al., 2013). Additionally, other brain regions, including the bilateral dlPFC, inferior parietal gyrus, putamen, MTG and insula, had higher ReHo values. Herein, these within-group maps are merely for visualizing ReHo. RE group differences in regional ReHo were revealed by a two-sample t-test (p < 0.05, AlphaSim correction), as shown in Fig. 2 and Table 2. Specifically, increased

t score

R

L

Fig. 1. Mean ReHo maps within UREs (top plane), REs (bottom plane) groups (p < 0.01, FDR corrected). Numbers in the upper left of each image refer to the z-plane coordinates of the Montreal Neurological Institute (MNI) space. Letters L and R correspond to the left and right sides of the brain, respectively. 5.49

J

I E

D K

M

A

t score

B

2.69 -2.69

H N

C

F

R

G

L

-4.51

Fig. 2. ReHo map of statistically significant differences by two-sample t-test between REs and UREs (p < 0.05, AlphaSim corrected). Numbers in the upper left of each image refer to the y-plane coordinates of the Montreal Neurological Institute (MNI) space. Letters L and R correspond to the left and right sides of the brain, respectively. (A) Cerebellum anterior lobe; (B) parahippocampal gyrus; (C) superior temporal gyrus; (D) cerebellum posterior lobe; (E) cuneus; (F) dorsolateral prefrontal cortex; (G) orbitofrontal cortex; (H) thalamus/caudate; (I) middle temporal gyrus; (J) inferior parietal lobule; (K) paracentral lobule; (M) precuneus; (N) anterior insula.

121

D. Dong et al. / Neuroscience 266 (2014) 116–126 Table 2. Regions of significantly increased and decreased ReHo (p < 0.05, corrected) among REs Brain region

BA

Increased ReHo R cerebellum anterior lobea (lobule V) R parahippocampal gyrus L orbitofrontal cortexb L cerebellum posterior lobea (lobule VI) L superior temporal gyrus L lingual gyrus L cuneus L dorsolateral prefrontal cortex R thalamus/caudate R middle temporal gyrus R inferior parietal lobule R paracentral lobule Decreased ReHo L Anterior insula R Precuneus R cerebellum posterior lobea (lobule VIIIB) R cerebellum posterior lobea (Crus II) R cerebellum posterior lobea (Crus I) L medulla

35

38 18 17 46 39 40 6/3 13 7

MNI

Volume (mm3)

t-Value

30 24 12 30 48 30 15 42 12 51 45 3

36 24 54 60 9 87 96 48 6 72 57 30

33 21 27 21 21 18 6 9 15 18 45 72

594 432 405 702 648 1350 810 675 594 567 459 1944

3.62 4.48 3.70 4.48 3.24 3.83 4.15 5.49 3.70 3.40 4.11 3.84

36 3 24 36 51 3

18 48 36 87 72 45

9 48 48 45 39 45

459 2160 756 702 459 675

3.80 4.23 4.51 2.96 3.00 3.64

Note: BA, Brodmann area; L = left brain; R = right brain. a Locations were determined using the MRI atlas of the human cerebellum (Schmahmann et al., 2000). b Cluster size = 15; AlphaSim corrected.

ReHo was found in the right cerebellum anterior lobe (lobule V), right parahippocampal gyrus, left OFC, left superior temporal gyrus, left cerebellum posterior lobe (lobule VI), left cuneus, left dlPFC, left lingual gyrus, right thalamus, right MTG, right inferior parietal lobule, right paracentral lobule among REs compared to UREs. Decreased ReHo was found in the right cerebellum posterior lobe (lobule VIIIB, Crus II, Crus I), left anterior insula, right precuneus, and left medulla among REs relative to UREs.

evaluate relations between differences in spontaneous brain activity and ability to inhibit pre-potent responses. As Fig. 3 illustrates, among REs the dlPFC had a significant negative correlation with SSRT (p < 0.05). Conversely, SSRT had a significant positive association with the left insula within the RE group. Notably, correlations between SSRT and the dlPFC (r = 0.01, p = 0.94) and left anterior insula (r = 0.06, p = 0.72) were not significant among UREs.

DISCUSSION Correlations between ReHo and SSRT Correlations between mean ReHo indices of identified regions and SSRT were computed within each group to

dlPFC

This study investigated spontaneous brain region activity measured by ReHo in restrained and UREs equated on background measures including BMI. Results were in

Anterior Insula

Mean ReHo Value(A.U.)

p=0.015

SSRT(ms)

r=0.49 p=0.020

Mean ReHo Value(A.U.)

r=-0.51

SSRT(ms)

Fig. 3. The correlation between the ReHo values and SSRT within the REs group. The SSRT negatively correlates with ReHo values in the left dlPFC, positively correlates with ReHo values in the left anterior insula.

122

D. Dong et al. / Neuroscience 266 (2014) 116–126

line with the hypothesis that ReHo score differences would be revealed in brain regions associated with food reward and attention (Coletta et al., 2009; Burger and Stice, 2011; Schur et al., 2012; Stice et al., 2013). Less expectedly, group differences were also observed in somatosensory regions. In addition, consistent with some behavioral studies (Nederkoorn et al., 2004; Jansen et al., 2009) but not others (e.g., Meule et al., 2011) previous, REs showed less efficient response inhibition than did UREs. Critically, the ReHo index in two areas, the left anterior insula and left dlPFC, had significant correlations with response inhibition among REs but not UREs. In relation to ReHo findings, many studies have implicated the OFC, dlPFC, parahippocampal gyrus, and thalamus in reward-driven eating behavior. Increased ReHo in the OFC, dlPFC, thalamus in this research is in line with reports of OFC, dlPFC, and thalamus activity in response to appetizing food pictures in fMRI studies of REs (Coletta et al., 2009; Schur et al., 2012). Hunger elicits activity in the OFC (Tataranni et al., 1999; Goldstone et al., 2009), a region involved in encoding food liking, food-cravings and anticipated rewards (Kringelbach, 2005). The dlPFC has been implicated in expectations of reward (Killgore et al., 2003; Kringelbach et al., 2004). Increased activity in the parahippocampal gyrus is associated with desirability of food (Pelchat et al., 2004) and the development of short- and long-term memories, including memories of food (Leidy et al., 2011). Located in a central position within the basal ganglia loops, the thalamus has been shown previously to encode anticipation of reward (Cho et al., 2012) and hunger signals (Tataranni et al., 1999). As these ReHo findings indicated, food-deprived REs having an average BMI had more hyper-responsive reward circuitry than UREs. This constellation of activation differences may initially contribute to increased risk for episodic overeating and binge eating (Stice, 2001; Wertheim et al., 2001; Johnson and Wardle, 2005; Neumark-Sztainer et al., 2006; Stice et al., 2008a). An increased ReHo index among REs compared to UREs also emerged in areas linked to visual attention processing – the inferior parietal lobule, MTG, lingual gyrus, cuneus, and calcarine. In caloric deprivation conditions, anticipated food receipt elicits activity in the lingual gyrus, cuneus, and MTG (Stice et al., 2013), regions associated with visual attention processing (Hahn et al., 2006; Schur et al., 2012). Thus, food deprivation may prompt REs to increase attention toward their next meal relative to UREs. Additionally, the inferior parietal lobule and MTG have been identified as critical regions in a ‘‘stimulus-drive-attention’’ system specialized for detecting behaviorally-relevant stimuli (Corbetta and Shulman, 2002). Degradation of the ReHo index in the precuneus, a structure involved in mediating relations between the self and outside world (Cavanna and Trimble, 2006) was also found in REs. Together, these findings suggest food deprivation prompts REs to be more sensitive than UREs in detecting palatable food within their environment. These

findings also suggest neural bases underlying attention biases of REs toward food cues shown in some behavior studies (Boon et al., 2000; Hollitt et al., 2010; Veenstra et al., 2010), although direct tests are needed to test this contention in the imaging literature. Less expectedly, REs had comparatively higher ReHo values in the paracentral lobule and cerebellum anterior lobe as well as decreased ReHo values in the anterior insula and cerebellum posterior lobe, regions associated with processing of somatosensory information. The paracentral lobule, located between precentral gyrus and postcentral gyrus, plays a role in sensorimotor integration (White et al., 1997; Desikan et al., 2006). A recent meta-analysis linked the cerebellar anterior lobe and parts of the lobule VIII with sensorimotor processing in contrast to associations between posterior lobules VI, involving both Crus I and Crus II, and cognitiveemotional processing (Stoodley and Schmahmann, 2009). Furthermore, the anterior insula is closely involved in homeostatic regulation (Seeley et al., 2007), representation of interoception (Craig, 2009), and somatosensory integration (Wyland et al., 2003). As such, these findings suggest RE during food deprivation is related to alterations in somatosensory regions that, in conjunction with hyper-responsive reward circuitry, contribute to risk for episodic overeating similar to both hypersensitive reward circuitry and altered somatosensory regions to food cues observed in obese groups (Stice et al., 2008b, 2011). Collectively, ReHo results suggest that REs subjected to caloric deprivation have altered local spontaneous activity in brain regions associated with food reward, attention and somatosensory compared with caloriedeprived UREs. These findings are consistent with previous food deprivation studies showing increased activation both reward and attention areas (Tataranni et al., 1999; Porubska et al., 2006; Goldstone et al., 2009; Siep et al., 2009; Stice et al., 2013) but also extend imaging studies on RE in important ways. Taskbased fMRI research (e.g., Coletta et al., 2009; Burger and Stice, 2011) have found REs show comparatively more reward region responsiveness to visual food cues, and food receipt but our findings suggest links between RE after food deprivation and reward as well as attention, and somatosensory region activity extend to rsfMRI. Consequently, drawing from the current results and those of obesity studies (Stice et al., 2008b, 2011), the hypothesis that structural correlates underlying RE include not only reward centers but also attention and somatosensory regions warrants further attention. Behavioral findings based on SST performance were in line with some past work (Nederkoorn et al., 2004; Jansen et al., 2009) indicating REs have more difficulty than UREs in inhibiting basic non-food related motor responses. For example, consistent with parameters of present SST, Nederkoorn et al. (2004) found REs were significantly worse than UREs in inhibiting basic nonfood related motor responses both in the presence and absence of food. Nonetheless, behavior results were at odds with those of Meule et al. (2011) who found REs showed more

D. Dong et al. / Neuroscience 266 (2014) 116–126

behavioral inhibition in an XY Go/No Go task. Certain methodological factors may have contributed to discrepant results between studies. First, Meule et al. had participants consume several snacks as a preload before the XY Go/No Go task, while participants in this study and Jansen et al. (2009) were food deprived before engaging in the SST. Consequently, differences in food intake versus food deprivation may have contributed to RE sample response differences (Herman and Mack, 1975). Second, stop-signal and classic Go/ No Go tasks appear to assess different aspects of inhibition given that performance on the two tasks is positively, yet weakly, correlated (Reynolds et al., 2006). In Go/No Go tasks, participants decide on every trial whether to respond or not. In contrast, stop-signal tasks requires participants to inhibit responses they have already initiated. Perhaps different outcomes regarding Go/No Go and SSTs also reflect variability in responses of REs outside the lab. That is, REs may be more able to restrict food intake when completely inhibiting eating as per Go/No Go tasks, but are more likely to lose inhibitory control once eating is initiated as per responses in SSTs. Regarding ReHo index-SSRT associations, a novel finding was the significant negative correlation of ReHo to SSRT in the left dlPFC among REs that was absent among UREs. The dlPFC may be critical for successful inhibition control (e.g., Verbruggen and Logan, 2008; Chambers et al., 2009). For example, Wager et al. (2005) and Watanabe et al. (2002a) observed increased activation in the bilateral dlPFC when participants carried out successful stop-signal response inhibition. The dlPFC is also related to planning, goal-directed behavior and executive control (Watanabe et al., 2002b; Heller, 2004). In concert with findings of higher ReHo values in dlPFC implicated in expectations of reward (Killgore et al., 2003; Kringelbach et al., 2004), the ReHo-SSRT correlation in the dlPFC may reflect increased anticipation of a next meal among fooddeprived REs who are simultaneously attempting to control this response because it clashes with the goal of maintaining or losing weight through calorie restriction. This interpretation is in line with contentions that REs keep conflicting goals in mind: enjoyment of eating versus control of eating (Stroebe et al., 2008). In noting the null effect for the inferior frontal gyrus, we speculated that task-related fMRI designs featuring food-specific Go/No Go tasks (Batterink et al., 2010) may elucidate links with inhibitory control further. The left anterior insula also had a significant positive correlation with SSRT among REs but not UREs. A recent meta-analysis of two response inhibition tasks (SST and No Go) revealed the anterior insula has important cognitive control functions (Swick et al., 2011). Structural MRI indicates anterior insula thickness is positively associated with impulsivity and impaired planning capacity (Churchwell and Yurgelun-Todd, 2013). The anterior insula is also considered the hub of a ‘‘salience network’’ (Seeley et al., 2007) that serves to initiate dynamic switching between central executive and default-mode networks (Sridharan et al., 2008).

123

Furthermore, sensory and limbic inputs are processed by the anterior insula, which detects salient stimuli and initiates appropriate control signals to regulate behavior and homeostasis (Menon and Uddin, 2010). Overall, these results suggest that anterior insula dysfunction contributes to less efficient response inhibition among food-deprived REs, possibly increasing risk for impulsive eating. In sum, the findings may help to explain why REs who attempt to maintain or lose weight through calorie restriction typically fail (Hetherington et al., 2000; Sysko et al., 2007). Specifically, even when visual food cues were absent, food-deprived REs showed stronger local spontaneous activity in brain regions associated with the reward value of food and attention as well as less efficient response inhibition relative to food-deprived UREs. This pattern may contribute to overeating in the former group. These results are consistent with taskbased fMRI studies illustrating how skipping breakfast increases reward circuit responsiveness to food cues (Goldstone et al., 2009), sensitivity to food stimuli (Stockburger et al., 2009), and risk for subsequent weight gain (Rampersaud et al., 2005; Niemeier et al., 2006; Timlin et al., 2008). In general, these separate lines of evidence imply skipping breakfast is unlikely to be a successful weight loss strategy among REs compared to approaches that involve breakfast consumption. Notwithstanding its implications, the main limitations of this study should be acknowledged. First, although sample sizes were larger than those used in many previous brain imaging studies, recruitment of larger samples in extensions may facilitate the evaluation of related research questions. For example, although the assessment of ReHo differences between food-deprived RE and URE groups was a sensible initial line of investigation, there is evidence that REs can be divided further into successful and unsuccessful subgroups (e.g., Meule et al., 2012; Hofmann et al., 2013). Given clear differences between general groups of REs and UREs, a logical focus of extensions should include the assessment of ReHo differences between these two RE subgroups and UREs. Second, while college-age women are a relevant at-risk group for RE, the inclusion of men and/or other age groups in future work would help to clarify whether findings generalize to other RE groups. Third, while ReHo is deemed to be a reliable approach to characterizing functional homogeneity of blood-oxygen-level-dependent (BOLD) signals within a small region (Zuo et al., 2013), test–retest reliability of ReHo could not be assessed here due to use of a cross-sectional research design. Once again, the current findings help to justify future research that employs costly longitudinal designs to evaluate the stability of local functional connectivity among REs over repeated assessments. Finally, altered activity observed in some brain regions of REs demonstrated local synchronization changes associated with spontaneous activity but functional links reflecting long-distance interregional connectivity (Lei et al., 2011) were not assessed and should be addressed in future work.

124

D. Dong et al. / Neuroscience 266 (2014) 116–126

CONCLUSION Building upon task-based fMRI research, this study is the first to implicate neural synchronization of local brain areas with restrained eating during a resting state following food deprivation. Specifically, REs subjected to caloric deprivation showed increased local spontaneous activity in brain regions associated with food reward, attention and somatosensory processing compared to UREs. In conjunction with previous findings, these data may help to explain why REs who diet to maintain or lose weight are generally unsuccessful and at risk for binge eating. In addition, correlation findings between (1) ReHo in the dlPFC and anterior insula and (2) SSRT among REs but not UREs suggested less efficient response inhibition fuels these responses within the former group.

ROLE OF THE FUNDING SOURCE This research was supported by grants from the National Nature Science Foundation of China (31170981 and 31371037), National Social Science Fund of China (12XSH018), BaYu Scholar Talents Program from Chongqing Government and Science and Technology Innovation Fund for SWU graduate students (kb2011001).

DISCLOSURE STATEMENT The authors report no conflicts of interest related to this research.

REFERENCES Albert NB, Robertson EM, Miall RC (2009) The resting human brain and motor learning. Curr Biol 19:1023–1027. Araujo DMR, Santos GFS, Nardi AE (2010) Binge eating disorder and depression: a systematic review. World J Biol Psychiatry 11:199–207. Band GP, Van Der Molen MW, Logan GD (2003) Horse-race model simulations of the stop-signal procedure. Acta Psychol 112:105–142. 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. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541. Boon B, Vogelzang L, Jansen A (2000) Do restrained eaters show attention toward or away from food, shape and weight stimuli? Eur Eat Disord Rev 8:51–58. Burger KS, 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. Cavanna AE, Trimble MR (2006) The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129:564–583. Chambers CD, Garavan H, Bellgrove MA (2009) Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci Biobehav Rev 33:631–646. Cho YT, Fromm S, Guyer AE, Detloff A, Pine DS, Fudge JL, Ernst M (2012) Nucleus accumbens, thalamus and insula connectivity during incentive anticipation in typical adults and adolescents. Neuroimage 66:508–521. Churchwell JC, Yurgelun-Todd DA (2013) Age-related changes in insula cortical thickness and impulsivity: significance for

emotional development and decision-making. Dev Cogn Neurosci 6:80–86. Coletta M, Platek S, Mohamed FB, van Steenburgh JJ, Green D, Lowe MR (2009) Brain activation in restrained and unrestrained eaters: an fMRI study. J Abnorm Psychol 118:598–609. Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3:201–215. Craig AD (2009) How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci 3:655–666. Dansinger ML, Tatsioni A, Wong JB, Chung M, Balk EM (2007) Metaanalysis: the effect of dietary counseling for weight loss. Ann Intern Med 147:41–50. Demos KE, Kelley WM, Heatherton TF (2011) Dietary restraint violations influence reward responses in nucleus accumbens and amygdala. J Cogn Neurosci 23:1952–1963. Desikan RS, Se´gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980. Dingemans A, Bruna M, Van Furth E (2002) Binge eating disorder: a review. Int J Obes (Lond) 26:299–307. Field AE, Haines J, Rosner B, Willett WC (2010) Weight-control behaviors and subsequent weight change among adolescents and young adult females. Am J Clin Nutr 91:147–153. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711. Garcia-Garcia I, Jurado MA, Garolera M, Segura B, Sala-Llonch R, Marques-Iturria I, Pueyo R, Sender-Palacios MJ, Vernet-Vernet M, Narberhaus A, Ariza M, Junque C (2012) Alterations of the salience network in obesity: a resting-state fMRI study. Hum Brain Mapp 34:2786–2797. Goldstone AP, Prechtl de Hernandez CG, Beaver JD, Muhammed K, Croese C, Bell G, Durighel G, Hughes E, Waldman AD, Frost G, Bell JD (2009) Fasting biases brain reward systems towards highcalorie foods. Eur J Neurosci 30:1625–1635. Guerrieri R, Nederkoorn C, Jansen A (2008) The interaction between impulsivity and a varied food environment: its influence on food intake and overweight. Int J Obes (Lond) 32:708–714. Guerrieri R, Nederkoorn C, Schrooten M, Martijn C, Jansen A (2009) Inducing impulsivity leads high and low restrained eaters into overeating, whereas current dieters stick to their diet. Appetite 53:93–100. Gunstad J, Paul RH, Cohen RA, Tate DF, Spitznagel MB, Grieve S, Gordon E (2008) Relationship between body mass index and brain volume in healthy adults. Int J Neurosci 118:1582–1593. Hahn B, Ross TJ, Stein EA (2006) Neuroanatomical dissociation between bottom–up and top–down processes of visuospatial selective attention. Neuroimage 32:842–853. Heller W (2004) Emotion. In: Banich M, editor. Cognitive neuroscience and neuropsychology. Boston, MA: Houghton Mifflin Company. p. 393–427. Herman CP, Mack D (1975) Restrained and unrestrained eating. J Pers 43:647–660. Herman CP, Polivy J (1980) Restrained eating. In: Stunkard A, Stellar E, editors. Obesity. Philadelphia: Saunders. p. 208–225. Hetherington MM, Bell A, Rolls BJ (2000) Effects of repeat consumption on pleasantness, preference and intake. Br Food J 102:507–521. Hofmann W, Adriaanse M, Vohs KD, Baumeister RF (2013) Dieting and the self-control of eating in everyday environments: an experience sampling study. Br J Health Psychol. http://dx.doi.org/ 10.1111/bjhp.12053. Hollitt S, Kemps E, Tiggemann M, Smeets E, Mills JS (2010) Components of attentional bias for food cues among restrained eaters. Appetite 54:309–313. Holm-Denoma JM, Joiner Jr TE, Vohs KD, Heatherton TF (2008) The ‘‘freshman fifteen’’ (the ‘‘freshman five’’ actually): predictors and possible explanations. Health Psychol 27:S3–S9.

D. Dong et al. / Neuroscience 266 (2014) 116–126 Hu S, Xu D, Peterson B, Wang Q, He X, Hu J, Xu X, Wei N, Long D, Huang M, Zhou W, Xu W, Zhang M, Xu Y (2013) Association of cerebral networks in resting state with sexual preference of homosexual men: a study of regional homogeneity and functional connectivity. PLoS One 8:e59426. 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. Jansen A, Nederkoorn C, van Baak L, Keirse C, Guerrieri R, Havermans R (2009) High-restrained eaters only overeat when they are also impulsive. Behav Res Ther 47:105–110. Jarry JL, Polivy J, Herman CP, Arrowood AJ, Pliner P (2006) Restrained and unrestrained eaters’ attributions of success and failure to body weight and perception of social consensus: the special case of romantic success. J Soc Clin psychol 25:885–905. Jasinska AJ, Yasuda M, Burant CF, Gregor N, Khatri S, Sweet M, Falk EB (2012) Impulsivity and inhibitory control deficits are associated with unhealthy eating in young adults. Appetite 59:738–747. Johnson F, Wardle J (2005) Dietary restraint, body dissatisfaction, and psychological distress: a prospective analysis. J Abnorm Psychol 114:119–125. Kendall M, Gibbons JD (1990) Rank correlation methods. Oxford: Oxford Univ. Press. Kessler RC, Berglund PA, Chiu WT, Deitz AC, Hudson JI, Shahly V, Xavier M (2013) The prevalence and correlates of binge eating disorder in the World Health Organization World Mental Health Surveys. Biol Psychiatry 73:904–914. Killgore WDS, Young AD, Femia LA, Bogorodzki P, Rogowska J, Yurgelun-Todd DA (2003) Cortical and limbic activation during viewing of high- versus low-calorie foods. Neuroimage 19:1381–1394. Kringelbach ML (2005) The human orbitofrontal cortex: linking reward to hedonic experience. Nat Rev Neurosci 6:691–702. Kringelbach ML, de Araujo IE, Rolls ET (2004) Taste-related activity in the human dorsolateral prefrontal cortex. Neuroimage 21:781–788. Kullmann S, Heni M, Veit R, Ketterer C, Schick F, Haring HU, 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. Lei X, Ostwald D, Hu J, Qiu C, Porcaro C, Bagshaw AP, Yao D (2011) Multimodal functional network connectivity: an EEG-fMRI fusion in network space. PLoS One 6:e24642. Lei X, Zhao Z, Chen H (2013) Extraversion is encoded by scale-free dynamics of default mode network. Neuroimage 74:52–57. Leidy HJ, Lepping RJ, Savage CR, Harris CT (2011) Neural responses to visual food stimuli after a normal vs. higher protein breakfast in breakfast-skipping teens: a pilot fMRI study. Obesity (Silver Spring) 19:2019–2025. Leidy HJ, Ortinau LC, Douglas SM, Hoertel HA (2013) Beneficial effects of a higher-protein breakfast on the appetitive, hormonal, and neural signals controlling energy intake regulation in overweight/obese, ‘‘breakfast-skipping’’, late-adolescent girls. Am J Clin Nutr 97:677–688. Logan GD, Cowan WB (1984) On the ability to inhibit thought and action: a theory of an act of control. Psychol Rev 91:295–327. Logan GD, Schachar RJ, Tannock R (1997) Impulsivity and inhibitory control. Psychol Sci 8:60–64. Lowe MR, Friedman MI, Mattes R, Kopyt D, Gayda C (2000) Comparison of verbal and pictorial measures of hunger during fasting in normal weight and obese subjects. Obes Res 8:566–574. Menon V, Uddin LQ (2010) Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 214:655–667. Meule A, Lukito S, Vo¨gele C, Ku¨bler A (2011) Enhanced behavioral inhibition in restrained eaters. Eat Behav 12:152–155. Meule A, Papies EK, Ku¨bler A (2012) Differentiating between successful and unsuccessful dieters. Validity and reliability of

125

the perceived self-regulatory success in dieting scale. Appetite 58:822–826. Nederkoorn C, Van Eijs Y, Jansen A (2004) Restrained eaters act on impulse. Pers Individ Differ 37:1651–1658. Neumark-Sztainer D, Wall M, Guo J, Story M, Haines J, Eisenberg M (2006) Obesity, disordered eating, and eating disorders in a longitudinal study of adolescents: how do dieters fare 5 years later? J Am Diet Assoc 106:559–568. Niemeier HM, Raynor HA, Lloyd-Richardson EE, Rogers ML, Wing RR (2006) Fast food consumption and breakfast skipping: predictors of weight gain from adolescence to adulthood in a nationally representative sample. J Adolesc Health 39:842–849. Pelchat ML, Johnson A, Chan R, Valdez J, Ragland JD (2004) Images of desire: food-craving activation during fMRI. Neuroimage 23:1486–1493. Polivy J, Herman CP (1985) Dieting and binging: a causal analysis. Am Psychol 40:193. Polivy J, Herman CP (1999) Distress and eating: why do dieters overeat? Int J Eat Disord 26:153–164. Porubska K, Veit R, Preissl H, Fritsche A, Birbaumer N (2006) Subjective feeling of appetite modulates brain activity: an fMRI study. Neuroimage 32:1273–1280. Qiu C, Liao W, Ding J, Feng Y, Zhu C, Nie X, Zhang W, Chen H, Gong Q (2011) Regional homogeneity changes in social anxiety disorder: a resting-state fMRI study. Psychiatry Res 194:47–53. Rampersaud GC, Pereira MA, Girard BL, Adams J, Metzl JD (2005) Breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. J Am Diet Assoc 105:743–760. Reynolds B, Ortengren A, Richards JB, de Wit H (2006) Dimensions of impulsive behavior: personality and behavioral measures. Pers Individ Differ 40:305–315. Schmahmann JD, Doyon J, Petrides M, Evans AC, Toga AW (2000) MRI atlas of the human cerebellum. San Diego: Academic Press. Schur EA, Kleinhans NM, Goldberg J, Buchwald DS, Polivy J, Del Parigi A, Maravilla KR (2012) Acquired differences in brain responses among monozygotic twins discordant for restrained eating. Physiol Behav 105:560–567. Seeley WW, Menon V, Schatzberg AF, Keller J, Glover GH, Kenna H, Reiss AL, Greicius MD (2007) Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 27:2349–2356. Siep N, Roefs A, Roebroeck A, Havermans R, Bonte ML, Jansen A (2009) Hunger is the best spice: an fMRI study of the effects of attention, hunger and calorie content on food reward processing in the amygdala and orbitofrontal cortex. Behav Brain Res 198:149–158. Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, He Y, Yan CG, Zang YF (2011) REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6:e25031. Sridharan D, Levitin DJ, Menon V (2008) A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci U S A 105:12569–12574. Stice E (2001) A prospective test of the dual-pathway model of bulimic pathology: mediating effects of dieting and negative affect. J Abnorm Psychol 110:124–135. 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. Stice E, Davis K, Miller NP, Marti CN (2008a) Fasting increases risk for onset of binge eating and bulimic pathology: a 5-year prospective study. J Abnorm Psychol 117:941–946. Stice E, Ng J, Shaw H (2010) Risk factors and prodromal eating pathology. J Child Psychol Psychiatry 51:518–525. Stice E, Presnell K (2010) Dieting and the eating disorders. In: The Oxford handbook of eating disorders. Oxford: Oxford Univ. Press. p. 148–179.

126

D. Dong et al. / Neuroscience 266 (2014) 116–126

Stice E, Spoor S, Bohon C, Veldhuizen MG, Small DM (2008b) Relation of reward from food intake and anticipated food intake to obesity: a functional magnetic resonance imaging study. J Abnorm Psychol 117:924–935. Stice E, Yokum S, Burger KS, Epstein LH, Small DM (2011) Youth at risk for obesity show greater activation of striatal and somatosensory regions to food. J Neurosci 31:4360–4366. Stockburger J, Schmalzle R, Flaisch T, Bublatzky F, Schupp HT (2009) The impact of hunger on food cue processing: an eventrelated brain potential study. Neuroimage 47:1819–1829. Stoodley CJ, Schmahmann JD (2009) Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. Neuroimage 44:489–501. Stroebe W, Mensink W, Aarts H, Schut H, Kruglanski AW (2008) Why dieters fail: testing the goal conflict model of eating. J Exp Soc Psychol 44:26–36. Swick D, Ashley V, Turken U (2011) Are the neural correlates of stopping and not going identical? Quantitative meta-analysis of two response inhibition tasks. Neuroimage 56:1655–1665. Sysko R, Timothy Walsh B, Terence Wilson G (2007) Expectancies, dietary restraint, and test meal intake among undergraduate women. Appetite 49:30–37. Tataranni PA, Gautier JF, Chen K, Uecker A, Bandy D, Salbe AD, Ravussin E (1999) Neuroanatomical correlates of hunger and satiation in humans using positron emission tomography. Proc Natl Acad Sci U S A 96:4569–4574. Tian L, Ren J, Zang Y (2012) Regional homogeneity of resting state fMRI signals predicts stop signal task performance. Neuroimage 60:539–544. Timlin MT, Pereira MA, Story M, Neumark-Sztainer D (2008) Breakfast eating and weight change in a 5-year prospective analysis of adolescents: project EAT (Eating Among Teens). Pediatrics 121:e638–e645. Trottier K, Polivy J, Herman CP (2005) Effects of exposure to unrealistic promises about dieting: are unrealistic expectations about dieting inspirational? Int J Eat Disord 37:142–149. Veenstra EM, de Jong PJ, Koster EH, Roefs A (2010) Attentional avoidance of high-fat food in unsuccessful dieters. J Behav Ther Exp Psychiatry 41:282–288. Verbruggen F, Logan GD (2008) Response inhibition in the stopsignal paradigm. Trends Cogn Sci 12:418–424. Verbruggen F, Logan GD (2009) Models of response inhibition in the stop-signal and stop-change paradigms. Neurosci Biobehav Rev 33:647–661. Verbruggen F, Logan GD, Stevens MA (2008) STOP-IT: windows executable software for the stop-signal paradigm. Behav Res Methods 40:479–483. Wadden TA, Brownell KD, Foster GD (2002) Obesity: responding to the global epidemic. J Consult Clin Psychol 70:510–525.

Wade TD, Wilksch SM, Lee C (2012) A longitudinal investigation of the impact of disordered eating on young women’s quality of life. Health Psychol 31:352–359. Wager TD, Sylvester CYC, Lacey SC, Nee DE, Franklin M, Jonides J (2005) Common and unique components of response inhibition revealed by fMRI. Neuroimage 27:323–340. Wang DY, Liu DQ, Li SF, Zang YF (2012) Increased local synchronization of resting-state fMRI signal after episodic memory encoding reflects off-line memory consolidation. Neuroreport 23:873–878. Watanabe J, Sugiura M, Sato K, Sato Y, Maeda Y, Matsue Y, Fukuda H, Kawashima R (2002a) The human prefrontal and parietal association cortices are involved in NO–GO performances: an event-related fMRI study. Neuroimage 17:1207–1216. Watanabe M, Hikosaka K, Sakagami M, Shirakawa SI (2002b) Coding and monitoring of motivational context in the primate prefrontal cortex. J Neurosci 22:2391–2400. Waxman SE (2009) A systematic review of impulsivity in eating disorders. Eur Eat Disord Rev 17:408–425. Wertheim EH, Koerner J, Paxton SJ (2001) Longitudinal predictors of restrictive eating and bulimic tendencies in three different age groups of adolescent girls. J Youth Adolesc 30:69–81. White L, Andrews T, Hulette C, Richards A, Groelle M, Paydarfar J, Purves D (1997) Structure of the human sensorimotor system. I: morphology and cytoarchitecture of the central sulcus. Cereb Cortex 7:18–30. Wu T, Long X, Zang Y, Wang L, Hallett M, Li K, Chan P (2009) Regional homogeneity changes in patients with Parkinson’s disease. Hum Brain Mapp 30:1502–1510. Wyland CL, Kelley WM, Macrae CN, Gordon HL, Heatherton TF (2003) Neural correlates of thought suppression. Neuropsychologia 41:1863–1867. Xiao Q, Zhong Y, Lu D, Gao W, Jiao Q, Lu G, Su L (2013) Altered regional homogeneity in pediatric bipolar disorder during manic state: a resting-state fMRI study. PLoS One 8:e57978. Yan C, Liu D, He Y, Zou Q, Zhu C, Zuo X, Long X, Zang Y (2009) Spontaneous brain activity in the default mode network is sensitive to different resting-state conditions with limited cognitive load. PLoS One 4:e5743. Yan CG, Zang YF (2010) DPARSF: a MATLAB toolbox for ‘‘pipeline’’ data analysis of resting-state fMRI. Front Syst Neurosci 4:13. Zang Y, Jiang T, Lu Y, He Y, Tian L (2004) Regional homogeneity approach to fMRI data analysis. Neuroimage 22:394–400. Zuo XN, Xu T, Jiang L, Yang Z, Cao XY, He Y, Zang YF, Castellanos FX, Milham MP (2013) Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage 65:374–386.

(Accepted 30 January 2014) (Available online 7 February 2014)