Eating behavior associated with gray matter volume alternations: A voxel based morphometry study

Accepted Manuscript Eating Behavior Associated with Gray Matter Volume Alternations: A Voxel Based Morphometry Study Lizheng Yao, Wang Li, Zhenyu Dai, Congsong Dong PII:

S0195-6663(15)30068-4

DOI:

10.1016/j.appet.2015.10.017

Reference:

APPET 2743

To appear in:

Appetite

Received Date: 8 January 2015 Revised Date:

10 October 2015

Accepted Date: 15 October 2015

Please cite this article as: Yao L., Li W., Dai Z. & Dong C., Eating Behavior Associated with Gray Matter Volume Alternations: A Voxel Based Morphometry Study, Appetite (2015), doi: 10.1016/ j.appet.2015.10.017. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Eating Behavior Associated with Gray Matter Volume Alternations: a

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Voxel Based Morphometry Study

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Lizheng Yao 1 Ϯ, Wang Li 2 Ϯ, Zhenyu Dai 1, Congsong Dong 1 1 Department of Radiology, The Affiliated Yancheng Hospital of Southeast University Medical College, Yancheng, 224000, China 2 Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China

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Abstract

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Little is known about whether eating behavior is associated with alterations of brain

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structure or whether the possible alterations are related to body weight status. The current study

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employed structural imaging from an open MRI data set

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(http://fcon_1000.projects.nitrc.org/indi/pro/nki.html) to examine the relationship between

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eating behavior traits and brain structural changes. The eating behavior traits were measured by

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the Three Factor Eating Questionnaire Scale. The brain structural alterations were analyzed

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using the Voxel Based Morphometry (VBM) method, and a multiple linear regression model

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was constructed to identify significant brain structural changes that related to eating behavior

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factors. We found that cognitive restraint of eating was positively correlated with the gray

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matter volume (GMV) in the dorsolateral prefrontal cortex (DLPFC) and negatively correlated

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with the GMV in the putamen; disinhibition scores were negatively associated with the GMV

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in the left middle frontal gyrus; hunger scores showed a positive correlation with the GMV in

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the hypothalamus and the visual memory areas and a negative association with the GMV in the

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inferior temporal gyrus and the bilateral middle frontal gyrus. These results indicated a close

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connection between the eating behavior traits and structural changes in particular brain regions.

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Conjunction analysis was also performed to further explore the brain structural alterations that

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were commonly associated with eating behavior and weight status. The findings add to our

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understanding of the neural basis underlying eating behaviors, and the connection between

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these behaviors and body weight status.

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Key words: Obesity; Eating Behavior; Voxel Based Morphometry; Three Factor Eating Questionnaire; Dorsolateral Prefrontal Cortex

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Ϯ Lizheng Yao and Wang Li contribute equally to this work.

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Introduction

Overweight and obesity are major public health concerns. There are 2.1 billion

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overweight (body mass index, BMI > 25 kg/m²) and/or obese people around the world (BMI >

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30 kg/m²) (Ng, et al., 2014). Obesity exacerbates various medical conditions, such as coronary

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heart disease (Buchholz & Bugaresti, 2005), hypertension, type 2 diabetes, and stroke (Boeing,

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et al., 2012; Zhang, et al., 2014; Zhang, M von Deneen, Tian, S Gold, & Liu, 2011). Abnormal

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eating behavior is a major contributor to overweight and obesity (Moszczynski, 2010; Zhang,

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et al., 2014; Zhang, et al., 2015). The Three Factor Eating Questionnaire (TFEQ) (Stunkard & Messick, 1985) has been

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used to measure different aspects of eating behavior traits, including: (1) cognitive restraint of

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eating, (2) disinhibition (of control) and (3) (susceptibility to) hunger. Cognitive restraint of

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eating is defined as a tendency to consciously restrict energy intake and types of foods eaten

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with concerns about body shape and weight gain; disinhibition is referred to as the drive to

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overeat in the presence of palatable foods or with negative affect; and hunger is described as

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the susceptibility to perceive body symptoms that signal the need for food (Stunkard &

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Messick, 1985).

Functional imaging studies have demonstrated strong associations between eating

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behavioral traits and regional brain activations. Cognitive restraint of eating showed a negative

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correlation with activations of the temporal visual association network (Kullmann, et al., 2013).

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In obese participants, disinhibition scores were negatively associated with activation of the

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anterior cingulate cortex (Pannacciulli, et al., 2006) and hunger scores were positively

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correlated with activation of the medial prefrontal cortex (MPFC) (Martin, et al., 2010). Higher

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disinhibition scores were found to accompany increased insular blood flow in obese

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individuals after consuming a liquid meal (DelParigi, Chen, Salbe, Reiman, & Tataranni, 2005).

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Lee et al. discovered that the low-dietary disinhibition participants had less ventral medial

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prefrontal cortex (VMPFC) response to food evaluation after being fed (Lee, et al., 2013),

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while the high-dietary disinhibition participants showed no attenuation in brain responses (Lee,

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et al., 2013). These observations demonstrate an apparent connection between brain responses

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and eating behaviors.

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Studies of brain anatomy can provide unique insights into the neural basis of cognitive

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and behavior disorders. However, data are scarce in this regard. To our knowledge, only one

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study has investigated the association between eating behaviors and brain structural changes

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(Maayan, Hoogendoorn, Sweat, & Convit, 2011). Maayan and colleagues employed the region

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of interest (ROI) (Sumithran, et al., 2011) method to analyze the relationship between

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disinhibition and cortical volumes including the frontal lobe, ACC and orbitofrontal cortex

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(OFC) (Maayan, et al., 2011) and discovered that disinhibition scores were negatively

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correlated with OFC volume (Maayan, et al., 2011). Despite this information, correlations

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between different aspects of eating behaviors and alterations of various brain structures have

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not been systematically explored. The first objective of the current study was to examine

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whether there are structural changes associated with eating behaviors from a whole brain

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perspective.

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Several studies have found that eating behaviors varied with BMI (Castellanos, et al., 2009; Kullmann, et al., 2013; Martin, et al., 2010). Obese adults exhibit lower scores of

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cognitive restraint of eating (Castellanos, et al., 2009; Kullmann, et al., 2013; Martin, et al.,

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2010), higher scores of disinhibition (Castellanos, et al., 2009; Martin, et al., 2010), and higher

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susceptibility to hunger (Castellanos, et al., 2009; Kullmann, et al., 2013; Martin, et al., 2010)

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than normal weight people. In addition, increased restraint and decreased disinhibition have

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been indicated to be associated with greater reduction in waist circumference (Bryant,

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Caudwell, Hopkins, King, & Blundell, 2012). In particular, restraint was found to be associated

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with a reduction in body weight (Bryant, et al., 2012). For example, cognitive restraint scores

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were reported to be associated with an average decrease in BMI after a Roux-en-Y gastric

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bypass surgery (Laurenius, et al., 2012).

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In addition, accumulating evidence reveals a concurrence of overweight and obesity with brain structural alterations (Pannacciulli, et al., 2006; Smucny, et al., 2012; Taki, et al., 2008;

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Walther, Birdsill, Glisky, & Ryan, 2010; Ward, Carlsson, Trivedi, Sager, & Johnson, 2005).

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Higher BMI was linked to lower global brain volumes (Ward, et al., 2005) and local brain

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volumes in areas including the OFC (Walther, et al., 2010), inferior and middle frontal gyri

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(Pannacciulli, et al., 2006; Taki, et al., 2008; Walther, et al., 2010). BMI increase was shown to

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be accompanied by attenuated activation in cognition-relevant posterior brain regions

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including the parahippocampus, fusiform, lingual gyrus, and right cerebellar regions

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(Pannacciulli, et al., 2006; Walther, et al., 2010). Smucny and colleagues found that gray

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matter volume (GMV) was lower in the insula, medial OFC and cerebellum in obese-prone

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compared to obese-resistant individuals (Smucny, et al., 2012), suggestive of structural

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differences in brain regions important in energy regulation in individuals at risk for weight gain

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(Smucny, et al., 2012). In light of these findings, our second objective was to investigate the

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relationship between brain anatomical changes, eating behaviors and BMI. In the current study, we first employed the Voxel Based Morphometry (VBM) method to

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quantify GMV. Then, we conducted a regression analysis between the GMV and TFEQ scores,

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and also employed a group-level statistical test to explore whether brain structural alterations

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were associated with various aspects of eating behavioral traits. Finally, conjunction analyses

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were performed to delineate a possible relationship between brain structural alterations, eating

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behaviors and BMI.

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Method

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Participants and Procedures:

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MRI data were obtained from an open data set

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(http://fcon_1000.projects.nitrc.org/indi/pro/nki.html) accessible from the Center for

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Advanced Brain Imaging of the Nathan S. Kline Institute (Nooner, et al., 2012). About 109

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adults with a TFEQ test (female: 62, male: 47; age: 20 - 60 years, mean age: 35.15 ± 11.24

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years) were included in the current study, consisting of 2 underweight subjects (BMI < 18), 44

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normal weight subjects (18 < BMI < 25), 31 overweight subjects (25 < BMI < 30) and 32 obese

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subjects (BMI > 30). T1-weighted images were scanned with Siemens MAGNETOM TrioTim

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Syngo MR (MPRAGE; TR = 2500 ms; TE = 3.50 ms; TI = 1200 ms; FOV = 256 x 256; slice

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thickness = 1 mm; Flip angle = 8°; matrix size = 256 x 256; 200 Transverse slices). All the

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subjects possessed no history of psychiatric disorders or any neurological illnesses. Written

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informed consent was obtained from each participant. The NKI institutional review board

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approved all procedures for data collection and sharing (Cao, et al., 2014; Nooner, et al., 2012).

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WM/GM Volumetric Analysis:

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All MPRAGE images were processed with the SPM8 toolbox

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(http://www.fil.ion.ucl.ac.uk/spm/). First, in order to screen for artifacts or gross anatomical

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abnormalities, each MR image was first displayed in SPM8. Images were reoriented manually

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to set to the anterior commissure for better registration. Then, MPRAGE images of each

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subject were spatially normalized to the standard T1 Montreal Neurological Institute template

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and segmented into gray matter, white matter and cerebrospinal fluid using the tissue

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classification algorithm in SPM8. These segmented partitions were subsequently normalized to

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their respective standard templates. The normalized, segmented gray matter images were then

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modulated by calculating the Jacobian determinants derived from the special normalization

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step and by multiplying each voxel by the relative change in volume (Good, et al., 2001).

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Finally, images were smoothed by convoluting a 6 mm3–full width at half maximum Gaussian

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kernel to increase the signal to noise ratio.

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Statistical Analyses

A one-way ANCOVA was conducted on scores of cognitive restraint of eating,

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disinhibition and hunger with age, gender and handedness as covariates respectively. Subjects

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were divided into three groups (two 2 underweight subjects were excluded from the behavioral

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analysis ): normal weight (18 < BMI < 25), overweight (25 < BMI < 30), and obese (BMI >

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30)(WHO). Three multiple linear regressions were conducted on a whole-brain range. Scores

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of each TFEQ factor and BMI were the variables of interest, while age, gender, handedness and

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global volumes of gray matter were entered as covariates. A group level statistical analysis was

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performed to find significant alterations of brain structures that were correlated with each

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TFEQ factor (P = 0.001, cluster size > 5) (uncorrected). Significant alterations of brain

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structures that were correlated with BMI were identified at threshold of P = 0.001 and cluster

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size > 10 (uncorrected). Then, the conjunction analysis of contrasts of the BMI and contrasts of

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each TFEQ factor were performed: i, positive correlation with BMI and TFEQ scores; ii,

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positive correlation with BMI and negative association with TFEQ scores; iii, negative

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correlation with BMI and positive association with TFEQ scores; and iv, negative correlation

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with BMI and negative correlation with TFEQ scores. The regions reached the threshold of P =

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0.005 (uncorrected) and with a cluster > 10 voxels were analyzed.

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Results

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Three Factor Eating Questionnaire scores and BMI

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The mean score of cognitive restraint of eating was 7.20 ± 4.54 (Table 1). The cognitive

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restraint of the eating scores of the three groups were: normal weight: 6.75 ± 4.56, overweight:

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7.90 ± 3.67, and obese: 7.13 ± 5.26. The mean disinhibition score was 4.01 ± 3.22. The

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disinhibition scores of the three groups were: 4.09 ± 3.35 (normal weight), 3.61 ± 3.12

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(overweight), and 4.28 ± 3.19 (obese). The mean hunger score was 4.35 ± 3.22. The hunger

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scores of the three groups were: 3.59 ± 2.93 (normal weight), 4.32 ± 3.33 (overweight), and

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5.43 ± 3.27 (obese). The ANCOVA analysis disclosed a group effect in the hunger scores

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(F(2,104) = 3.17, P = 0.046) due to significantly lower hunger scores for the normal weight

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group than those of the obese group ( t = -2.58, P < 0.05). No significant group difference was

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found for scores of cognitive restraint of eating and disinhibition, respectively (ps. > 0.56).

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Mean BMI was 27.58 ± 6.05. BMI of the three groups were: normal weight: 22.15 ± 1.81,

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overweight: 27.76 ± 1.48, and obese: 34.87 ± 4.70 (Table 1).

Table 1 inserts about here

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Brain imaging

The results of the whole brain analysis were shown in Table 2 (Fig 1). The scores of

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cognitive restraint of eating were positively correlated with GMV in the bilateral middle

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occipital gyrus (Brodmann area 37, BA 37), bilateral fusiform gyrus (BA 37), posterior part of

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the left inferior temporal gyrus (BA 37) and anterior part of the right inferior temporal gyrus

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(BA 20), bilateral inferior parietal lobule (BA 40), right lingual gyrus (BA 18), right inferior

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occipital gyrus (BA 19), left cingulate gyrus (BA 32), left superior frontal gyrus (BA 6) and

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posterior cingulate (BA 23), posterior part and anterior part of the right middle temporal gyrus

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(BA 22 & 37 ) and superior temporal gyrus (BA 39), right cuneus (BA 18), right precuneus

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(BA 31) and right inferior frontal gyrus (BA 9). The scores of cognitive restraint of eating were

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negatively correlated with GMV in the right inferior frontal gyrus (BA 45) and lentiform

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nucleus (putamen) (Fig 1a).

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The disinhibition scores were positively correlated with GMV in the left subgyrals (BA 6) and right middle temporal gyrus (BA 21), and negatively correlated with GMV in the left

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precuneus (BA 7) and left middle frontal gyrus (BA 6) (Fig 1b).

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The hunger scores were positively correlated with GMV in the right middle temporal

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gyrus (BA, 21 & 22), right hypothalamus, bilateral parahippocampal gyrus (BA 34), and right

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anterior cingulate (BA 32). But they were negatively correlated with GMV in the right inferior

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temporal gyrus (BA 20), right fusiform gyrus (BA 20), and bilateral middle frontal gyrus (BA 6)

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(Fig 1c). Table 2 inserts about here

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Fig. 1 inserts about here

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The correlation between whole brain structural alterations and BMI were shown in Table

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3. The BMI was positively associated with GMV in the left precuneus (BA 30), left cerebellar

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tonsil and right lentiform nucleus. It was also negatively correlated with GMV in the bilateral

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inferior temporal gyrus (BA 20), right uncus (BA 38), bilateral inferior semi-lunar lobule, right

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supramarginal gyrus (BA 40), left fusiform gyrus (BA 20), thalamus, right anterior cingulate

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(BA 24 & 32), bilateral superior temporal gyrus (BA 22), left medial frontal gyrus (BA 9), right

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lingual gyrus (BA 18), right inferior frontal gyrus (BA 47), left posterior cingulate (BA 30) and

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middle occipital gyrus (BA 19), right middle temporal gyrus (BA 21), bilateral precuneus (BA

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7 & 19), lobule VIII region of the bilateral cerebellum, left middle frontal gyrus (BA 11),

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bilateral superior temporal gyrus (BA 38), right paracentral lobule (BA 5), right insula (BA 13),

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left parahippocampal gyrus (BA 28), right middle frontal gyrus (BA 8) and left fusiform gyrus

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(BA 19) (Fig 1d).

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Table 3 inserts about here

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The results of the conjunction analysis were presented in Table 4. The GMV in the right

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putamen was positively correlated with BMI and negatively correlated with cognitive restraint

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of eating (Fig 2a). The conjunction analysis showed that GMV in several brain regions were

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negatively associated with BMI and positively correlated with cognitive restraint of eating;

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these brain areas included the right lingual gyrus (BA 18), left fusiform gyrus (BA 9), right

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middle temporal gyrus (BA 37), left precuneus (BA 7), right inferior temporal gyrus (BA 20),

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and right inferior parietal lobule (BA 40) (Fig 2b). The left inferior temporal gyrus (BA 20)

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was inversely correlated with both BMI and disinhibition (Fig 2c). Brain regions that were

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negatively correlated with BMI and positively correlated with hunger scores included the right

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middle temporal gyrus (BA 21), right superior frontal gyrus (BA 10), bilateral middle frontal

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gyrus (BA 9, 46 & 10), and left precentral gyrus (BA 6)(Fig 2d). Table 4 inserts about here

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Fig. 2 inserts about here

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Discussion

In the current study, we first examined the relationship between the alterations of brain

structure and eating behavior, and then explored anatomical alterations that were associated

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with both eating behaviors and BMI. We found that eating behaviors were tightly correlated

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with the GMV of brain areas involved in homeostatic keeping, habitual learning and cognitive

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control of food intake. We also observed that certain altered brain structures were associated

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with both the eating behaviors and the BMI.

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Brain Imaging

Cognitive restraint of eating scores was positively correlated with the right inferior frontal gyrus (BA 9, part of DLPFC) and anterior cingulate gyrus (Pannacciulli, et al., 2006) The

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DLPFC is implicated in high-level executive processes such as cognitive control and emotion

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regulation (Hare, Camerer, & Rangel, 2009). Activity level of the DLPFC is found to be

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correlated with the ‘goal value’ of food (Hare, et al., 2009) and carving attenuation

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(Scharmüller, Übel, Ebner, & Schienle, 2012). The ACC is a gateway between bottom-up

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mesolimbic reward responses and top-down cognitive control (Matthews, Paulus, Simmons,

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Nelesen, & Dimsdale, 2004). The region critically impacts executive function by mediating

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attention to action and conflict monitoring (Hinton, et al., 2004). Both the DLPFC and ACC

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are part of the inhibitory control system that regulates feeling of satiety tied to reward value of

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food (Bruce, et al., 2010; Davids, et al., 2009). Our data demonstrated that cognitive restraint

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of eating was correlated with the GMV in inhibitory cortices, i.e., the smaller the GMV in the

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DLPFC and ACC, the less inhibitory control there was in the obese group.

Additionally, the cognitive restraint of eating scores were positively correlated with the GMV in several cortices involving in visual semantic processing, including the occipital and

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temporal regions, and the parietal cortex implicated in visual attention processing. Posterior

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occipital regions and temporo-parietal cortices respond more to high-calorie food than to

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low-calorie food stimuli, and the visual semantic processing of food has been suggested to

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influence this response to the food stimuli (Toepel, Knebel, Hudry, le Coutre, & Murray,

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2009). The parietal cortex was thought to take part in attention manipulation during visual

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processing of food (Dong, et al., 2014). Food-related visual stimuli also elicited greater

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responses in the parietal cortex when participants were in a hungry state relative to a satiated

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state (Baicy, et al., 2007; LaBar, et al., 2001). Resting state functional imaging study noted

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that restrained eaters exhibited higher temporal synchronization of spontaneous fluctuations

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in the parietal cortex (Dong, et al., 2014). Because these regions are essential components of

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the brain networks for cognitive processing of ingested food. An augmentation of GMV in

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these neuro-loci may indicate a benefit of evaluating food cues more attentively when food

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stimuli are present.

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In the current study, several brain areas including the lingual gyrus, posterior and anterior

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parts of middle temporal gyrus, fusiform gyrus and inferior parietal lobule were positively

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correlated with cognitive restraint of eating while negatively correlated with BMI. Earlier

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evidence has pointed out that neural activity in the temporal cortex is inversely associated with

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weight loss (Boraxbekk, et al., 2015). For example, dietary weight loss was shown to increase

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brain activity in the superior/middle temporal gyri (Boraxbekk, et al., 2015). When compared

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to behavioral dieters, patients after bariatric surgery demonstrated elevated brain activation in

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the bilateral temporal cortex following weight loss (Bruce, et al., 2014). One neurophysiology

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study found leptin-reversibly increased neural activity in response to visual food cues in the

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middle temporal gyrus and lingual gyrus after weight loss (Rosenbaum, Sy, Pavlovich, Leibel,

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& Hirsch, 2008). Our results extended these findings, illustrating a close link of cognitive

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restraint of eating, BMI and the GMV in the middle temporal gyrus. These data add the

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knowledge to the development of a behavioral and neuro-response model that may help better

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understand and achieve weight loss.

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Our results revealed that both cognitive restraint of eating and BMI were negatively correlated with the GMV in the putamen. The putamen is located near the lentiform nucleus in

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the dorsal striatum. Dopamine within the caudate-putamen is believed to be integral for the

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integration and regulation of motor and cognitive functions such as learning and memory

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(Green, Jacobson, Haase, & Murphy, 2011). The caudate and putamen were previously

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reported to participate in habit-learning, and as such, probably driving compulsive behaviors

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in drug addiction (Volkow, et al., 2006). Other investigations have identified a role of the

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putamen in eating regulation. Brooks et al. found increased activation in the lentiform nucleus

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in the obese/overweight subjects when they were exposed to visual foods versus non-food

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stimuli (Brooks, Cedernaes, & Schiöth, 2013). Ochner et al. reported reductions in the desire

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to eat high vs. low calorie food after gastric bypass surgery treatment, and this reduction could

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be predicted by neural responses to either type of food in the lentiform nucleus (Ochner, et al.,

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2012). The heightened functional connectivity of putamen in the salience network has been

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recognized in the obese group before, and it was postulated to contribute to overeating due to

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an imbalance between autonomic processing and reward processing of food stimuli

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(Garcia-Garcia, et al., 2013). Our finding of an augmented GMV in the putamen may imply an

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enhanced drive for compulsive eating behaviors, which may corrupt the cognitive restraint of

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eating and in turn cause weight gain. This result echoes the idea of appetite regulation with

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cognitive reappraisal in that cognitive regulation could stimulate frontal inhibition system

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while reducing the influence of the dopamine reward (Giuliani, Calcott, & Berkman, 2013;

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Hollmann, et al., 2012).

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We observed that the disinhibition scores were negatively correlated with the GMV in the

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left middle frontal gyrus. The middle frontal gyrus (BA 6) is mainly involved in inhibition and

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working memory (Owen, McMillan, Laird, & Bullmore, 2005). Activation of the left BA 6

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was shown to be lower in obese binge eaters as compared to obese subjects without binge

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eating (Giessen, 2012). The negative correlation we identified might implicates a

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disinhibition associated with an atrophied left BA6.

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In regards to the hunger scores, we found them to be positively correlated with the GMV

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in the hypothalamus. The hypothalamus is a key component of the central nervous system for

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maintaining energy homeostasis (Ho, Kennedy, & Dimitropoulos, 2012). Caloric-dense food

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images trigger greater activation in the hypothalamus than neutral food images (Cornier, Von

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Kaenel, Bessesen, & Tregellas, 2007). Overfeeding leads to diminished hypothalamic

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activation (Cornier, et al., 2009; Holsen, et al., 2012). The correlation between the hunger

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scores and the GMV in the hypothalamus may suggest that susceptibility to hunger is related

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to both the functional and structural integrity of the hypothalamus. We further discovered that the GMV in the right superior frontal gyrus (BA 10) and

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bilateral middle frontal gyrus (BA 9) were positively correlated with the hunger scores and

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negatively correlated with BMI. The DLPFC has been indicated in working memory during

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food evaluation. The experiences that encourage more accurate food recall can enhance

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appetite (Higgs, 2005). The greater GMV in the DLPFC may serve as a strong recall of food

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and promote a tighter association with hunger. The obese children did display a higher

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activation of the DLPFC in response to food pictures than normal weight group (Bruce, et al.,

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2010; Davids, et al., 2009), they also experience hunger more intensely. This observation is in

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line with the evidence that the GMV elevation within the DLPFC may contribute to hunger

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regulation (Harris, Hare, & Rangel, 2013). Our own data suggest that working memory of

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food stimuli or of eating foods may be important for homeostatic weight maintenance.

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Limitations

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The current study employed the regression model to analyze the relationship between

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alterations of brain structures and eating behaviors and BMI. The analysis employed only

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cross-sectional data and was unable to infer any cause-effect relationship of the eating

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behavior changes, brain structure and BMI alterations. Future studies should employ

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longitudinal data to elucidate possible causal interactions among the three aspects eating

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behaviors and brain structures. Moreover, we had intended to explore the brain structural

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alterations that may be associated with eating behaviors at a range of whole brain scale.

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However, we observed insignificant brain activations after performing FDR correction

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considering the whole brain range. One reason could be that we included BMI as one of the

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covariates since extensive brain structure changes were found to be associated with BMI

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(Table 2). We did choose a comparatively lower statistics threshold as a result (Eckart, et al.,

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2011; Franklin, et al., 2013). This approach, while likely lowers the risk of false activation. In

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the current study, disinhibition score of 57.94% subjects are lower than 4. The disinhibition

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scores is lower for overweight and obese subjects than it was reported in the previous

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literatures (Castellanos, et al., 2009; Foster, et al., 1998), and thus there is not much variability.

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The low disinhibition score may be specific to the subjects used in the current study. The

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regression analysis also fails to explore the association of brain structural alteration with

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disinhibition score. It is a limitation for the current study. Future studies should also examine

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the relationship of brain structures alterations and disinhibition with larger number of

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subjects.

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Conclusion In conclusion, the current study investigated the association between alterations of brain

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structure, eating behaviors and BMI. These findings demonstrated that eating behavior traits

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were tightly related to brain structures implicated in habitual learning and cognitive control.

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Furthermore, the altered brain structures were associated with both the eating behaviors and

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the BMI. These findings add to our understanding of the neural basis underlying eating

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behaviors and obesity.

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Figure and Table Legends:

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Fig. 1 GMVs exhibiting positive (red) and negative correlation with factors of TFEQ and BMI (p = 0.001, cluster size > 5). (a) GMVs correlated with cognitive restraint of eating. (b) GMVs correlated with disinhibition. (c) GMVs correlated with hunger. (d) GMVs correlated with BMI.

512 513 514 515 516 517 518 519 520 521 522 523

Fig. 2 Brain structures present common alterations and association with BMI and TFEQ factor (p = 0.005, cluster size > 10). (a) Brain regions positively related with BMI and negatively related with cognitive restraint of eating. (b) Brain areas negatively related with BMI and positively related with cognitive restraint of eating. (c) Brain regions both negatively related with BMI and disinhibition. (d) Brain areas negatively related with BMI and positively related with Hunger. Table 1 Mean Three Factor Eating and BMI Table 2 Brain regions significantly related with Three Factor Eating Questionnaire factors (P = 0.001, cluster size > 5 voxels) Table 3 Brain regions significantly related with BMI (P = 0.001, cluster size > 10 voxels) Table 4 Brain regions of the conjunction analysis (P = 0.005, cluster size > 10 voxels)

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Group

Normal weight

Overweight

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Table 1 Mean Three Factor Eating Questionnaire scores and BMI Obesity

Total

Mean ± SD

Range

Mean ± SD

Range

Mean ± SD

Range

Mean ± SD

Cognitive restraint of eating

0 - 18

6.75 ± 4.56

2 - 16

7.90 ± 3.67

0 - 20

7.13 ± 5.26

0 - 20

7.20 ± 4.54

Disinhibition Hunger

0 - 13 0 - 13

4.09 ± 3.35 3.59 ± 2.93

0 - 10 1 - 12

3.61 ± 3.12 4.32 ± 3.33

0 -11 1 - 14

4.28 ± 3.19 5.43 ± 3.27

0 - 13 0 - 14

4.01 ± 3.22 4.35 ± 3.22

18.90 - 24.82

22.15 ± 1.81

25.09 - 29.98

27.76 ± 1.48

30.07 - 50.07

34.87 ± 4.70

16.31 - 50.07

27.58 ± 6.05

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BMI

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Range

ACCEPTED MANUSCRIPT Table 2 Brain regions significantly related with Three Factor Eating Questionnaire factors (p = 0.001, cluster size >5 voxels) MNI Region BA Voxel Z X Y Z

Brain regions positively related with cognitive restraint of eating score

524 1005

172 128 42 58 62 131

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4.62 3.78 3.51 4.24 3.75 4.08 3.63 3.56 3.86 3.4 3.8 3.78 3.62 3.56 3.53 3.52 3.42 3.35 3.34 3.32 3.28 3.27 3.21 3.19 3.16

-50 -51 -59 -56 -51 6 5 15 35 45 -20 -36 -21 -6 59 54 50 56 59 62 -6 48 44 0 44

133

43 14 17 11 8 5

-72 -57 -70 -31 -34 -90 -79 -82 -82 -84 15 -84 9 -58 -46 -51 -64 -67 -57 -31 -78 -64 -37 -70 20

-6 -8 3 28 39 4 4 6 -12 -12 46 -6 69 18 1 12 -11 -6 -11 -20 27 12 48 24 31

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37 37 37 40 40 18 18 18 19 19 32 18 6 23 22 39 37 37 37 20 18 37 40 31 9

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BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA BA

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Middle Occipital Gyrus Fusiform Gyrus Inferior Temporal Gyrus Inferior Parietal Lobule Inferior Parietal Lobule Lingual Gyrus Lingual Gyrus Lingual Gyrus Inferior Occipital Gyrus Fusiform Gyrus Cingulate Gyrus Middle Occipital Gyrus Superior Frontal Gyrus Posterior Cingulate Middle Temporal Gyrus Superior Temporal Gyrus Fusiform Gyrus Middle Occipital Gyrus Middle Temporal Gyrus Inferior Temporal Gyrus Cuneus Middle Temporal Gyrus Inferior Parietal Lobule Precuneus Inferior Frontal Gyrus

Brain regions negatively related with cognitive restraint of eating score

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Lentiform Nucleus Inferior Frontal Gyrus

Putamen 6 5 BA 45

3.16 3.15

26 50

20 32

-3 0

-1 -43 -40

42 1 -6

-9 -30

-61 5

70 64

66 62 12

-36 -42 -9

-3 3 -11

Brain regions positively related with disinhibition score subgyrals Middle Temporal Gyrus Middle Temporal Gyrus

BA 6 BA 22 BA 21

23 190

4.04 3.87 3.76

-26 60 63

Brain regions negatively related with disinhibition score Precuneus Middle Frontal Gyrus

BA 7 BA 6

7 9

3.38 3.19

Brain regions positively related with hunger score Middle Temporal Gyrus Middle Temporal Gyrus Hypothalamus

BA 21 BA 22 *

186 37

3.67 3.42 3.45

ACCEPTED MANUSCRIPT Parahippocampal Gyrus Parahippocampal Gyrus Anterior Cingulate

BA 34 BA 36 BA 32

6 5

3.3 3.2 3.19

12 -23 29

-3 -43 36

-17 -11 16

54 59 -26 11

-6 -27 12 -25

-45 -32 57 57

BA BA BA BA

20 20 6 6

116 43 11 14

3.45 3.41 3.37 3.26

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Inferior Temporal Gyrus Fusiform Gyrus Middle Frontal Gyrus Medial Frontal Gyrus

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Brain regions negatively related with hunger score

ACCEPTED MANUSCRIPT Table 3 Brain regions significantly related with BMI (p = 0.001, cluster size > 10 voxels) MNI Region BA Voxel Z X Y Z

Brain mappings positively related with BMI BA 30 * * Putamen

43 11 11 36

3.79 3.46 3.25 3.23

-20 11 33 26

-48 -37 -57 11

9 -47 -41 -5

-32 26 -8 6 42 -66 -57 -50 -3 0 6 0 72 -6 18 20 -32 -35 72 68 35 -33 54

-9 -1 -79 -79 -48 -25 -34 -21 -13 -28 47 38 -24 53 -84 32 -73 -75 -16 -23 -43 -57 3

-51 -51 -48 -41 37 -26 -21 -26 -3 10 7 9 -3 21 -3 -29 16 24 -20 -29 54 -63 -30

3.2

62

3

-36

3.44 3.42 3.41 3.37 3.3 3.28 3.26 3.23 3.22 3.23

-21 -30 47 -17 -42 2 38 62 66 41

27 -72 8 -49 5 -37 -65 -6 -6 23

-27 39 -23 49 -29 69 -60 -39 -32 6

Brain mappings negatively related with BMI

Middle Temporal Gyrus

BA 21

Middle Frontal Gyrus Precuneus Superior Temporal Gyrus Precuneus Superior Temporal Gyrus Paracentral Lobule Loubule VIII Inferior Temporal Gyrus Middle Temporal Gyrus Insula

BA 11 BA 19 BA 38 BA 7 BA 38 BA 5 cerebellum BA 20 BA 21 BA 13

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805 548 994 73 975

5.59 4.92 4.24 3.97 4.08 3.96 3.81 3.78 3.84 3.71 3.81 3.35 3.81 3.79 3.77 3.76 3.76 3.71 3.63 3.31 3.54 3.52 3.45

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BA 20 BA 38 * * BA 40 BA 20 BA 20 BA 20 * Pulvinar BA 32 BA 24 BA 22 BA 9 BA 18 BA 47 BA 30 BA 19 BA 21 BA 20 BA 7 cerebellum BA 21

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Inferior Temporal Gyrus Uncus Inferior Semi-Lunar Lobule Inferior Semi-Lunar Lobule Supramarginal Gyrus Inferior Temporal Gyrus Inferior Temporal Gyrus Fusiform Gyrus Thalamus Thalamus Anterior Cingulate Anterior Cingulate Superior Temporal Gyrus Medial Frontal Gyrus Lingual Gyrus Inferior Frontal Gyrus Posterior Cingulate Middle Occipital Gyrus Middle Temporal Gyrus Inferior Temporal Gyrus Precuneus Loubule VIII Middle Temporal Gyrus

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Precuneus Cerebellar Tonsil Cerebellar Tonsil Lentiform Nucleus

1198 165 89 133 179 141 150 217 153 158 129

35 36 47 11 14 17 37 30 28

ACCEPTED MANUSCRIPT BA 28 BA 8 BA 19

12 16 12

3.21 3.2 3.2

-24 33 -35

-22 41 -75

-18 40 -18

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Parahippocampal Gyrus Middle Frontal Gyrus Fusiform Gyrus

ACCEPTED MANUSCRIPT Table 4 Brain regions of conjunction analysis (p = 0.005, cluster size > 10voxels) MNI Region BA Voxel Z X Y Z

Brain mappings of positively related with BMI and negatively related cognitive restraint of eating Lentiform Nucleus

Putamen

23

2.77

26

17

-5

BA BA BA BA BA BA BA

18 19 37 7 20 22 40

64 10 52 16 46 11 11

3.23 3.02 3 2.91 2.91 2.76 2.76

12 -33 53 -24 69 56 41

SC

Lingual Gyrus Fusiform Gyrus Middle Temporal Gyrus Precuneus Inferior Temporal Gyrus Middle Temporal Gyrus Inferior Parietal Lobule

RI PT

Brain mappings of negatively related with BMI and positively related with cognitive restraint of eating -91 -82 -60 -60 -24 -45 -37

-8 -5 7 39 -24 -2 52

Brain mappings of both negatively related with BMI and disinhibition 25

2.91

M AN U

Inferior Temporal Gyrus

BA 20

-44

-12

-50

Brain mappings of negatively related with BMI and positively related with Hunger BA BA BA BA BA

21 10 9/46 6 9/46

AC C

EP

TE D

Middle Temporal Gyrus Superior Frontal Gyrus Middle Frontal Gyrus Precentral Gyrus Middle Frontal Gyrus

417 49 20 43 10

3.67 2.84 2.79 2.79 2.73

66 30 -32 -17 32

-36 53 41 -12 41

-3 25 31 64 21

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

ACCEPTED MANUSCRIPT

Research highlights:

RI PT

SC M AN U TE D

5.

EP

4.

Cognitive restraint of eating correlated with gray matter volume of DLPFC Disinhibition associated with decreased GMV of left middle frontal gyrus Hunger score positively related to GMV of hypothalamus and visual memory areas Hunger score negatively related with GMV of inferior temporal gyrus and bilateral middle frontal gyrus. Common brain structural change correlated with eating behaviors and BMI.

AC C

1. 2. 3.