Alterations of EEG functional connectivity in resting state obese and overweight patients with binge eating disorder: A preliminary report

Accepted Manuscript Title: Alterations of EEG functional connectivity in resting state obese and overweight patients with Binge Eating Disorder: a preliminary report Author: Claudio Imperatori Mariantonietta Fabbricatore Benedetto Farina Marco Innamorati Maria Isabella Quintiliani Dorian A. Lamis Anna Contardi Giacomo Della Marca Anna Maria Speranza PII: DOI: Reference:

S0304-3940(15)30152-X http://dx.doi.org/doi:10.1016/j.neulet.2015.09.026 NSL 31555

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Neuroscience Letters

Received date: Revised date: Accepted date:

21-7-2015 10-9-2015 22-9-2015

Please cite this article as: Claudio Imperatori, Mariantonietta Fabbricatore, Benedetto Farina, Marco Innamorati, Maria Isabella Quintiliani, Dorian A.Lamis, Anna Contardi, Giacomo Della Marca, Anna Maria Speranza, Alterations of EEG functional connectivity in resting state obese and overweight patients with Binge Eating Disorder: a preliminary report, Neuroscience Letters http://dx.doi.org/10.1016/j.neulet.2015.09.026 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.

Alterations of EEG functional connectivity in resting state obese and overweight patients with Binge Eating Disorder: a preliminary report Claudio Imperatori1*, Mariantonietta Fabbricatore1, Benedetto Farina1, Marco Innamorati1, Maria Isabella Quintiliani1, Dorian A. Lamis2, Anna Contardi1, Giacomo Della Marca3, Anna Maria Speranza4

1Department

of Human Sciences, European University, Rome, Italy

2Department

of Psychiatry and Behavioral Sciences, Emory University School of

Medicine, Atlanta, GA USA 3Institute

of Neurology, Catholic University, Rome, Italy

4Department

of Dynamic and Clinical Psychology, Sapienza University, Rome, Italy

Short title: Functional connectivity in Binge Eating Disorder Number of words: 4935

*Corresponding author: Claudio Imperatori Department of Human Science, European University of Rome, Italy Via degli Aldobrandeschi 190, 00163 Roma. Tel. 06 66 54 38 73 E-mail: [email protected]

Abstract 1

Alterations in brain functional connectivity have been detected in patients with eating disorders, but have not been studied in Binge Eating Disorder (BED). We have investigated electroencephalographic (EEG) functional connectivity in thirteen overweight and obese patients with BED and thirteen overweight and obese patients without BED during RS condition. EEG analyses were conducted by means of the exact Low Resolution Electric Tomography software (eLORETA). Compared to patients without BED, patients with BED demonstrated an increase of lagged phase synchronization in the beta frequency band among the cortical areas explored by FC1T3 (left superior frontal gyrus – left middle temporal gyrus), T5-O1 (left inferior temporal gyrus – left middle occipital gyrus), and C4-O1 (right postcentral gyrus – left middle occipital gyrus) electrodes (T = 4.861, p < 0.05). EEG connectivity values were also significantly related to binge eating symptomatology after controlling for depressive symptoms. Our results may reflect the impairment of frontal control network and visual processing networks, which lead patients with BED to be more vulnerable to food cues and lack of control with regards to over eating.

Key-words: Binge Eating Disorder; EEG Functional Connectivity; eLORETA; Obesity; Overweight

Introduction

Functional connectivity, defined as the degree of correlation among "temporal neurophysiological (functional) measurements made in different brain areas” [1, p. 2

9], is considered to be an important biomarker in many psychiatric and brain diseases [2, 3]. Recent studies have shown that the resting state (RS) of patients with Anorexia Nervosa and Bulimia Nervosa [4-7], as well as obesity [8, 9] is characterized by several alterations in the functional integration between brain areas. To our knowledge, no studies have investigated functional connectivity in patients with Binge Eating Disorders (BED) during RS condition, which is thought to reflect intrinsic activity in the brain revealing valuable information on how different structures communicate [10]. BED has been recently identified as an independent eating disorders (EDs), in the 5th edition of Diagnostic and Statistical Manual of Mental Disorder (DSM-5)[11]. There are several symptoms (e.g., episodic binge eating, body image disturbance) and neurobiological abnormalities (e.g., functional alterations in reward system) that are either overlapping or stay at opposite ends of a clinical spectrum across EDs [for a review see 12]. Binge eating without compensatory behaviors is the hallmark symptom of this eating disorder. Binge eating episodes are characterized by abnormal and excessive food intake in a discrete period of time (i.e., two hours) associated with the experience of loss of control (i.e., eating quickly without controlling what or how much one is eating) and psychological distress (i.e., feeling guilty after binge eating) [11]. From a neurobiological point of view, BED is characterized by functional alteration in different brain areas, such as the prefrontal cortex [13-15], and several neurotransmitter systems, such as opioids and dopamine [for a review see 16]. Neurophysiological correlates of BED have been less extensively explored. Tammela et al. [17] found that, compared to obese women without BED, patients with BED had higher fronto-central electroencephalographic (EEG) beta activity during RS and different tasks (e.g., visual processing of food and control visual task stimuli). 3

EEG is considered a suitable method for investigating functional connectivity across frequency bands in large scale functional networks given that “EEG time-series data directly relate to dynamic postsynaptic activity in the cerebral cortex with a high temporal resolution” [18, p. 2]. Therefore, we extended previous findings by exploring the modifications of EEG functional connectivity in overweight and obese patients with and without BED patients during RS condition.

Methods Participants Thirteen overweight and obese patients with BED (three men and ten women, mean age: 43.01 ± 12.19, mean BMI: 30.23 ± 2.74) and thirteen overweight and obese patients without BED (four men and nine women, mean age: 39.54 ± 10.92, mean BMI: 28.51 ± 1.56), were enrolled in the present study. All patients were admitted to an outpatient medical center in Rome (Italy) that specialized in the treatment of overweight and obesity. Patients were included in the study if they were 18 years or older, and had a BMI of 25 kg/m2 or higher. Exclusion criteria were: left handedness; history of medical, neurologic diseases; psychiatric comorbidity; head trauma; assumption of Central Nervous System active drugs in the 3 weeks before the study; presence of EEG abnormalities at the baseline recording. Patients were assessed between June and December 2014. Clinical and socio-demographic variables of the sample are listed in Table 1. After receiving information about the aims of the study, all patients provided written consent to participate. The study was in accordance with the Helsinki declaration standards and was approved by the local research ethics review board. 4

Measures In order to exclude patients with psychiatric and medical comorbidity, all patients received a complete psychiatric and anamnestic interview performed by a trained psychiatrist (BF), and were diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders 4th edition, text revision (DSM-IV-TR) [19]. All of the participants were also administered the Italian version of the Binge Eating Scale (BES) [20] and the Italian version of Hospital Anxiety and Depression Scale (HADS) [21]. Finally, a ten point visual analogue scale was used to assess the level of hunger at the moment of EEG recording. The BES [22] is a 16-item questionnaire assessing binge eating severity as well as behavioral manifestations and the feelings/cognitions manifestations related to such behavior. Total score ranged from 0 to 46. In the original validation study, the BES successfully discriminated among individuals with or without binge eating tendencies [22]. The Cronbach’s α in the present sample was 0.88. The HADS is a 14-item questionnaire assessing anxiety and depression symptoms in a variety of clinical populations [23]. The anxiety and depression subscales consist of seven items each, which are rated on a four-point scale (0-3), with total subscale scores ranging from 0-21. Although, it was originally developed to screen for depression and anxiety in a hospital setting, the HADS was also widely used to assess anxiety and depression symptoms in general population [23]. The Cronbach’s α in the present sample was 0.81 and 0.85, respectively, for the anxiety and depression subscales.

5

EEG recordings and Connectivity analysis

EEG recordings were performed in an EEG Lab, with each patient sitting in a comfortable armchair, with his/her eyes closed, in a quiet, semi-darkened silent room for 5 minutes. At the moment of EEG recordings, patients were not yet on dietary restriction. Participants were asked to consume regular meals but to refrain from eating and/or drinking (including caffeinated beverages) for 4 to 6 hours prior to their EEG recordings. EEG was recorded by means of a Micromed System Plus digital EEGraph (Micromed© S.p.A., Mogliano Veneto, TV, Italy). EEG montage included 31 standard scalp leads positioned according to the 10-20 system (recording sites: Fp1, AF3, F3, FC1, C3, CP1, P3, PO3, O1, F7, FC5, T3, CP5, T5, Fz, Cz, Pz, Fp2, AF4, F4, FC2, C4, CP2, P4, PO4, O2, F8, FC6, T4, CP6, T6), EOG, and EKG. The reference electrodes were placed on the linked mastoids. Impedances were kept below 5KΩ before starting the recording and checked again at the end of the experimental recording [more details could be found in 24]. The following frequency bands were considered: delta (0.5-4 Hz); theta (4.5–7.5 Hz); alpha (8–12.5 Hz); beta (13–30 Hz); gamma (30.5–60 Hz). Artifact rejection (eye movements, blinks, muscular activations, or movement artifacts) was performed visually on the raw EEG; the recordings were attended by trained technicians, and the simultaneous recording of EOG and EKG further improved the artifact recognition and removal [details of artifact rejection procedure could be found in 24]. At least 120 seconds of EEG artifact-free recording (not necessarily consecutive) were analysed for each participant. The average time analyzed was 283 ± 29 sec. and 292 ± 27 sec. respectively for overweight and obese patients with and without BED. All EEG analysis were performed by means of the eLORETA 6

software [25] a validated method for localizing the electric activity in the brain based on multichannel surface EEG recordings [25]. Lagged phase synchronization index, which has been widely used to investigate electrophysiological connectivity in both psychiatric and brain diseases [18, 26], was chosen to compute connectivity analysis. The eLORETA software computes lagged phase synchronization “ , by the formula [27]:

Additional details regarding the eLORETA connectivity algorithm can be found in Pascual-Marqui’s studies [27]. Thirty-one regions of interest (ROIs) corresponding to the site of the electrode (one for each scalp electrode) were defined in order to evaluate the connectivity [details of ROIs selection could be found in 28].

Statistical analysis EEG connectivity was compared in patients with BED versus patients without BED for each discrete frequency band. Comparisons were performed using the statistical non-parametric mapping (SnPM) methodology supplied by the LORETA software [29], which is based on the Fisher’s permutation test. The non-parametric randomization procedure, which can be found in the eLORETA program package, was used for the correction of significance for multiple testing. For all comparisons, the eLORETA statistical package provides experimental values of T, and a two-level Tthreshold for statistical significance; the T-thresholds are the values of T corresponding to a significance of p < 0.01 and p < 0.05 [30]. 7

Two-way chi-squared (χ2) and t tests were used to analyze differences between groups, respectively, for N x N contingency tables and dimensional measures. Pearson’s r correlation coefficients were reported as measures of associations among the BES total scores, behavioral subscale of BES, feelings/cognitions subscale of BES and any significantly modified connections between ROIs. Variables which showed significant differences in the bivariate analysis (BED vs non BED patients) were included as control variables in a partial correlation (rp) analyses. All analyses were performed with the Statistical Package for Social Science (SPSS®) software version 19.

Results

EEG recordings suitable for the analysis were obtained in all patients. Visual evaluation of the EEG recordings showed no relevant modifications of the background rhythm frequency, focal abnormalities or epileptic discharges. No participants showed evidence of drowsiness or sleep during the recordings. Differences between groups are listed in Table 1. No significant differences between groups for socio-demographic and clinical variables were observed. Compared to patients without BED, patients with BED reported higher BES (22.54 ± 5.74 vs. 3.69 ± 2.49; t24 = 10.86, p < 0.001), Feelings/Cognitions (11.15 ± 3.69 vs. 1.77 ± 1.36; t15.21 = 8.56, p < 0.001), Behaviors (12.15 ± 2.88 2.01 ± 1.47; t17.86 = 11.31, p < 0.001) and HADS-D subscale total scores (8.92 ± 2.45 vs. 3.01 ± 2.89; t24 = 5.63, p < 0.001). 8

Connectivity analysis In the comparison of EEG connectivity between patients with and without BED, the thresholds for significance were T = 4.617 (p < 0.05) and T = 5.281, (p < 0.01). Compared to patients without BED, patients with BED showed an increase of beta lagged phase synchronization among the cortical areas explored by FC1-T3 (left superior frontal gyrus – left middle temporal gyrus), T5-O1 (left inferior temporal gyrus – left middle occipital gyrus), and C4-O1 (right postcentral gyrus – left middle occipital gyrus) electrodes (T = 4.861, p < 0.05). No significant differences were observed in the other frequency bands.

Association between modified connections ROIs and binge eating symptomatology BES total scores were significantly correlated with the modified connections between ROIs in beta band (r > 0.66, p < 0.001). Furthermore, both the Feelings/Cognitions subscale and Behaviours subscale were positively correlated with all interconnected ROIs (r ≥ 0.56, p < 0.01). The association among BES total scores, Feelings/Cognitions and Behaviours subscales and interconnected ROIs were significant after controlling for depressive symptoms (rp ≥ 0.41, p < 0.05). Detailed partial correlations are reported in Table 2.

Discussion

The principal aim of the present study was to explore the modifications of EEG functional connectivity in overweight and obese patients with BED during RS condition. Compared to patients without BED, individuals with BED showed an 9

increase of beta EEG connectivity in a network involving the left superior frontal gyrus, left middle temporal gyrus, left inferior temporal gyrus, left middle occipital gyrus, and right postcentral gyrus. EEG connectivity values were also moderately and positively related with BE symptomatology after controlling for depressive symptoms, which are known to be associated with BED [for a review see 31]. Our results are in line with previous EEG and functional imaging studies which showed the alterations of frontal-temporal areas and occipito-temporo-parietal areas in BED [13-15]. Schienle et al. [14] reported that, compared to controls, patients with BED in response to food stimuli showed increased activation in the orbitofrontal cortex, anterior cingulate cortex, and insula. In a magnetoencephalographic study with patients with BED, Hege and colleagues [15] detected alterations in the prefrontal control network and visual processing system during a food-related visual go–nogo task. Balodis et al. [13], using a monetary reward/loss task, showed that obese patients with BED reported a decrease of bilateral ventral striatal activity during anticipatory reward/loss processing. In an EEG study, Tammela et al. [17] reported greater fronto-central beta activity in obese women with BED compared to obese women without BED during RS condition, food stimuli presentation, and during a control visual task. It is possible that the increase of EEG beta connectivity observed in BED patients reflects an impairment of frontal control network and visual processing network, which lead patients with BED to be more vulnerable to food cues and lack control with regards to over eating. Several studies reported that frontal-temporal areas and occipito-temporo-parietal areas are respectively involved in behavioral disinhibition and in visual food stimuli processing. In a fMRI study during a stop-signal task, Li et al. [32] demostred that superior medial frontal brain areas, including superior frontal gyrus, are crucial in inhibition response. Similarly, several studies have 10

suggested that occipito-temporo-parietal areas play an important role in visual food stimuli processing. Frank et al. [33] reported that compared to non-food stimuli, high caloric food pictures are associated with greater activation of occipital gyrus than brain reward structures, such as insula. The involvement of the occipital gyrus in food related stimuli processing was also detected in both obese and healthy children [34]. Simmons et al. [35] found that viewing food pictures, relative to control pictures, produced an increase in bilateral ventral occipito-temporal areas including the inferior occipital gyrus and the inferior temporal gyrus. Postcentral gyrus is also involved in food cues processing. Several studies reported that both pre and postcentral gyrus are implicated in taste perception and their activation were observed in relation to food cues exposure [33, 36-38]. Geliebter et al. [37] suggested that the activation of these brain areas may reflect past or concurrent motor planning about eating binge foods. Nevertheless, it is important to note that our interpretation remains largely speculative due to the absence of visual food related task/inhibition in the present study. Notably, the modification in connectivity occurred selectively in the beta frequency band. Beta frequencies are indices of hyper-excitability and increased RS beta activity and beta coherence were observed in several mental disorders characterized by impulsivity and behavioral disinhibition, such as Intermittent Explosive Disorder [39], alcoholism [40-42] and other substance use disorders [43, 44], and BED [17]. In BED, as well as in alcoholism, it has been proposed that hyperexcitability is produced by an excitation–inhibition imbalance [17, 41, 45]. At the neuro-pharmacological level, EEG beta frequencies have been associated with γamino-butyric acid (GABA) neurotransmitter action [46, 47]. Substantial evidence suggests that GABA and glutamate modulation pathways might be potential targets for the treatment of BED [for a review see 48]. Psychotropic drugs such as topiramate, 11

which modulate GABAergic and glutamatergic neurotransmission, have been shown to be effective in the treatment BED symptomatology, including impulsivity and lack of control with regards to over eating [49]. Moreover, baclofen, which has specific effect on GABAergic transmission, is effective in reducing binge frequency relative to placebo in individuals exhibiting binge eating symptoms [50]. The present study has several limitations. The most important limitation is the small sample size which makes it difficult to draw definitive conclusions. As such, the findings are promising but they must be considered preliminary until they are either replicated or refuted in larger samples. Furthermore, in our clinical sample, the BMI range was limited, and we did not assess patients according to obesity classes II and III with BED, which may present different EEG connectivity patterns.

Conclusion In conclusion, our results revealed that overweight and obese patients with BED are characterized by increased RS EEG beta functional connectivity in frontaltemporal and occipito-temporo-parietal areas. This neurophysiological pattern may reflect a hyper-excitability of the frontal control and visual processing networks, which could contribute to BED patients’ vulnerability to food cues and over eating controllability. Furthermore, a clinical implication of our study includes “identifying how specific ED behaviors are linked to particular neurobiological mechanisms could help better categorize ED subgroups and develop specific treatments” [12, p. 1]. Our results highlight the importance of BED and other EDs treatment approaches which focus on the neuro-behavioral correlates of self-regulating, such as neurofeedback [51]. Finally, it can be hypothesized that the definition of a dysfunctional brain network could help to choose specific pharmacological treatment. 12

Conflict of interest: none

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Table 1 Demographic and clinical data of patients Patients with BED (n=13)

Patients without BED (n=13)

Test

Sign

Age- mean (SD)

43.01 (12.19)

39.54 (10.92)

t24 = 0.76

0

Females- N (%)

10 (76.9 %)

9 (69.2 %)

χ21 = 0.19

0

School attainment > 13 years- N (%)

4 (30.8 %)

5 (38.5 %)

χ21 = 0.17

0

Unmarried- N (%)

2 (15.4 %)

1 (7.7 %)

χ21 = 0.38

0

Unemployed- N (%)

4 (30.8 %)

3 (23.1%)

χ21 = 0.19

0

Alcohol use in the last 6 months- N (%)

1 (7.7 %)

4 (30.8 %)

χ21 = 2.23

0

Variables

17

4 (30.8 %)

8 (61.5 %)

χ21 = 2.48

0

5 (50 %)

2 (22. 2 %)

χ21 = 1.57

0

BMI- mean (SD)

30.23 (2.74)

28.51 (1.56)

t17.61 = 0.84

0

Hungry at the moment of EEG- mean (SD)

4.85 (1.92)

4.31 (1.89)

t24 = 0.72

0

BES- mean (SD)

22.54 (5.74)

3.69 (2.49)

t24 = 10.86

<

Feelings/Cognitions- mean (SD)

11.15 (3.69)

1.77 (1.36)

t15.21 = 8.56

<

Behaviors mean (SD)

12.15 (2.88)

2.01 (1.47)

t17.86 = 11.31

<

HADS-A- mean (SD)

8.23 (4.01)

5.69 (2.84)

t24 = 1.87

HADS-D- mean (SD)

8.92 (2.47)

3.01 (2.89)

t24 = 5.63

Tobacco use in the last 6 months- N (%) Menopause- N (%)

0 <

Abbreviations: BMI = body mass index; BES = Binge Eating Scale; HADS-A = Hospital Anxiety and Depression Scale – Anxiety; HADS-D = Hospital and Depression Scale – Depression

Table 2 Values of partial correlation’s coefficient among values of interconnected lagged phase synchronization ROIs, BES total scores and BES subscales controlling for HAM-D. Significant correlations are in bold font with stars (*) Beta FC1-T3 0.57**

Beta T5-O1 0.63***

Beta C4-O1 0.58**

Feelings/ Cognitions

0.41*

0.52**

0.43*

Behaviours

0.63***

0.64***

0.65***

Controlling for HADS-D

BES-Total

*

p < 0.05; ** p < 0.01; *** p < 0.001 BES = Binge Eting Scale; HADS = HADS-D = Hospital Anxiety and Depression Scale – Depression subscale

Figure 1

Legend to Figure 18

Results of the eLORETA comparison of EEG lagged phase synchronization in beta frequency bands. Threshold values (T) for statistical significance (corresponding to p <0.05) are reported in the right side of the figure; red lines indicate connections which presented increase of coherence; blue lines would indicate reduction of coherence (not present). Compared to patients without BED, patients with BED showed an increase of beta lagged phase synchronization among the cortical areas explored by FC1-T3, T5-O1, and C4-O1 electrodes. Abbreviations: R = right; L = left;

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