theta training in a non-clinical sample

INTPSY-11202; No of Pages 9 International Journal of Psychophysiology xxx (2016) xxx–xxx

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Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample Claudio Imperatori a,⁎, Enrico Maria Valenti a, Giacomo Della Marca b, Noemi Amoroso a, Chiara Massullo a, Giuseppe Alessio Carbone a, Giulia Maestoso a, Maria Isabella Quintiliani a, Anna Contardi a, Benedetto Farina a a b

Department of Human Sciences, Università Europea, Rome, Italy Sleep Disorders Unit, Institute of Neurology, Catholic University, Rome, Italy

a r t i c l e

i n f o

Article history: Received 15 August 2016 Received in revised form 8 November 2016 Accepted 11 November 2016 Available online xxxx Keywords: Food craving EEG-neurofeedback Alpha/theta training EEG power spectra eLORETA

a b s t r a c t The aim of the present study was to explore the usefulness of the alpha/theta (A/T) training in reducing Food Craving (FC) in a non-clinical sample. The modifications of electroencephalographic (EEG) power spectra associated with A/T training was also investigated. Fifty subjects were enrolled in the study and randomly assigned to receive ten sessions of A/T training [neurofeedback group (NFG) = 25], or to act as controls [waiting list group (WLG) = 25]. All participants were administered the Food Cravings Questionnaire-Trait, the Eating Disorder Examination Questionnaire and the Symptom Checklist-90-Revised. In the post training assessment, compared to the WLG, the NFG showed a significant reduction of intentions and plans to consume food (F1; 49 = 4.90; p = .033; d = 0.626) and of craving as a physiological state (F1; 49 = 8.09; p = .007; d = 803). In NFG, changes in FC persisted after 4 months follow-up. Furthermore, A/T training was associated with significant a increase of resting EEG alpha power in several brain areas involved in FC (e.g., insula) and food cue reactivity (e.g., parahippocampal gyrus, inferior and superior temporal gyrus). Taken together, our results showed that ten sessions of A/T training are associated with a decrease of self-reported FC in a non-clinical sample. These findings suggest that this brain-directed intervention may be useful in the treatment of dysfunctional eating behaviors characterized by FC. © 2016 Elsevier B.V. All rights reserved.

1. Introduction The construct of craving is considered a key feature in Substance-related and Addictive Disorders (Sinha, 2013; Tiffany and Wray, 2012), functionally related to the maintenance of addictive behaviors (Everitt, 1997) as well as to relapse rates (Oslin et al., 2009). Craving has been defined as a strong motivational state characterized by “intense desires typically relating to the anticipation of consuming pleasure-producing substances or engaging in hedonic behaviors” (Potenza and Grilo, 2014, p. 1). In the last decades several studies suggested that craving for food may also be clinically relevant for the understanding and treatment of obesity and eating disorders (EDs) (Potenza and Grilo, 2014). Food Craving (FC) has been defined as an intense desire to consume a particular food, which is extremely difficult to resist (Weingarten and Elston, 1990, 1991; White et al., 2002), and it is characterized by several crucial features such as the lack of control over eating (Meule et al., 2012a). FC is widespread detected in general population (Gendall et al., 1997a; Lafay et al., 2001; Pelchat, 1997) as well as in patients with EDs (Gendall et al., ⁎ Corresponding author at: Via degli Aldobrandeschi 190, 00163 Rome, Italy. E-mail address: [email protected] (C. Imperatori).

1997b; Moreno et al., 2009; Ng and Davis, 2013). A recent meta-analysis (Boswell and Kober, 2016) on 3292 individuals showed that the experience of craving significantly contributes to dysfunctional eating behaviors and weight gain. Furthermore, FC appears to be a risk factor in precipitating binge eating episodes both in healthy subjects (Cepeda-Benito et al., 2000a, 2000b) and in patients with EDs (van der Ster Wallin et al., 1994; Waters et al., 2001), and it may also discriminate between successful and unsuccessful dieters (Meule et al., 2012b). In overweight and obese patients, FC is associated with future high-caloric food intake (Martin et al., 2008) and may discriminate between patients with and without binge eating (Innamorati et al., 2015). FC severity is also positively related to both body mass index (BMI) (Delahanty et al., 2002; Franken and Muris, 2005; Meule et al., 2014a; White et al., 2002) and drop-out from weight lost programs (Meule et al., 2012b; Meule et al., 2011; Sitton, 1991). Finally, under a neurobiological point of view, several studies observed parallels between brain regions involved in FC and drug craving (e.g., anterior cingulate cortex and prefrontal cortex) (for a review see Frascella et al., 2010). Given the crucial role of FC in obesity and EDs as well as the neurobiological overlapping with drug craving, several treatments have been implemented in order to reduce this crucial symptom. Previous studies reported that both pharmacological (for a review see Billes and

http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010 0167-8760/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

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Greenway, 2011; Billes et al., 2014) and psychotherapeutic treatments (Abiles et al., 2013; Alberts et al., 2010; Hill et al., 2011) may reduce FC in obese and EDs patients. Furthermore, a recent meta-analysis showed that non-invasive neuro-stimulation techniques, such as repetitive trans-cranial magnetic stimulation and transcranial direct current stimulation (tDCS), were effective in decreasing craving levels for substances as well as for high palatable food (Jansen et al., 2013). Although these treatments are generally well tolerated, it has been recently suggested (Bartholdy et al., 2013) that the combination of cognitive therapy and neuromodulation elements, using feedback-based treatment (i.e. biofeedback and neurofeedback), may be effective in reducing EDs psychopathology, including FC. Furthermore, whereas other neuro-stimulation technique (i.e., tDCS) may be associated with several mild and transient adverse effects (Brunoni et al., 2011), it has been reported that electroencephalographic neurofeedback (EEG-NF) is not related to side effects (Lansbergen et al., 2011). Finally, EEG-NF seems to be an easy and affordable technique for general practices and clinicians (Sherlin et al., 2011; Thatcher, 2014). As concerns the effectiveness of feedback-based treatment in EDs few studies are available. Meule et al. (2012a, 2012b) reported preliminary evidence for the effectiveness of heart rate variability biofeedback in decreasing FC in individuals with strong craving for food. Lackner et al. (2016) showed that in anorexic women EEG-NF (i.e., alpha frequency training) was associated with an improvement in eating behavior traits (e.g., dieting), emotion regulation as well as with significant modifications in resting EEG parameters. Furthermore, Schmidt and Martin (2015, 2016) reported that in non-clinical individuals with overeating, ten sessions of beta training NF are associated with a decrease of overeating episodes. Authors also detected a significant reduction in FC severity from pre-treatment to 3 months follow-up (Schmidt and Martin, 2015, 2016). Finally, Ihssen et al. (2016), in a functional Magnetic Resonance Imaging (fMRI)-based NF study, showed that fMRI feedback may reduce brain areas activation (i.e., insula and amygdala) during exposure to palatable food pictures. Regarding NF training in Substance-related and Addictive Disorders, alpha/theta (A/T) training has been the most widely studied (Trudeau, 2005). This training was originally employed to facilitate autosuggestion in hypnagogic states in order to improve standard therapy approaches in substance abuse treatment programs and appears the most suitable in alcoholism (Trudeau, 2005). The goal of this training is to raise posterior theta (4.5–7.5 Hz) over alpha (8–12.5 Hz) amplitude with eyes closed without falling asleep. Typically, on eye closure the EEG displays high amplitude rhythmic alpha activity associated with shallow relaxation. Progressive increase of theta activity is associated with a deeper relaxation (Gruzelier, 2014). This would enhance well-being and the ability to better tolerate stress, during anxiety situation associated with addiction (i.e., withdrawal symptoms, craving) (Dehghani-Arani et al., 2013; Peniston and Kulkosky, 1989; Scott et al., 2005). Several studies showed the effectiveness of A/T NF training in decreasing substances craving (for a review see Sokhadze et al., 2008). Furthermore, previous reports have also shown similar EEG abnormalities between substance craving (for a review see Parvaz et al., 2011) and FC (Imperatori et al., 2015; Meule et al., 2013). To the best of our knowledge, no studies have investigated the potential role of A/T training in FC. Therefore, the main aim of the present study was to explore the usefulness of the A/T training in reducing FC in a non-clinical population. Furthermore, according to suggestions by Schmidt and Martin (2015), we also investigated the modifications of EEG power spectra associated with A/T training.

explained the NF procedure, and we stated that it consisted essentially in a relaxation technique. In order to avoid the participants' awareness of the experimental hypotheses, we did not reveal to the participants any hypotheses regarding the possible benefits of A/T training on FC. Study participants contributed voluntarily and anonymously after providing informed consent, and were free to drop out of the study at any moment. They did not receive payment or any other compensation (i.e., academic credit). The enrollment lasted from November 2015 to May 2016. Inclusion criteria were: normal- or over-weight (BMI = 18.50–29.99 kg/m2); age between 18 and 40 years, both genders. Exclusion criteria were: obesity (BMI ≥ 30 kg/m2); underweight (BMI ≤ 18.49 kg/m2); history of medical, psychiatric (including eating disorders) and/or neurologic diseases; head trauma; assumption of central nervous system active drugs in the two weeks prior to assessment (pre and post assessment); nutritional treatment (e.g., dietary restrictions) at the moment of assessment (pre and post assessment). A checklist with dichotomous items was used to assess inclusion criteria and exclusion criteria. Sixty-two respondents were assessed for eligibility. Fifty individuals fulfilling the inclusion criteria were enrolled in the present study (fourteen men and thirty-six women, mean age: 22.90 ± 2.68 years, mean BMI 21.93 ± 3.41). After receiving information about the aims of the study all subjects provided written consent to participate in the study, which was performed according to the Helsinki declaration standards and was approved by the ethics review board of the European University. Altogether, four subjects were lost to follow-up. Details on participant flow, are reported in the Consolidated Standards of Reporting Trials (CONSORT) diagram (Fig. 1). 2.2. Study design and procedures 2.2.1. Pre-treatment phase (T0) After giving written informed consent, all participants were administered the Food Cravings Questionnaire-Trait (FCQ-T; Cepeda-Benito et al., 2000a, 2000b), the Eating Disorder Examination Questionnaire (EDE-Q; Fairburn and Beglin, 1994) and the Symptom Checklist-90-Revised (SCL-90-R; Derogatis, 1977). All participants also completed a checklist assessing socio-demographic (e.g., age, educational attainment) and clinical data (e.g., height and weight, tobacco and alcohol use in the last six months). After the assessment, all participants underwent a resting state (RS) EEG recording. 2.2.2. Neurofeedback training According with a randomized, controlled study design, participants were randomly assigned to receive neurofeedback training [neurofeedback group (NFG) = 25], or to act as controls [waiting list group (WLG) = 25], with the constraint that the groups would be matched regarding sex. 2.2.3. Post-treatment phase (T1) At the end of NF, all participants (i.e., NFG and WLG) were asked to complete the FCQ-T, the EDE-Q and the SCL-90-R again, and to perform another RS EEG recording. 2.2.4. Follow-up session (T2) Participants were finally administered the FCQ-Trait 4 months after the last training session. 2.3. Questionnaires

2. Materials and methods 2.1. Participants Participants were recruited at the European University of Rome through advertisements posted in the university. In the post, we briefly

The FCQ-T (Cepeda-Benito et al., 2000a, 2000b) is a 39-item questionnaire on 6-point Likert scale (from 1 = never to 6 = always) assessing FC severity. It is composed by nine dimensions detected through factor analysis (Cepeda-Benito et al., 2003; Cepeda-Benito et al., 2000a, 2000b; Franken and Muris, 2005; Moreno et al., 2008): (1)

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

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Fig. 1. Participants flow according to CONSORT guidelines.

anticipation of positive reinforcement from eating (Ant+); (2) anticipation of relief of negative states and feelings from eating (Ant-); (3) intentions and plans to consume food (Intent); (4) cues that may trigger food cravings (Cues); (5) thoughts or preoccupation associated with FC (Thoughts); (6) craving as a physiological state (Hunger); (7) lack of control over eating (Control); (8) emotions that may be experienced before or during FC (Emotions); and (9) guilt from cravings and/or for giving into them (Guilty). Previous studies conducted both in samples of healthy individuals and patients with eating disorders reported that the scale has good internal consistency (Cronbach's alphas between 0.92 and 0.97) (Cepeda-Benito et al., 2003; Cepeda-Benito et al., 2000a, 2000b; Franken and Muris, 2005; Meule et al., 2012b; Moreno et al., 2008; Vander Wal et al., 2007). Also test-retest reliability was satisfactory (Cepeda-Benito et al., 2000a, 2000b; Innamorati et al., 2014; Meule et al., 2012b). In the present study, a previously validated Italian version of the scale was used (Innamorati et al., 2014) and the Cronbach's alpha in the present sample was 0.90 for the total score and ranged between 0.78 for Hunger and 0.93 for Control dimensions. The EDE-Q (Fairburn and Beglin, 1994) is a 28-item questionnaire on 7-point Likert scale (0-6) assessing eating disorder symptoms, with higher scores reflecting greater severity or frequency. The EDE-Q provides a global score based on four subscales (Restraint, Eating Concern, Shape Concern, and Weight Concern) designed to reflect the severity of the main features of eating disorder psychopathology. In the present study, we decided to use EDE-Q global because our focus was on general eating pathology instead of distinct dysfunctional behaviors (e.g. shape concerns). The questionnaire has been widely used and its good psychometric properties have been consistently reported (Berg et al., 2011, 2012). In the present study we used the Italian version of the EDE-Q (Calugi et al., 2016). The Cronbach's alpha in the present sample was 0.88 for the EDE-Q global score. The SCL-90-R (Derogatis, 1977) is a 90-item questionnaire on 5point Likert scale (0–4) widely used to assess psychopathology. It consists in nine primary symptom dimensions: somatization (SOM), obsessive-compulsive symptoms (O-C), interpersonal sensitivity (I-S), depression (DEP), anxiety (ANX), hostility (HOS), phobic anxiety (PAR), paranoid ideation (PAR) and psychoticism (PSY). Furthermore, seven additional items assess disturbances in appetite and sleep (OTHER). The SCL-90-R also provides a global severity index (GSI) which is designed to measure overall psychological distress. Higher scores indicate more psychological symptoms in each subscale as well

as a higher degree of distress, higher intensity of symptoms, and more self-reported symptoms (Sarno et al., 2011). In the present study, a previously validated Italian version of the scale was used (Sarno et al., 2011) and the Cronbach's alpha in the present sample was 0.89 for the GSI. 2.4. EEG recordings and power spectral analysis RS recordings were performed in the European University EEG Lab, with each subject sitting in a comfortable armchair, with his/her eyes closed, in a quiet, semi-darkened silent room for 5 min. In order to avoid alcohol and or caffeine effects on EEG data (Dimpfel et al., 1993; Kahkonen et al., 2003), participants were asked to refrain from drinking alcohol and caffeine for 4 to 6 h immediately before 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 19 standard scalp leads positioned according to the 10–20 system (recording sites: Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2), EOG and ECG. The reference electrodes were placed on the linked mastoids. Impedances were kept below 5 kΩ before starting the recording and checked again at the end of the experimental recording. Sampling frequency was 256 Hz; A/D conversion was made at 16 bit; pre-amplifiers amplitude range was ±3200 μV and low-frequency pre-filters were set at 0.15 Hz. The following band-pass filters were used: HFF = 0.2 Hz; LFF = 128 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 ECG further improved the artifact recognition and removal (more details on the artifact rejection procedure can be found in Imperatori et al., 2013; Imperatori et al., 2014). At least 180 s of EEG artifact-free recording (not necessarily consecutive) were extracted from the eyes closed EEG record for quantitative analysis. All EEG analyses were performed by means of the exact Low Resolution Electric Tomography software (eLORETA), a validated tool for localizing the electric activity in the brain based on multichannel surface EEG recordings (Pascual-Marqui et al., 1994). Power spectral analysis was performed using Fast Fourier Transform algorithm, with a 2 s interval on the EEG signal, in all scalp locations. In the present study we have considered the following frequency bands: delta (0.5–4 Hz); theta (4.5–7.5 Hz); alpha (8–12.5 Hz); beta (13–

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

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30 Hz); and gamma (30.5–60 Hz). EEG frequency analysis was performed using monopolar EEG traces (each electrode referred to joint mastoids). Topographic sources of EEG activities were determined using the eLORETA software. This software calculates the 3-dimensional current distribution throughout the brain volume by assuming that neighboring neurons are activated both simultaneously and synchronously. This is in accordance with results of single cell recordings in the brain (Kreiter and Singer, 1992; Murphy et al., 1992). The computational task is to select the smoothest 3-dimensional current distribution, which is a widely used procedure in signal processing (Grave de Peralta-Menendez and Gonzalez-Andino, 1998; Grave de Peralta-Menendez et al., 2000). The result is a true 3-dimensional tomography, in which the localization of brain signals is conserved with a low amount of dispersion (Pascual-Marqui et al., 2011). 2.5. Neurofeedback training According with previous of neurofeedback studies in non-clinical sample (Schmidt and Martin, 2015; Vernon, 2005) ten neurofeedback sessions was chosen (each session lasted 27 min) for the NFG. ProComp5 Infiniti hardware and Biograph Infiniti software (Thought Technology Ltd., Montreal, Canada) were used for the neurofeedback sessions. During the first session the underlying principles of A/T training were explained to the participants who were instructed to close their eyes and relax as deeply as possible, without falling asleep. In this NF protocol, the participants were asked to close their eyes and listen to the sound being played to them. Two distinct tones were used for alpha and theta reinforcement, with the higher sound related to higher-frequency alpha band. When participants' alpha was higher than theta, a “babbling brook” sound was heard, and when theta was higher than alpha, this changed to “ocean waves”. The aim of the training was the increase of theta over alpha, to reach the “crossover”. This was defined as “point at which the alpha amplitude dropped below the level of theta” (Dehghani-Arani et al., 2013, p. 136). NF was set to automatically adjusting the reward threshold for alpha and theta amplitudes in real time in order to: i) maximize rewards to encourage training ii) slowly increase the level of difficulty (Burns, 2015). The Biograph Infiniti software computes the reward threshold by the formula:

2.6. Statistical analysis Two-way chi-squared and univariate ANalysis Of VAriance (ANOVAs) were used to analyze differences between groups at T0, respectively for dichotomous and dimensional measures. According to the recommendations for pre-post designs (Senn, 2006), questionnaire data on intervention effects were analyzed using multivariate analyses of covariance (MANCOVAs) with group (NFG vs WLG) as a betweensubject factor, value at T1 as dependent variables, and value at T0 as a covariate. Finally, repeated measures ANOVAs were performed to investigate differences in FC between T0, T1, and T2. The Greenhouse-Geisser corrected p-values are reported. Post-hoc, Bonferroni corrected pairwise comparisons within and between groups were performed in order to investigate effects. In order to evaluate the magnitude of the treatment effect, we measured the effect size by the Cohens' d using Ray and Shadish's (1996) formula (i.e., equation number 2). Cohen (1988) characterized d = 0.20 as a small, 0.50 as a medium, and 0.80 as a large effect size. According with Raymond et al. (2005), in order to assess NF learning in NFG, the theta/alpha ratio progression was calculated. Specifically, we calculated the mean theta/alpha ratio in each 1-minute epoch, in all sessions, for all participants. Then, we measured the Spearman's rank correlation index between theta/alpha ratio and time (i.e., number of epochs since the start of the session). All data were analyzed using SPSS software version 20. Power spectra analysis was performed with eLORETA software. The following comparisons for the power spectra analysis were performed: i) T0-NFG vs T0-WLG, ii) T1-NFG vs T1-WLG; iii) T1-NFG vs T0 NFG; iv) T1-WLG vs T0-WLG. Comparisons were performed using the statistical non-parametric mapping methodology supplied by the eLORETA software (Nichols and Holmes, 2002). This methodology is based on the Fisher's permutation test (i.e., a subset of non-parametric statistics, (more details can be found in Nichols and Holmes, 2002). The non-parametric randomization procedure, available in the eLORETA program package, was performed for the correction of multiple comparison (Nichols and Holmes, 2002). T-level thresholds were computed by the statistical software implemented in the eLORETA, which correspond to statistically significant p-values (p b 0.05 and p b 0.01) (Friston et al., 1991). 3. Results

Theta−Alpha Theta þ Alpha

Each A/T session began with the subject sitting in a comfortable chair with his/her eyes closed. After a careful skin preparation, according with Egner et al. (2002) the active electrode was placed on Pz referred to joint mastoids. The impedances were kept below 5 kΩ. EEG neurofeedback was in audio format only. Then, a 2 min guided imagery script was presented to the subjects, in order to elicit the relaxation. After that, the A/T training has begun. The frequency bands connected to feedback were: Theta (4.5–7.5 Hz), associated with the sound of ocean waves, Alpha (8–12.5 Hz), associated with the sound of a stream's flow, and Delta (0.5–4 Hz), linked with a bell sound and aimed at preventing slippage in the first sleep phase (Dehghani-Arani et al., 2013; Scott et al., 2005). Beta frequency band was also recorded, but it was not connected to feedback. According with Dehghani-Arani et al. (2013) individuals reporting previous meditative practices were asked not to use them during the training, because the association between meditation and alpha-theta crossover was previously reported (Scott et al., 2005). After each session, participants were asked to perform a set of exercises [i.e., autogenic training “taking back” procedure (Linden, 2007)] in order to facilitate the return to a state of alertness. Furthermore, they were given the opportunity to discuss their experience.

Socio-demographic and clinical data for both NFG and WLG are reported in Table 1. The two groups did not significantly differ in age, educational level, BMI and in other socio-demographic and clinical variables at T0. Visual evaluation of the EEG recordings (T0 and T1) showed no relevant modifications of the background rhythm frequency, focal abnormalities or epileptic discharges. 3.1. Neurofeedback learning According with Raymond et al. (2005), the time progression of the mean theta/alpha ratios was calculated for all NFG participants. A Spearman's rank correlation showed that theta/alpha ratios increased significantly with time (Spearman's rho = 0.844, p b 0.001). 3.2. Food craving, eating disorder symptomatology and general psychopathology Detailed descriptive and F-statistics are displayed in Table 2. In posttreatment phase, significant group main effects were observed for Intent and Hunger dimensions of FCQT. Compared to the WLG, the NFG showed a significant reduction of intentions and plans to consume food (NFG: 6.28 ± 2.30 vs WLG: 6.40 ± 2.71; F1; 49 = 4.90; p = 0.033; d = 0.626) and of craving as a physiological state (NFG:

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

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Table 1 Demographic and clinical data of participants.

Variables Age - M ± SD Educational level – M ± SD Alcohol use in the last 6 months - N (%) Tobacco use in the last 6 months - N (%) Caffeine use in the last 6 months - N (%) BMI - M ± SD State Hunger at T0 - M ± SD State Hunger at T1 - M ± SD

NFG (N = 25)

WLG (N = 25)

Test statistics

p

23.08 ± 2.60 16.68 ± 1.25 18 (72.0%) 17 (68.0%) 23 (92.0%) 22.02 ± 3.27 5.68 ± 2.76 4.92 ± 2.66

22.48 ± 2.45 16.84 ± 1.09 17 (68.0%) 11 (44%) 21 (84%) 22.34 ± 3.10 4.80 ± 2.63 5.32 ± 2.78

F1;49 = 0.71 F1;49 = 0.23 χ21 = 0.09 χ21 = 2.92 χ21 = 0.76 F1;49 = 0.05 F1;49 = 1.33 F1;49 = 0.27

0.405 0.618 0.758 0.087 0.384 0.830 0.255 0.606

Note. aYear. NFG = Neurofeedback group; WLG = waiting list group; BMI = body mass index; T0 = Pre-treatment phase; T1 = Post-treatment phase.

10.08 ± 4.22 vs WLG: 11.56 ± 3.25; F1; 49 = 8.09; p = 0.007; d = 0.803). No significant group main effects were observed for the other FCQT dimensions, although a trend was observed for decreased Ant + dimensions in NFG (NFG: 11.40 ± 5.40 vs WLG: 11.48 ± 3.78; F1; 49 = 2.97; p = 0.093; d = 0.486). No significant group main effects were observed for the EDE-Q global score as well as for the GSI total score. 3.3. EEG power spectra In the between group comparison at T0, the thresholds for significance were T = ± 3.314 corresponding to p b 0.05, and T = ± 3.833, corresponding to p b 0.01. In this comparison, no significant differences were observed between groups. In the between group comparison at T1, the thresholds for significance were T = ± 3.196 corresponding to p b 0.05, and T = ± 3.646, corresponding to p b 0.01. In this comparison, no significant differences were observed between groups. In the within group comparison (T1 vs T0), the thresholds for significance were T = ±3.480 corresponding to p b 0.05, and T = ±4.110, corresponding to p b 0.01. In this comparison, significant modifications

were observed in the alpha frequency band in the NFG, but not in the WLG (Fig. 2). Compared to T0, NFG showed a widespread increase of alpha activity at T1, localized in the left temporo-parieto-occipital areas. The eLORETA software localized these modifications in the following left hemisphere areas: i) insula (Brodmann Area, BA 13; T = 3.70; p = 0.015); ii) middle occipital gyrus (BA = 18; T = 3.51; p = 0.023); iii) fusiform gyrus (BA 19; T = 3.57; p = 0.019); iv) inferior temporal gyrus (BA 20; T = 3.51; p = 0.023); v) superior temporal gyrus (BA 22; T = 3.53; p = 0.022); vi) retrosplenial cortex (BA 29; T = 3.94; p = 0.011); vii) parahippocampal gyrus (BA 37; T = 3.66; p = 0.016). No significant differences were observed in the other frequency bands. 3.4. Food craving follow-up Stability of the A/T training effect was assessed at 4 months followup (T2). In NFG, significant changes in FCQ-T total score (mean differences T0–T2 = 11.28; p = 0.042) as well as in Ant + (mean differences T0–T2 = 2.32; p = 0.026), Intent (mean differences T0–T2 = 1.32; p = 0.022) Cues (mean differences T0–T2 = 2.02; p = 0.021), and Control (mean differences T0–T2 = 2.24; p = 0.044) dimensions were

Table 2 Pre–post means and group differences in treatment outcomes. Variable

Time

NFG (N = 25) M ± SD

WLG (N = 25) M ± SD

Test statistics

FCQT

T0 T1 T0 T1 T0 T1 T0

97.96 ± 44.35 85.76 ± 36.92 13.40 ± 5.68 11.40 ± 5.40 7.56 ± 3.79 6.40 ± 4.06 8.04 ± 3.71

88.72 ± 30.63 84.20 ± 27.09 12.16 ± 4.76 11.48 ± 3.78 6.56 ± 2.90 6.41 ± 2.52 6.28 ± 3.16

T1 T0 T1 T0 T1 T0 T1 T0 T1 T0 T1 T0 T1 T0 T1 T0 T1

6.28 ± 2.30 12.80 ± 5.12 10.88 ± 4.89 12.88 ± 8.20 11.36 ± 6.14 12.04 ± 4.74 10.08 ± 4.22 13.68 ± 8.01 11.76 ± 6.47 10.60 ± 5.72 11.40 ± 5.32 6.96 ± 4.51 6.20 ± 3.66 8.56 ± 7.01 7.42 ± 6.76 0.64 ± 0.48 0.50 ± 0.36

6.40 ± 2.71 12.32 ± 4.34 11.20 ± 3.76 11.24 ± 4.61 10.48 ± 4.47 11.84 ± 4.00 11.56 ± 3.25 12.04 ± 4.77 11.48 ± 4.66 9.68 ± 5.07 9.04 ± 4.30 6.60 ± 3.56 6.16 ± 3.13 7.09 ± 7.53 7.34 ± 6.76 0.55 ± 0.33 0.57 ± 0.42

FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) p = 0.077 FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49) FT0(1; 49) FT1(1; 49)

Ant+ AntIntent

Cues Thoughts Hunger Control Emotions Guilty EDE-Q global score GSI

Cohen's d

= = = = = = =

0.74; p = 0.296 2.60; p = 0.115 0.70; p = 0.407 2.97; p = 0.093 1.10; p = 0.300 2.72; p = 0.107 3.261;

= = = = = = = = = = = = = = = = =

4.90; p = 0.033 0.13; p = 0.722 1.15; p = 0.291 0.76; p = 0.388 0.59; p = 0.447 0.03; p = 0.873 8.09; p = 0.007 0.77; p = 0.385 1.03; p = 0.318 0.36; p = 0.550 1.15; p = 0.291 0.10; p = 0.755 0.93; p = 0.342 0.51; p = 0.478 0.90, p = 0.349 0.52; p = 0.473 0.93; p = 0.341

0.455 0.486 0.467

0.626 0.303 0.218 0.803 0.286 0.303 0.272 0.269 0.272

In bold significant tests. Abbreviations: T0 = Pre-treatment phase; T1 = Post-treatment phase; NFG = neurofeedback group; WLG = waiting list group; FCQT = Food Cravings Questionnaire-Trait; Ant+ = Positive reinforcement; Ant− = negative reinforcement; EDE-Q = Eating Disorder Examination Questionnaire; GSI = global severity index. Note: Mean and Standard deviation for T1 are not adjusted for the covariates.

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

6 C. Imperatori et al. / International Journal of Psychophysiology xxx (2016) xxx–xxx

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

Fig. 2. Results of the eLORETA within comparison (T1 VS T0) of EEG power spectra in all frequency bands in NFG and WLG. Panel A: T1 vs T0 for NFG; Panel B: T1 vs T0 for WLG. Coloured spots indicate areas where modifications of EEG spectral power was observed. Levels of significance are represented in the centre of the figure. Red-to-yellow spectrum colours indicate increase in power spectra; blue spectrum colours indicate decrease in power spectra. Threshold values (T) for statistical significance (corresponding to p b 0.05 and p b 0.01) are reported in the figure centre. In this comparison, significant modifications (*) were observed in the NFG, but not in the WLG, in the alpha frequency band. Compared to T0, NFG showed at T1 a widespread increase of alpha activity in left temporo-parieto-occipital areas. Abbreviations: NFG = Neurofeedback group; WLG = waiting list group; T0 = Pre-treatment phase; T1 = Posttreatment phase; BA = Brodmann area. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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observed. No significant differences between T0 and T2 were observed for Hunger and Thought dimensions. No significant effect was observed in the WLG. 4. Discussion The main aim of the present study was to investigate the usefulness of the A/T training in reducing FC in a non-clinical sample. As reported for drug craving (Arani et al., 2010; Burkett et al., 2004; Dehghani-Arani et al., 2013; Fahrion et al., 1992; Peniston and Kulkosky, 1989; Rostami and Dehghani-Arani, 2015), our results showed that ten sessions of A/T are associated with a decrease in two dimensions of FC: i) intentions and plans to consume food (medium effect), and ii) craving as a physiological state (large effect). Effects of A/ T training remained stable after 4 months follow-up for FCQT total score as well as for Ant+, Intent, Cues, and Control dimensions. Notably, we observed modifications in FCQ-T, which is a trait-measure associated with high retest-reliability and it is not affected by state-dependent variables (e.g., food deprivation) (Meule et al., 2014b). No significant effect of A/T training was observed on psychological dimensions typically associated with eating disorder psychopathology (i.e. EDE global score). This result suggests a possible selective role of this NF training on FC. Furthermore, contrary to previous studies (Arani et al., 2010; Dehghani-Arani et al., 2013; Fahrion et al., 1992; Peniston and Kulkosky, 1991; Rostami and Dehghani-Arani, 2015), no significant effect of A/T training was observed on psychopathological scores. This is probably due to the non-clinical sample involved in the present study. Finally, our data revealed that A/T training was associated with significant modifications of EEG power spectra. In the post treatment assessment, NFG, but not WLG, showed a significant increase of alpha power in several left temporo-parieto-occipital areas including: i) insula, ii) middle occipital gyrus; iii) fusiform gyrus; iv) inferior temporal gyrus; v) superior temporal gyrus; vi) retrosplenial cortex; vii) parahippocampal gyrus. From a neurophysiological point of view, our study showed A/T training was also associated with significant modifications in EEG power spectra in several brain areas involved in FC and in food cue reactivity. For example, increased activity in insula in response to FC has been previously reported in several neuroimaging and neurophysiological studies (Gearhardt et al., 2011; Innamorati et al., 2015; Pelchat et al., 2004). Furthermore, recent meta-analyses studies documented the association between increased activity in temporo-parieto-occipital areas (including middle occipital gyrus, fusiform gyrus, posterior cingulate cortex, parahippocampal gyrus, inferior and superior temporal gyrus) and food cue processing/reactivity (Huerta et al., 2014; Tang et al., 2012; van der Laan et al., 2011). Although the aim of this NF training was the increase of theta over alpha, we observed an increase of EEG alpha power in post-treatment. Alpha rhythm is the dominant EEG rhythm in awake RS EEG recordings and relaxed state, whereas theta is enhanced in various neocortical sites during sleep as well as during several cognitive tasks (i.e., working memory tasks) (for a review see Wang, 2010). Therefore, the increase of alpha power in post-session recording (i.e., 5 min RS) observed in NFG, may reflect the expression of the relaxation obtained with the NF. This is in line with several studies investigating EEG power modifications associated with A/T training (Arani et al., 2010; Fahrion et al., 1992). It has been proposed (Fahrion et al., 1992) that, as a result of A/ T training, the increase of alpha power may reflect neural mechanisms underlying successful coping with stressful, craving-related situations. Consistently, it has been observed that the deep relaxation associated with NF enhances well-being, the ability to better tolerate stress as well as emotional and self-awareness (Boynton, 2001; Gruzelier, 2014) during anxiety situation associated with addiction (i.e., withdrawal symptoms, craving) (Dehghani-Arani et al., 2013; Peniston and Kulkosky, 1989; Scott et al., 2005). Therefore, in accordance with our

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self-report results, after NF, participants may be more aware of their interoceptive abilities (e.g., emotional and physical state) and consequently, they may cope more effectively with some FC's features, such as craving related to physiological state. Similarly, they may be more able to avoid making plans to eat palatable foods, whenever they crave. However, in the absence of an a priori hypothesis about how NF may effect specific FC dimensions, this interpretation must be treated with caution. The increase of alpha power in temporo-parieto-occipital areas may also reflect the “normalization” effect on the reward system associated with NF (Blum et al., 2000). It has been proposed (Blum et al., 2000; Blum et al., 2012; Blum et al., 2011) that a dysfunction in the brain reward cascade could lead to abnormal cravings and multiple drug-seeking behaviors in order to compensate the inability to experience pleasant feelings during normal stimulation. According with Blum et al. (2000) some authors suggested that A/T training may promote a “normalizing” shift in the brain reward system (Dehghani-Arani et al., 2013; Rostami and Dehghani-Arani, 2015; Scott et al., 2005). It is important to note that our interpretations remain speculative due to the nonpathological sample investigated in the present study. Although this idea is purely hypothetical, this might be useful in guiding future research (e.g., investigating the usefulness of A/T training to alter reward system responses in patients with food addiction). Although our data are promising, there are some limitations in generalizing our results that must be considered. First, we focused on a nonclinical sample. Although FC is frequently observed in the general population (Gendall et al., 1997a; Lafay et al., 2001; Pelchat, 1997) the applicability and effectiveness in patients with EDs should be investigated by future studies. Secondly, we did not compare A/T training with a sham procedure to rule out the placebo effect. Nevertheless the increase of theta/alpha ratios within sessions may be considered a specific effect of A/T training (Raymond et al., 2005). Third, we have assessed the long term effect of A/T training only on FC, without analyzing long term effect on EEG data. Fourth, although ten sessions would be suitable for non-clinical samples (Schmidt and Martin, 2015), as observed in patients with Substance-related and Addictive Disorders (Arani et al., 2010; Fahrion et al., 1992; Peniston and Kulkosky, 1989; Peniston and Kulkosky, 1991; Saxby and Peniston, 1995), more sessions (i.e., from 20 to 30) may maximize A/T results. Finally, we investigated the modifications following only A/T training. Therefore, as observed in drug addiction (Arani et al., 2010; Rostami and Dehghani-Arani, 2015), it is possible that the combination of two NF training methods (i.e., A/T training and EEG-NF targeting the beta band after cue exposure) may be more effective in reducing FC and improving eating psychopathology (i.e., binge eating symptoms). This idea may be useful in guiding future research. In conclusion, to the best of our knowledge, this is the first study which investigated the effectiveness of A/T training in reducing FC using an accurate and validated tool (i.e., eLORETA) to localize electric activity in the brain. Taken together, our results showed that ten sessions of A/T are associated, in a non-clinical sample, with a decrease in FC, suggesting that the deep relaxation related with NF may enhance self-awareness and the ability to better tolerate stress during anxiety situation associated with FC. Our results also showed that NF was related to significant modifications in EEG power spectra in several brain areas involved in FC and in food cue reactivity, suggesting that this brain-directed method may be useful in the treatment of dysfunctional eating behaviors characterized by FC. Disclosures This study was performed without any financial support. Conflict of interest The authors have no conflicts of interest.

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

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References Abiles, V., Abiles, J., Rodriguez-Ruiz, S., Luna, V., Martin, F., Gandara, N., FernandezSantaella, M.C., 2013. Effectiveness of cognitive behavioral therapy on weight loss after two years of bariatric surgery in morbidly obese patients. Nutr. Hosp. 28 (4): 1109–1114. http://dx.doi.org/10.3305/nh.2013.28.4.6536. Alberts, H.J., Mulkens, S., Smeets, M., Thewissen, R., 2010. Coping with food cravings. Investigating the potential of a mindfulness-based intervention. Appetite 55 (1): 160–163. http://dx.doi.org/10.1016/j.appet.2010.05.044. Arani, F.D., Rostami, R., Nostratabadi, M., 2010. Effectiveness of neurofeedback training as a treatment for opioid-dependent patients. Clin. EEG Neurosci. 41 (3):170–177. http://dx.doi.org/10.1177/155005941004100313. Bartholdy, S., Musiat, P., Campbell, I.C., Schmidt, U., 2013. The potential of neurofeedback in the treatment of eating disorders: a review of the literature. Eur. Eat. Disord. Rev. 21 (6):456–463. http://dx.doi.org/10.1002/erv.2250. Berg, K.C., Peterson, C.B., Frazier, P., Crow, S.J., 2011. Convergence of scores on the interview and questionnaire versions of the Eating Disorder Examination: a meta-analytic review. Psychol. Assess. 23 (3):714–724. http://dx.doi.org/10.1037/a0023246. Berg, K.C., Peterson, C.B., Frazier, P., Crow, S.J., 2012. Psychometric evaluation of the eating disorder examination and eating disorder examination-questionnaire: a systematic review of the literature. Int. J. Eat. Disord. 45 (3):428–438. http://dx.doi.org/10.1002/eat.20931. Billes, S.K., Greenway, F.L., 2011. Combination therapy with naltrexone and bupropion for obesity. Expert. Opin. Pharmacother. 12 (11):1813–1826. http://dx.doi.org/10.1517/ 14656566.2011.591382. Billes, S.K., Sinnayah, P., Cowley, M.A., 2014. Naltrexone/bupropion for obesity: an investigational combination pharmacotherapy for weight loss. Pharmacol. Res. 84:1–11. http://dx.doi.org/10.1016/j.phrs.2014.04.004. Blum, K., Braverman, E.R., Holder, J.M., Lubar, J.F., Monastra, V.J., Miller, D., ... Comings, D.E., 2000. Reward deficiency syndrome: a biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors. J. Psychoactive Drugs 32 (Suppl, i–iv), 1–112. Blum, K., Liu, Y., Shriner, R., Gold, M.S., 2011. Reward circuitry dopaminergic activation regulates food and drug craving behavior. Curr. Pharm. Des. 17 (12), 1158–1167. Blum, K., Chen, A.L., Giordano, J., Borsten, J., Chen, T.J., Hauser, M., ... Barh, D., 2012. The addictive brain: all roads lead to dopamine. J. Psychoactive Drugs 44 (2):134–143. http://dx.doi.org/10.1080/02791072.2012.685407. Boswell, R.G., Kober, H., 2016. Food cue reactivity and craving predict eating and weight gain: a meta-analytic review. Obes. Rev. 17 (2):159–177. http://dx.doi.org/10.1111/ obr.12354. Boynton, T., 2001. Applied research using alpha/theta training for enhancing creativity and well-being. J. Neurother. 5 (1/2), 5–18. Brunoni, A.R., Amadera, J., Berbel, B., Volz, M.S., Rizzerio, B.G., Fregni, F., 2011. A systematic review on reporting and assessment of adverse effects associated with transcranial direct current stimulation. Int. J. Neuropsychopharmacol. 14 (8):1133–1145. http:// dx.doi.org/10.1017/S1461145710001690. Burkett, V.S., Cummins, J.M., Dickson, R.M., Skolnick, M.H., 2004. Treatment effects related to EEG-biofeedback for crack cocaine dependency in a faith-based homeless mission. J. Neurother. 8 (2), 138–140. Burns, S.T., 2015. Neurofeedback in hereditary angioedema: a single case study of symptom reduction. Appl. Psychophysiol. Biofeedback 40 (3):251–256. http://dx.doi.org/ 10.1007/s10484-015-9288-7. Calugi, S., Milanese, C., Sartirana, M., El Ghoch, M., Sartori, F., Geccherle, E., ... Dalle Grave, R., 2016. The Eating Disorder Examination Questionnaire: reliability and validity of the Italian version. Eat. Weight Disord. http://dx.doi.org/10.1007/s40519-016-0276-6 . Cepeda-Benito, A., Gleaves, D.H., Fernandez, M.C., Vila, J., Williams, T.L., Reynoso, J., 2000a. The development and validation of Spanish versions of the State and Trait Food Cravings Questionnaires. Behav. Res. Ther. 38 (11), 1125–1138 doi: S00057967(99)00141-2. Cepeda-Benito, A., Gleaves, D.H., Williams, T.L., Erath, S.A., 2000b. The development and validation of the State and Trait Food-Cravings Questionnaires. Behav. Ther. 31, 151–173. Cepeda-Benito, A., Fernandez, M.C., Moreno, S., 2003. Relationship of gender and eating disorder symptoms to reported cravings for food: construct validation of state and trait craving questionnaires in Spanish. Appetite 40 (1), 47–54 doi: S0195666302001459. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. second ed. Hillsdale, NJ: Erlbaum . Dehghani-Arani, F., Rostami, R., Nadali, H., 2013. Neurofeedback training for opiate addiction: improvement of mental health and craving. Appl. Psychophysiol. Biofeedback 38 (2):133–141. http://dx.doi.org/10.1007/s10484-013-9218-5. Delahanty, L.M., Meigs, J.B., Hayden, D., Williamson, D.A., Nathan, D.M., Diabetes Prevenion Program Research, Group, 2002. Psychological and behavioral correlates of baseline BMI in the diabetes prevention program (DPP). Diabetes Care 25 (11), 1992–1998. Derogatis, L., 1977. The SCL-90-R Manual. Clinical Psychometric Research Unit. Johns Hopkins University School of Medicine, Baltimore, MD . Dimpfel, W., Schober, F., Spuler, M., 1993. The influence of caffeine on human EEG under resting conditions and during mental loads. Clin. Investig. 71 (3), 197–207. Egner, T., Strawson, E., Gruzelier, J.H., 2002. EEG signature and phenomenology of alpha/ theta neurofeedback training versus mock feedback. Appl. Psychophysiol. Biofeedback 27 (4), 261–270. Everitt, B., 1997. Craving cocaine cues: cognitive neuroscience meets drug addiction research. Trends Cogn. Sci. 1 (1):1–2. http://dx.doi.org/10.1016/S1364-6613(97)01009-7. Fahrion, S.L., Walters, E.D., Coyne, L., Allen, T., 1992. Alterations in EEG amplitude, personality factors, and brain electrical mapping after alpha-theta brainwave training: a controlled case study of an alcoholic in recovery. Alcohol. Clin. Exp. Res. 16 (3), 547–552. Fairburn, C.G., Beglin, S.J., 1994. Assessment of eating disorders: interview or self-report questionnaire? Int. J. Eat. Disord. 16 (4), 363–370.

Franken, I.H., Muris, P., 2005. Individual differences in reward sensitivity are related to food craving and relative body weight in healthy women. Appetite 45 (2):198–201. http://dx.doi.org/10.1016/j.appet.2005.04.004. Frascella, J., Potenza, M.N., Brown, L.L., Childress, A.R., 2010. Shared brain vulnerabilities open the way for nonsubstance addictions: carving addiction at a new joint? Ann. N. Y. Acad. Sci. 1187:294–315. http://dx.doi.org/10.1111/j.1749-6632.2009.05420. Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S., 1991. Comparing functional (PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab. 11 (4): 690–699. http://dx.doi.org/10.1038/jcbfm.1991.122. Gearhardt, A.N., Yokum, S., Orr, P.T., Stice, E., Corbin, W.R., Brownell, K.D., 2011. Neural correlates of food addiction. Arch. Gen. Psychiatry 68 (8):808–816. http://dx.doi. org/10.1001/archgenpsychiatry.2011.32. Gendall, K.A., Joyce, P.R., Sullivan, P.F., 1997a. Impact of definition on prevalence of food cravings in a random sample of young women. Appetite 28 (1):63–72. http://dx. doi.org/10.1006/appe.1996.0060. Gendall, K.A., Sullivan, P.F., Joyce, P.R., Bulik, C.M., 1997b. Food cravings in women with a history of anorexia nervosa. Int. J. Eat. Disord. 22 (4), 403–409 doi: 10.1002. Grave de Peralta-Menendez, R., Gonzalez-Andino, S.L., 1998. A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem. IEEE Trans. Biomed. Eng. 45 (4):440–448. http://dx.doi.org/10.1109/10.664200. Grave de Peralta-Menendez, R., Gonzalez-Andino, S.L., Morand, S., Michel, C.M., Landis, T., 2000. Imaging the electrical activity of the brain: ELECTRA. Hum. Brain Mapp. 9 (1), 1–12. Gruzelier, J.H., 2014. EEG-neurofeedback for optimising performance. II: creativity, the performing arts and ecological validity. Neurosci. Biobehav. Rev. 44:142–158. http://dx.doi.org/10.1016/j.neubiorev.2013.11.004. Hill, D.M., Craighead, L.W., Safer, D.L., 2011. Appetite-focused dialectical behavior therapy for the treatment of binge eating with purging: a preliminary trial. Int. J. Eat. Disord. 44 (3):249–261. http://dx.doi.org/10.1002/eat.20812. Huerta, C.I., Sarkar, P.R., Duong, T.Q., Laird, A.R., Fox, P.T., 2014. Neural bases of food perception: coordinate-based meta-analyses of neuroimaging studies in multiple modalities. Obesity (Silver Spring) 22 (6):1439–1446. http://dx.doi.org/10.1002/oby.20659. Ihssen, N., Sokunbi, M.O., Lawrence, A.D., Lawrence, N.S., Linden, D.E., 2016. Neurofeedback of visual food cue reactivity: a potential avenue to alter incentive sensitization and craving. Brain Imaging Behav. http://dx.doi.org/10.1007/s11682-0169558-x . Imperatori, C., Farina, B., Brunetti, R., Gnoni, V., Testani, E., Quintiliani, M.I., ... Della Marca, G., 2013. Modifications of EEG power spectra in mesial temporal lobe during n-back tasks of increasing difficulty. A sLORETA study. Front. Hum. Neurosci. 7:109. http:// dx.doi.org/10.3389/fnhum.2013.00109. Imperatori, C., Farina, B., Quintiliani, M.I., Onofri, A., Castelli Gattinara, P., Lepore, M., ... Della Marca, G., 2014. Aberrant EEG functional connectivity and EEG power spectra in resting state post-traumatic stress disorder: a sLORETA study. Biol. Psychol. 102C:10–17. http://dx.doi.org/10.1016/j.biopsycho.2014.07.011. Imperatori, C., Fabbricatore, M., Innamorati, M., Farina, B., Quintiliani, M.I., Lamis, D.A., ... Della Marca, G., 2015. Modification of EEG functional connectivity and EEG power spectra in overweight and obese patients with food addiction: an eLORETA study. Brain Imaging Behav. 9 (4):703–716. http://dx.doi.org/10.1007/s11682-014-9324-x. Innamorati, M., Imperatori, C., Balsamo, M., Tamburello, S., Belvederi Murri, M., Contardi, A., ... Fabbricatore, M., 2014. Food Cravings Questionnaire-Trait (FCQ-T) Discriminates Between Obese and Overweight Patients with and Without Binge Eating Tendencies: The Italian Version of the FCQ-T. 96(6):pp. 632–639. http://dx.doi.org/10.1080/ 00223891.2014.909449. Innamorati, M., Imperatori, C., Meule, A., Lamis, D.A., Contardi, A., Balsamo, M., ... Fabbricatore, M., 2015. Psychometric properties of the Italian food cravings questionnaire-trait-reduced (FCQ-T-r). Eat. Weight Disord. 20 (1):129–135. http://dx.doi.org/ 10.1007/s40519-014-0143-2. Jansen, J.M., Daams, J.G., Koeter, M.W., Veltman, D.J., van den Brink, W., Goudriaan, A.E., 2013. Effects of non-invasive neurostimulation on craving: a meta-analysis. Neurosci. Biobehav. Rev. 37 (10 Pt 2):2472–2480. http://dx.doi.org/10.1016/j.neubiorev.2013. 07.009. Kahkonen, S., Wilenius, J., Nikulin, V.V., Ollikainen, M., Ilmoniemi, R.J., 2003. Alcohol reduces prefrontal cortical excitability in humans: a combined TMS and EEG study. Neuropsychopharmacology 28 (4):747–754. http://dx.doi.org/10.1038/sj.npp. 1300099. Kreiter, A.K., Singer, W., 1992. Oscillatory neuronal responses in the visual cortex of the awake macaque monkey. Eur. J. Neurosci. 4 (4), 369–375. Lackner, N., Unterrainer, H.F., Skliris, D., Shaheen, S., Dunitz-Scheer, M., Wood, G., ... Neuper, C., 2016. EEG neurofeedback effects in the treatment of adolescent anorexia nervosa. Eat. Disord. 24 (4):354–374. http://dx.doi.org/10.1080/10640266.2016.1160705. Lafay, L., Thomas, F., Mennen, L., Charles, M.A., Eschwege, E., Borys, J.M., Basdevant, A., 2001. Gender differences in the relation between food cravings and mood in an adult community: results from the fleurbaix laventie ville sante study. Int. J. Eat. Disord. 29 (2):195–204. http://dx.doi.org/10.1002/1098-108. Lansbergen, M.M., van Dongen-Boomsma, M., Buitelaar, J.K., Slaats-Willemse, D., 2011. ADHD and EEG-neurofeedback: a double-blind randomized placebo-controlled feasibility study. J. Neural Transm. 118 (2):275–284. http://dx.doi.org/10.1007/s00702010-0524-2. Linden, W., 2007. The autogenic training method of J. H. Schultz. In: Lehrer, P.M., Woolfolk, R.L., Sime, W.E. (Eds.), Principles and Practice of Stress Management. Guilford Press, New York, pp. 151–174. Martin, C.K., O'Neil, P.M., Tollefson, G., Greenway, F.L., White, M.A., 2008. The association between food cravings and consumption of specific foods in a laboratory taste test. Appetite 51 (2):324–326. http://dx.doi.org/10.1016/j.appet.2008.03.002. Meule, A., Westenhofer, J., Kubler, A., 2011. Food cravings mediate the relationship between rigid, but not flexible control of eating behavior and dieting success. Appetite 57 (3):582–584. http://dx.doi.org/10.1016/j.appet.2011.07.013.

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010

C. Imperatori et al. / International Journal of Psychophysiology xxx (2016) xxx–xxx Meule, A., Freund, R., Skirde, A.K., Vogele, C., Kubler, A., 2012a. Heart rate variability biofeedback reduces food cravings in high food cravers. Appl. Psychophysiol. Biofeedback 37 (4):241–251. http://dx.doi.org/10.1007/s10484-012-9197-y. Meule, A., Lutz, A., Vogele, C., Kubler, A., 2012b. Food cravings discriminate differentially between successful and unsuccessful dieters and non-dieters. Validation of the Food Cravings Questionnaires in German. Appetite 58 (1):88–97. http://dx.doi.org/ 10.1016/j.appet.2011.09.010. Meule, A., Kubler, A., Blechert, J., 2013. Time course of electrocortical food-cue responses during cognitive regulation of craving. Front. Psychol. 4:669. http://dx.doi.org/10. 3389/fpsyg.2013.00669. Meule, A., Hermann, T., Kubler, A., 2014a. A short version of the Food Cravings Questionnaire-Trait: the FCQ-T-reduced. Front. Psychol. 5:190. http://dx.doi.org/10.3389/ fpsyg.2014.00190. Meule, A., Teran, C.B., Berker, J., Grundel, T., Mayerhofer, M., Platte, P., 2014b. Erratum: on the differentiation between trait and state food craving: half-year retest-reliability of the Food Cravings Questionnaire-Trait-reduced (FCQ-T-r) and the Food Cravings Questionnaire-State (FCQ-S). J. Eat. Disord. 2 (1):33. http://dx.doi.org/10.1186/ s40337-014-0033-z. Moreno, S., Rodriguez, S., Fernandez, M.C., Tamez, J., Cepeda-Benito, A., 2008. Clinical validation of the trait and state versions of the Food Craving Questionnaire. Assessment 15 (3):375–387. http://dx.doi.org/10.1177/1073191107312651. Moreno, S., Warren, C.S., Rodriguez, S., Fernandez, M.C., Cepeda-Benito, A., 2009. Food cravings discriminate between anorexia and bulimia nervosa. Implications for “success” versus “failure” in dietary restriction. Appetite 52 (3):588–594. http://dx.doi. org/10.1016/j.appet.2009.01.011. Murphy, T.H., Blatter, L.A., Wier, W.G., Baraban, J.M., 1992. Spontaneous synchronous synaptic calcium transients in cultured cortical neurons. J. Neurosci. 12 (12), 4834–4845. Ng, L., Davis, C., 2013. Cravings and food consumption in Binge Eating Disorder. Eat. Behav. 14 (4):472–475. http://dx.doi.org/10.1016/j.eatbeh.2013.08.011. Nichols, T.E., Holmes, A.P., 2002. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15 (1):1–25. http://dx.doi.org/ 10.1002/hbm.1058. Oslin, D.W., Cary, M., Slaymaker, V., Colleran, C., Blow, F.C., 2009. Daily ratings measures of alcohol craving during an inpatient stay define subtypes of alcohol addiction that predict subsequent risk for resumption of drinking. Drug Alcohol Depend. 103 (3): 131–136. http://dx.doi.org/10.1016/j.drugalcdep.2009.03.009. Parvaz, M.A., Alia-Klein, N., Woicik, P.A., Volkow, N.D., Goldstein, R.Z., 2011. Neuroimaging for drug addiction and related behaviors. Rev. Neurosci. 22 (6):609–624. http://dx. doi.org/10.1515/RNS.2011.055. Pascual-Marqui, R.D., Michel, C.M., Lehmann, D., 1994. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18 (1), 49–65 doi: 10.10167-8760(84)90014. Pascual-Marqui, R.D., Lehmann, D., Koukkou, M., Kochi, K., Anderer, P., Saletu, B., ... Kinoshita, T., 2011. Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 369 (1952): 3768–3784. http://dx.doi.org/10.1098/rsta.2011.0081. Pelchat, M.L., 1997. Food cravings in young and elderly adults. Appetite 28 (2):103–113. http://dx.doi.org/10.1006/appe.1996.0063. Pelchat, M.L., Johnson, A., Chan, R., Valdez, J., Ragland, J.D., 2004. Images of desire: foodcraving activation during fMRI. NeuroImage 23 (4):1486–1493. http://dx.doi.org/ 10.1016/j.neuroimage.2004.08.023. Peniston, E.G., Kulkosky, P.J., 1989. Alpha-theta brainwave training and beta-endorphin levels in alcoholics. Alcohol. Clin. Exp. Res. 13 (2), 271–279. Peniston, E.G., Kulkosky, P.J., 1991. Alpha-theta brainwave neurofeedback therapy for Vietnam veterans with combat-related posttraumatic stress disorder. Z. Med. Psychol. 4, 47–60. Potenza, M.N., Grilo, C.M., 2014. How relevant is food craving to obesity and its treatment? Front. Psych. 5:164. http://dx.doi.org/10.3389/fpsyt.2014.00164. Ray, J.W., Shadish, W.R., 1996. How interchangeable are different estimators of effect size? J. Consult. Clin. Psychol. 64 (6):1316–1325. http://dx.doi.org/10.1037/0022-006X.64. 6.1316. Raymond, J., Varney, C., Parkinson, L.A., Gruzelier, J.H., 2005. The effects of alpha/theta neurofeedback on personality and mood. Cogn. Brain Res. 23 (2–3):287–292. http://dx.doi.org/10.1016/j.cogbrainres.2004.10.023. Rostami, R., Dehghani-Arani, F., 2015. Neurofeedback training as a new method in treatment of crystal methamphetamine dependent patients: a preliminary study. Appl.

9

Psychophysiol. Biofeedback 40 (3):151–161. http://dx.doi.org/10.1007/s10484-0159281-1. Sarno, I., Preti, E., Prunas, A., Madeddu, F., 2011. SCL-90-R: Symptom Checklist 90 R. Versione Italiana Validata e Standardizzata. Giunti O.S, Firenze . Saxby, E., Peniston, E.G., 1995. Alpha-theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms. J. Clin. Psychol. 51 (5), 685–693. Schmidt, J., Martin, A., 2015. Neurofeedback reduces overeating episodes in female restrained eaters: a randomized controlled pilot-study. Appl. Psychophysiol. Biofeedback 40 (4):283–295. http://dx.doi.org/10.1007/s10484-015-9297-6. Schmidt, J., Martin, A., 2016. Neurofeedback against binge eating: a randomized controlled trial in a female subclinical threshold sample. Eur. Eat. Disord. Rev. 24 (5): 406–416. http://dx.doi.org/10.1002/erv.2453. Scott, W.C., Kaiser, D., Othmer, S., Sideroff, S.I., 2005. Effects of an EEG biofeedback protocol on a mixed substance abusing population. Am. J. Drug Alcohol Abuse 31 (3), 455–469. Senn, S., 2006. Change from baseline and analysis of covariance revisited. Stat. Med. 25 (24):4334–4344. http://dx.doi.org/10.1002/sim.2682. Sherlin, L.H., Arns, M., Lubar, J., Heinrich, H., Kerson, C., Strehl, U., Sterman, M.B., 2011. Neurofeedback and basic learning theory: implications for research and practice. J. Neurother. 15 (4):292–304. http://dx.doi.org/10.1080/10874208.2011.623089. Sinha, R., 2013. The clinical neurobiology of drug craving. Curr. Opin. Neurobiol. 23 (4): 649–654. http://dx.doi.org/10.1016/j.conb.2013.05.001. Sitton, S.C., 1991. Role of craving for carbohydrates upon completion of a protein-sparing fast. Psychol. Rep. 69 (2), 683–686. Sokhadze, T.M., Cannon, R.L., Trudeau, D.L., 2008. EEG biofeedback as a treatment for substance use disorders: review, rating of efficacy, and recommendations for further research. Appl. Psychophysiol. Biofeedback 33 (1):1–28. http://dx.doi.org/10.1007/ s10484-007-9047-5. Tang, D.W., Fellows, L.K., Small, D.M., Dagher, A., 2012. Food and drug cues activate similar brain regions: a meta-analysis of functional MRI studies. Physiol. Behav. 106 (3): 317–324. http://dx.doi.org/10.1016/j.physbeh.2012.03.009. Thatcher, R.W., 2014. Advances in assessment and treatment of ADHD using network analyses. Biofeedback 42 (2):58–67. http://dx.doi.org/10.5298/1081-5937-42.2.07. Tiffany, S.T., Wray, J.M., 2012. The clinical significance of drug craving. Ann. N. Y. Acad. Sci. 1248:1–17. http://dx.doi.org/10.1111/j.1749-6632.2011.06298. Trudeau, D.L., 2005. EEG biofeedback for addictive disorders—the state of the art in 2004. J. Adult Dev. 12 (2/3), 139–146. van der Laan, L.N., de Ridder, D.T., Viergever, M.A., Smeets, P.A., 2011. The first taste is always with the eyes: a meta-analysis on the neural correlates of processing visual food cues. NeuroImage 55 (1):296–303. http://dx.doi.org/10.1016/j.neuroimage.2010.11. 055. van der Ster Wallin, G., Norring, C., Holmgren, S., 1994. Binge eating versus nonpurged eating in bulimics: is there a carbohydrate craving after all? Acta Psychiatr. Scand. 89 (6), 376–381. Vander Wal, J.S., Johnston, K.A., Dhurandhar, N.V., 2007. Psychometric properties of the State and Trait Food Cravings Questionnaires among overweight and obese persons. Eat. Behav. 8 (2):211–223. http://dx.doi.org/10.1016/j.eatbeh.2006.06.002. Vernon, D.J., 2005. Can neurofeedback training enhance performance? An evaluation of the evidence with implications for future research. Appl. Psychophysiol. Biofeedback 30 (4):347–364. http://dx.doi.org/10.1007/s10484-005-8421-4. Wang, X.J., 2010. Neurophysiological and computational principles of cortical rhythms in cognition. Physiol. Rev. 90 (3):1195–1268. http://dx.doi.org/10.1152/physrev.00035. 2008. Waters, A., Hill, A., Waller, G., 2001. Internal and external antecedents of binge eating episodes in a group of women with bulimia nervosa. Int. J. Eat. Disord. 29 (1):17–22. http://dx.doi.org/10.1002/1098-108X(200101)29. Weingarten, H.P., Elston, D., 1990. The phenomenology of food cravings. Appetite 15 (3), 231–246. Weingarten, H.P., Elston, D., 1991. Food cravings in a college population. Appetite 17 (3), 167–175. White, M.A., Whisenhunt, B.L., Williamson, D.A., Greenway, F.L., Netemeyer, R.G., 2002. Development and validation of the food-craving inventory. Obes. Res. 10 (2): 107–114. http://dx.doi.org/10.1038/oby.2002.17.

Please cite this article as: Imperatori, C., et al., Coping food craving with neurofeedback. Evaluation of the usefulness of alpha/theta training in a non-clinical sample, Int. J. Psychophysiol. (2016), http://dx.doi.org/10.1016/j.ijpsycho.2016.11.010