Journal of Clinical Medicine Article What Difference Does it Make? Risk-Taking Behavior in Obesity after a Loss is Associated with Decreased Ventromedial Prefrontal Cortex Activity Trevor Steward 1,2,† , Asier Juaneda-Seguí 3,4,5,† , Gemma Mestre-Bach 2,3 , Ignacio Martínez-Zalacaín 3,5 , Nuria Vilarrasa 6,7 , Susana Jiménez-Murcia 2,3,5 , Jose A Fernández-Formoso 2 , Misericordia Veciana de las Heras 8 , Nuria Custal 3 , Nuria Virgili 6,7 , Rafael Lopez-Urdiales 6 , Amador García-Ruiz-de-Gordejuela 9 , José M Menchón 1,3,4 , Carles Soriano-Mas 3,4,10, * and Fernando Fernandez-Aranda 2,3,5, * 1 School of Psychological Sciences, University of Melbourne, Parkville, Victoria 3010, Australia; Trevor.steward@unimelb.edu.au (T.S.); jmenchon@bellvitgehospital.cat (J.M.M.) 2 Ciber Fisiopatología Obesidad y Nutrición (CIBERObn), Instituto Salud Carlos III, Feixa Llarga s/n, 08907 Barcelona, Spain; Gemma.mestre.bach@gmail.com (G.M.-B.); sjimenez@bellvitgehospital.cat (S.J.-M.); tono.fernandez@ciberisciii.es (J.A.F.-F.) 3 Department of Psychiatry, Bellvitge University Hospital-IDIBELL, C/Feixa Llarga s/n, 08907 Barcelona, Spain; ajuaneda@idibell.cat (A.J.-S.); imartinezz@outlook.es (I.M.-Z.); ncustal@bellvitgehospital.cat (N.C.) 4 Ciber Salud Mental (CIBERSAM), Instituto Salud Carlos III, Feixa Llarga s/n, 08907 Barcelona, Spain 5 Department of Clinical Sciences, School of Medicine, University of Barcelona, C/Feixa Llarga s/n, 08907 Barcelona, Spain 6 Department of Endocrinology and Nutrition, University Hospital of Bellvitge-IDIBELL, C/Feixa Llarga s/n, 08907 Barcelona, Spain; nuriag@bellvitgehospital.cat (N.V.); mvirgili@bellvitgehospital.cat (N.V.); rafaellopez@bellvitgehospital.cat (R.L.-U.) 7 CIBERDEM-CIBER de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, C/Feixa Llarga s/n, 08907 Barcelona, Spain 8 Neurology Department, Bellvitge University Hospital-IDIBELL, C/Feixa Llarga s/n, 08907 Barcelona, Spain; mveciana@bellvitgehospital.cat 9 Bariatric and Metabolic Surgery Unit, Service of General and Gastrointestinal Surgery, Bellvitge University Hospital-IDIBELL, C/Feixa Llarga s/n, 08907 Barcelona, Spain; gordeju@icloud.com 10 Departament of Psychobiology and Methodology in Health Sciences. Universitat Autònoma de Barcelona, 08193 Barcelona, Spain * Correspondence: csoriano@idibell.cat (C.S.-M.); ffernandez@bellvitgehospital.cat (F.F.-A.); Tel.: +34-93-2607227 (F.F.-A.) † Theses authors contributed equally to this work.  Received: 6 August 2019; Accepted: 24 September 2019; Published: 27 September 2019  Abstract: Altered activity in decision-making neural circuitry may underlie the maladaptive food choices found in obesity. Here, we aimed to identify the brain regions purportedly underpinning risk-taking behavior in individuals with obesity. Twenty-three adult women with obesity and twenty-three healthy weight controls completed the Risky Gains Task during functional magnetic resonance imaging (fMRI). This task allows participants to choose between a safe option for a small, guaranteed monetary reward and risky options with larger rewards. fMRI analyses comparing losing trials to winning trials found that participants with obesity presented decreased activity in the left anterior insula in comparison to controls (p < 0.05, AlphaSim corrected). Moreover, left insula activation during losses vs. wins was negatively correlated with UPPS-P questionnaire sensation seeking scores. During safe vs. risky trials following a loss, the control group exhibited increased activation in the ventromedial prefrontal cortex (vmPFC) (p < 0.05, AlphaSim corrected) in comparison to the OB group. Moreover, vmPFC response in the obesity group during post-loss trials was negatively correlated with risky choices on the task overall. As a whole, our findings support J. Clin. Med. 2019, 8, 1551; doi:10.3390/jcm8101551 www.mdpi.com/journal/jcm J. Clin. Med. 2019, 8, 1551 2 of 11 that diminished tuning of the insula towards interoceptive signals may lead to a lack of input to the vmPFC when weighing the costs and benefits of risky choices. Keywords: obesity; fMRI; risk; reward; sensation seeking; impulsivity 1. Introduction The worldwide number of adult women with obesity has increased from approximately 69 million in 1975 to 390 million in 2016 [1]. The combination of high chronic stress and risk-taking has been found to increase the likelihood of visceral fat gain in women over the course of 18 months, thereby suggesting that impulsivity may be a potentially beneficial intervention target for obesity prevention [2]. Still, several questions regarding risk-taking among individuals with obesity remain unsettled, including whether differences in risk taking are due to an underestimation of the potential drawbacks of a making a risky decision or whether high-risk behaviors are contained only to the realm of food stimuli. Risk-taking can be broadly defined as a predisposition to select options with potential for large beneficial or adverse outcomes over alternatives that provide smaller benefits or adverse outcomes [3]. One recent meta-analysis examining performance on the Iowa Gambling Task determined decision-making to be impaired in individuals with obesity, though the clinical importance of non-food-related decision-making deficits remains unclear in terms of the impact such deficits have on weight itself [4]. Other research has found that individuals with obesity have greater difficulty inhibiting dominant behaviors and may be more sensitive to reward than healthy weight controls [5,6], thereby increasing the probability of engaging in risk-taking. Studies using functional magnetic resonance imaging (fMRI) have linked higher adiposity to hypo-responsivity of somatosensory regions during monetary reward feedback, indicating that blunted activation in somatosensory regions may underpin altered reward feedback learning [7]. The Risky Gains Task (RGT) has been utilized during fMRI scanning in diverse populations to examine neural activation patterns while individuals weigh less rewarding, safe choices against more rewarding, risky choices [8]. Research has linked RGT-derived brain activity to the personality trait of sensation seeking, suggesting that, in individuals with high levels of this trait, positive responses to reward outweigh the impact of the equivalent loss [3]. The RGT has consistently been found to elicit activations in the insula [9,10], a region that is crucial for integrating homeostatic signals with external information and expected outcomes [11]. Indeed, one longitudinal study following a sample of adolescents during a weight-loss intervention found that increased activation in the anterior insula during the RGT was positively associated with reductions in the body mass index (BMI) and fat mass [12]. Relatedly, individual differences in interoceptive sensitivity are thought to modulate decision-making processes orchestrated in the ventromedial prefrontal cortex (vmPFC) by guiding one’s awareness of internal cues of hunger or satiety [13]. Obesity and eating disorders characterized by impulsive behaviors have been linked to poor interoceptive sensitivity [14,15], which may lead a failure to integrate interceptive feedback outcomes into the decision-making process [16]. The present study, which was part of a larger national project examining impulsivity in obesity and eating disorders, sought to assess the neural correlates of risk-taking between adult women with obesity and healthy controls (HC) by means of the RGT during fMRI. Additionally, we aimed to determine whether brain-derived activation levels during the RGT are linked to sensation seeking traits. We only included women in our sample given the strong evidence supporting sex differences in sensation seeking traits and risk taking [17] and the fact that previous research has found that the direction of the association between sensitivity to reward and weight is gender-specific [18]. Based on prior studies using the RGT [10,12,19], we hypothesized that women with obesity would exhibit altered activation patterns in the insula during reward vs. punishment trials. We further hypothesized J. Clin. Med. 2019, 8, 1551 3 of 11 that RGT behavioral measures would be related to neural activity in prefrontal regions related to decision making and reward sensitivity [9,20]. 2. Methods 2.1. Participants The study featured 23 adult women with obesity who were recruited from the Bariatric and Metabolic Surgery Unit and the Endocrinology and Nutrition Unit at Bellvitge University Hospital (Barcelona, Spain). Participants with obesity were compared to 23 HC women recruited from the same hospital catchment area. The obesity (36.57 ± 9.75) and HC (30.57 ± 10.96) groups did not significantly differ with regards to age (p = 0.056). These data are included in Table 1. All participants underwent the Mini-International Neuropsychiatric Interview (M.I.N.I.) with staff psychologists from the Bellvitge University Hospital Department of Psychiatry to screen for the presence of a psychiatric disorder [21]. This study was undertaken as part of a national project including women with eating disorders and obesity. Complete inclusion and exclusion criteria for this study are included in the Supplementary Information. Table 1. Group characteristics. Healthy Weight Obese n = 23 n = 23 Mean SD Mean SD p |d| Age 30.57 10.96 36.57 9.75 0.056 0.58 Education (years) 15.80 1.67 14.79 2.29 0.17 0.50 BMI 20.93 1.90 43.35 6.98 <0.001 4.39 b a Body fat mass (%) 24.50 5.26 47.11 5.12 <0.001 4.35 b a UPPS-P subscales Negative urgency 25.22 6.52 28.17 6.39 0.13 0.46 Lack of premeditation 21.78 4.96 23.30 5.14 0.31 0.30 Lack of perseverance 19.30 5.30 22.17 5.16 0.07 0.54 Sensation seeking 28.35 8.44 24.26 6.84 0.08 0.53 Positive urgency 22.52 6.63 20.78 5.13 0.33 0.29 Risky gains task Risky choices following loss (%) 59.87 21.05 46.93 29.55 0.09 0.50 Risky choices overall (%) 67.34 16.81 60.26 21.09 0.22 0.37 BMI: Body mass index (kg/m2 ). SD: standard deviation. a Significant difference (p < 0.05). b Large effect size (|d| > 0.80). The Bellvitge University Hospital Clinical Research Ethics Committee approved the study (PR146/14). Signed informed consent was obtained from all participants. 2.2. Impulsivity Measure (UPPS-P) This 59-item self-report UPPS-P questionnaire assesses five facets of impulsivity: negative urgency, positive urgency, lack of premeditation, lack of perseverance, and sensation seeking [22]. Individuals are asked to consider acts/incidents during the last six months when rating their behaviors and attitudes. The Spanish adaptation of the UPPS-P has demonstrated good reliability (Cronbach’s α between 0.79 and 0.93) and external validity [23]. J. Clin. Med. 2019, 8, 1551 4 of 11 2.3. Anthropometric Measures The Tanita BC-420MA was utilized to assess body composition and to calculate BMI. This noninvasive device (Tanita BC-420MA, Tanita Corp. Tokyo, Japan) uses bioelectrical impedance analysis to measure weight and body composition variables [24]. Height was measured via a stadiometer. 2.4. fMRI Risky Gains Task The RGT has been described in previous studies featuring diverse clinical populations [3,9,25]. During the task, participants completed trials showing the numbers 20, 40, and 80 in increasing order. These numbers represented the amount of monetary reward (cents) that could be added to their total. Prior to scanning, participants were instructed that choosing 20 was always the safest option but that they could choose to wait to receive, or risk losing, 40 cents, or to wait again to receive or risking losing 80 cents. Unbeknownst to the participants, the RGT included predefined outcome frequencies so that the final amount won remained identical irrespective of whether 20, 40, or 80 cents were selected. Visual feedback was provided after each choice, and the cumulative total of the participants winnings was displayed after the completion of each trial to allow for performance monitoring. Participants were paid the same amount for their participation in the study irrespective of task performance. The task was made up of 96 trials lasting 3.5 seconds each. Trials were presented in a predetermined randomized order, with 54 trials being “win” trials and 42 “loss” trials. Responses were measured via a button box; responses outside of the designated timeframe resulted in a loss. 2.5. In-Scanner Behavioral Analysis The frequency of trials counted as “risky” (±40 or ±80) or “safe” (+20), and as “wins” (+20, +40, or +80) vs. “losses” (−40 or −80) were compared between groups. In addition, the frequency of “safe” choices vs. the number of “risky” choices following a loss were compared between groups, as in previous research [9]. 2.6. Imaging Data Acquisition, Pre-processing, and Analysis Participants were scanned using a 3T Phillips Ingenia system equipped with a thirty-two-channel phased-array head coil. The fMRI sequence used a single-shot gradient-echo echo-planar imaging (EPI, 2D), with a repetition time of 2000 msec, an echo time of 25 msec, and a pulse angle of 90◦ , in a 24 cm field of view and an 80 × 80 pixel matrix (readout bandwidth at 2040 Hz/pixel) providing isotropic voxel sizes of 3 × 3 × 3 mm with no gap. A total of 40 interleaved sections, parallel to the anterior-posterior commissure line, were acquired for each whole-brain volume for 8 min and 18 s. A high-resolution T1-weighted anatomical scan was also acquired to facilitate registration of the EPI data into standard space. Specifically, a three-dimensional fast-spoiled gradient, inversion-recovery sequence with 233 contiguous slices (repetition time, 10.43 msec; echo time, 4.8 msec; flip angle, 8◦ ) in a 24 cm field of view, with a 320 × 320 pixel matrix, readout bandwidth at 144 Hz/pixel, and isotropic voxel sizes of 0.75 × 0.75 × 0.75 mm was used. The total scan time was 5 min and 4 s 2.6.1. fMRI Image Preprocessing All functional images were initially preprocessed using the Wavelet Despike procedure within the BrainWavelet Toolbox [26]. This process removes a range of high and low frequency artifacts from a time series by denoising the synchronized signal transients induced by abrupt physical movements. Next, image preprocessing was performed using the statistical parametric mapping software (SPM 12) toolbox running on MATLAB R2017a. For each participant, functional images were realigned to the mean position of the individual time-series scans and the corresponding high-resolution structural T1 image was oriented to the anterior and posterior commissure (AC-PC) line. Functional scans were then co-registered to the T1 image, which was used for non-lineal normalization to standard Montreal Neurological Institute (MNI) space. Normalization parameters were then applied to functional J. Clin. Med. 2019, 8, 1551 5 of 11 time-series, which were finally smoothed with an 8 mm full width at half maximum (FWHM) kernel. White matter, cerebrospinal fluid (CSF), and global blood oxygen level-dependent (BOLD) time-series, in addition to translation and rotation movement parameters, were also introduced as confounders in a further denoising step performed with the CONN toolbox [27]. 2.6.2. First-Level Analyses The contrasts of interest defined for first-level (single-subject) analysis were: losses vs. wins, safe vs. risky choices, and safe choices vs. risky choices following a loss. Conditions were modelled as the time elapsed from the beginning of the trial to the time of the participants’ response or to the appearance of punishment feedback. The BOLD response at each voxel was convolved using the SPM12 canonical hemodynamic response function and a 128 s high-pass filter. 2.6.3. Second-Level Analyses Between-group comparisons in task-induced activations were conducted with two-sample t-tests using group (HC vs. OB) as the main factor. Analyses were carried out within regions generated by extracting and conjoining areas from one-sample (OB and HC) activations for each contrast (p< 0.001, uncorrected). Derived peak activation differences following significance thresholding were extracted and entered into an SPSS data matrix to assess their relationship with clinical and RGT behavioral measures. 2.6.4. Significance Thresholding Statistical significance was determined by a combination of voxel-level and cluster-extent thresholds using the AlphaSim algorithm as implemented in the SPM RESTplus V1.2 toolbox [28]. A minimum spatial cluster extent (ke) to satisfy a family-wise error (FWE) rate correction of pFWE < 0.05 over contrasts was applied. Input parameters to AlphaSim included a voxel-level probability of p < 0.001, a rmm (cluster edge connected) of 3, a FWHM corresponding to the actual smoothing of the data after model estimation, and the mask volume from the contrast of interest. 2.7. Statistical Analyses of Non-Imaging Data Statistical analyses of non-imaging data (e.g., sociodemographic variables and UPPS-P questionnaire results) were carried out with SPSS 21 (IBM Corp; Armonk, NY). Between-group comparisons were carried out using independent sample t-tests, and linear associations were estimated using Pearson’s correlations. Shapiro–Wilk tests were performed to confirm the normal distribution of the variables of interest. Effect sizes for mean differences were measured using Cohen’s d coefficient (|d| >0.2–0.5 was considered low, |d|>0.5–0.8 moderate, and |d| >0.8 large) [29]. For Pearson’s correlations, p values were used to determine significance and effect sizes were also reported (|r| >0.10–0.24 was considered low, |r| >0.24–0.37 moderate, and |r| >0.37 large; 23). 3. Results 3.1. Clinical and Behavioral Data As expected, the OB group had a significantly higher BMI and body fat percentage than the HC group (Table 1). No significant differences were found between groups in the UPPS-P impulsivity scores. There were no significant differences between groups in the frequency of risky decisions on the RGT (p = 0.215) or in the frequency of risky decisions following a loss (p = 0.094). J. Clin. Med. 2019, 8, 1551 6 of 11 3.2. Imaging Data 3.2.1. Losses vs. Wins One-sample t-tests showed that both groups commonly activated the precuneus, the dorsomedial and dorsolateral prefrontal cortex, and the anterior insula during the losses vs. wins contrast (Supplementary Figure S1). Group comparisons using independent sample t-tests (AlphaSim corrected, pPWE< 0.05) found that participants in the OB group showed decreased activation in the left anterior insula, the angular gyrus, and the posterior visual areas in comparison to HC during the losses vs. wins J. Clin.contrast Med. 2019,(Figure 8, x FOR 1, Table PEER 2). REVIEW 6 of 11 Figure 1: (a) Figure 1. (a) Increased Increased activation activation was was found found in in the the left left anterior anterior insula insula in in the the healthy healthy control control group group in in comparison comparisontotothe theobesity obesitygroup group(AlphaSim (AlphaSim voxel level voxel probability level = p <=0.001, probability p < 0.05 p < 0.001, p < familywise error 0.05 familywise (FWE) cluster-extent error (FWE) corrected) cluster-extent during during corrected) the losses thevs. winsvs. losses contrast wins of the Risky contrast Gains of the Task. Risky Color Gains bar Task. represents t-values. (b) A scatterplot depicting the negative association between extracted Color bar represents t-values. (b) A scatterplot depicting the negative association between extracted activation eigenvalues from the leftfrom activation eigenvalues anterior the insula peak (losses left anterior insulavs.peak wins) and UPPS-P (losses sensation vs. wins) seekingsensation and UPPS-P scores in the obesity group [n = 23, r (23) =−0.419, p = 0.047]. seeking scores in the obesity group [n = 23, r (23) =−0.419, p = 0.047]. Table 2. fMRI Risky Gains Task Results for Group by Outcome Interactions. Table 2. fMRI Risky Gains Task Results for Group by Outcome Interactions. MNI CoordinatesMNI Contrast Peak Region (x,y,z) Ke a coordinates t Losses vs. wins Contrast Right angular gyrus Peak region 46, −54, (x,y,z) 30 188 Ke5.78 a t HC > OB Right occipital lobe 10, −82, 36 301 4.53 Losses vs wins Right angular gyrus 46, −54, 30 188 5.78 Left anterior insula −44, 8, −14 52 4.15 HC > OB LeftRight extrastriate visual occipital lobe −6, −80, 10,18−82, 36 101 3.88 4.53 301 cortex Left anterior insula −44, 8, −14 52 4.15 Safe choices vs risky choices following a loss Ventromedial 2, 44, −24 175 4.75 HC > OB prefrontal Left cortex visual extrastriate −6, −80, 18 101 3.88 cortex Regions showing between-group differences during the Risky Gains Task (AlphaSim voxel level probability = p < 0.001, vs Safe choices p OB cortex 3.2.2. Safe Choices vs. Risky Choices Regions showing between-group differences during the Risky Gains Task (AlphaSim voxel level One-sample probability = p