Influence of calorie information on food choices

Regardless of its increasing presence, point-of-purchase calorie labeling has had mixed success in nudging food selection and consumption toward lower calorie options [13–17]. In fact, systematic reviews and meta-analyses targeting the influence of calorie labeling have largely yielded null or insignificantly-sized effects [18–21]. Even when it is available, only about one third of consumers report using calorie information to make food decisions [15]. Moreover, consumers over-report their use of nutrition labels; although they express an intention to use nutrition labeling, they fail to incorporate this information when making actual food decisions [9,22].

Still, a few factors appear to facilitate the influence of calorie labeling on food selection. For example, calorie labels are more effective when paired with additional interpretive labeling—like exercise equivalents [16], symbolic health icons [15,18], or recommended daily allowances [14]—which provide additional context and implications, enabling consumers to make informed decisions about their diet. Additionally, consumer characteristics appear to moderate the influence of calories on food decisions. Specifically, females [15,18] and dieters [16] tend to weigh calorie information more when making food decisions than do males and non-dieters. These person-level motivations might shape attitudes toward food and moderate the efficacy of nutrition-related interventions. To better understand the influence of calories on food valuation and choices, the present study examines their influence on brain responses during the evaluation of food cues.

Neuroimaging studies of food reward

Food cues typically elicit robust activation in putative reward regions—including the nucleus accumbens (NAcc) and orbitofrontal cortex (OFC) (e.g., [23,24–27]), which motivate eating behavior. Individual differences in food cue reactivity relate to eating and health outcomes—including weight status [28,29], weight gain [23], post-surgery weight loss [30], body fat [25], moment to moment food cravings [24], and giving in to food cravings [31]. Although palatable foods reliably activate the brain’s reward system and motivate consumption (e.g., [23,24]), changes in valuation or enhanced regulation by cognitive control regions [32] might attenuate this response in the presence of calorie information.

Influence of calorie information and dieting status on brain activation

Responses in reward-related brain regions, specifically the OFC, scale with idiosyncratic food preferences, such that more preferred foods elicit more reward activation [33,34]. These reward responses are also sensitive to broader contextual features, like the presence of alternative rewards [35], hunger/satiety [36], and value changes [36,37]. As most research in food reward has focused on sensitivity of the reward system to appetitive aspects of food, it is not well understood how other aspects of food, like its healthiness, may alter reward responses. One study found that the presence of health cues nudged healthier food decisions by altering value-related brain activation in the ventromedial prefrontal cortex (VMPFC) [38]. Calorie information, in particular, may signal a food’s healthiness and provide another dimension on which to evaluate food options. That is, in the absence of calorie information, people may make food decisions based on the perceived tastiness and appetitive features of a food. Conversely, when foods are presented in conjunction with calorie information, they may instead rely on their ability to self-regulate and make decisions based on health and their broader dieting goals. This shift may rely on brain-based valuation processes.

Calorie information may additionally trigger increases in cognitive control activation (e.g. the fronto-parietal [FP] system), particularly for dieters regulating food desire [32]. Prefrontal cortical activation often increases in response to food cues for populations concerned with weight management, namely those who are obese [39,40] or dieting [41]. Indeed, one study measured activation of the FP control system and the reward system in chronic dieters viewing food images [31]. Over the subsequent week, these dieters completed daily assessments of their real-world eating behaviors, including how often they gave in to a food desire. In that study, the relative balance of FP control activation to reward activation among dieters viewing food images predicted the percentage of enacted food desires over the subsequent week—highlighting the importance of these brain systems for the self-control of food desire.

Dieters may be particularly sensitive to the presence of calorie information because of their concern for restraining caloric intake. In lab studies, dieters eat fewer high-fat and unhealthy foods when their self-regulatory goals are primed by the surrounding context—e.g., by presence of a mirror [42] or thin human-like sculptures [43]. In fact, calorie labels more strongly influence dieters’ food choices [7] and how much they eat [44], even if the calorie information they receive is incorrect. Through their experience attending to food calories, dieters may learn to represent foods differently than non-dieters—possibly integrating the perceived healthiness of foods with other appetitive characteristics, even in the absence of explicit health information. By using representational similarity analysis (RSA)—which permits a more direct comparison of the shared information across different conditions [45]—we can compare dieters’ and non-dieters’ representations of food inside and outside the context of calorie information. If brain activation patterns across the two groups differentially change with exposure to calorie information, it may suggest a baseline difference in dieters’ and non-dieters’ representation of food.

Given that prior research has shown that calorie information has a strong influence on dieters’ food choices and consumption, we sought to investigate how the presence or absence of calorie information altered neural responses to food items in brain regions associated with reward and self-control. Specifically, we tested whether the presence of calorie information would simultaneously decrease reward-related activation while increasing activation in regions associated with cognitive control during the evaluation of food images. Next, we tested whether the presence of calorie information would alter the association between reward-related activation and idiosyncratic food preferences using a parametric modulation approach. Another aim of the current study was to evaluate the sensitivity of dieters’ and non-dieters’ brain responses to calorie-labeled foods. We approached this goal in three complementary ways: first, we compared activation magnitudes of as a function of dieting status; second, we compared the association between reward-related activation and food preferences by dieting status; and third, we compared differences in neural representations of calorie-labeled and calorie-unlabeled foods between these groups using representational similarity analysis.

Participants

Fifty dieting and non-dieting participants (M age = 19.7, range 18–22) were recruited to participate in the present study. Participants provided their written informed consent to participate in this study. Afterward, they received monetary compensation or class credit for participating in the study. All participants reported a normal neurological history and had normal or corrected-to-normal visual acuity, and only one participant was left-handed. FMRI data were excluded for participants (n = 8) whose movement during any run of the scan exceeded 3-mm in translation or 2 degrees in rotation. Participants completed the Revised Restraint scale [46] to estimate their chronic dieting tendencies. Following convention, participants scoring greater than 14 (across both the “Weight Fluctuation” and “Concern for Dieting” subscales) were classified as dieters and those scoring 14 or lower were classified as non-dieters [47]. Dieters and non-dieters were explicitly from a participant pool based on their early Restraint scale scores, so they were treated categorically in this study. Of the 42 participants (M age = 19.6, 9 male) included in analyses, 22 were dieters and 20 were non-dieters. Each participant’s weight, BMI, and percent body fat (Table 1) were obtained from a Tanita body composition scale (TBF-300A Arlington Heights), which estimates body composition using bioelectrical impedance analysis. On average, participants were in the normal weight range (M BMI = 23.5), with only two (one dieter, one non-dieter) classifying as obese (BMI > 30).

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Table 1. Participant characteristics by dieting status.

https://doi.org/10.1371/journal.pone.0204744.t001

Stimuli

Stimuli consisted of 180 food images collected from two sources: 1) the food-pics database [48], and 2) from popular fast food restaurant websites where calorie information (total kilocalories) was provided. Images selected from the internet were edited to replace any existing backgrounds with a white background and were resized to 650 x 450 pixels using Adobe Photoshop. As part of the manipulation of interest, each food image was paired with either an arbitrary number (e.g., “image 002”) or calorie content (e.g., “450 calories) associated with the food—which appeared on the screen below the food image (Fig 1). The same images were displayed twice—once with the image number and once with accurate calorie information (M = 396.5, SD = 240.2).

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Fig 1. Representation of the food evaluation paradigm.

180 food images were presented (2.5s trial, 0-10s ITI) under two conditions: without calorie information and with accurate calorie information. At the beginning of each run, participants were instructed to evaluate how much they would like to eat the food presented on each trial if they saw it in the campus dining hall (1 = “not at all”– 4 = “very much”).

https://doi.org/10.1371/journal.pone.0204744.g001

Experimental design and procedure

During the scan, participants first viewed the images of food paired with an accompanying image number (NO CALORIES: runs 1–2), and subsequently viewed these same food images paired with the corresponding calorie information (CALORIES: runs 3–4) (Fig 1). Under the guise of gauging college student food preferences for the dining hall, participants were asked to indicate how much they would like to eat each of the foods if it were offered in the campus dining hall on a scale from 1 to 4 (1 = “not at all”, 4 = “very much”). In this manner, we obtained an explicit food preference for each item while ensuring that the participants were paying attention to the images. On trials when calorie information was presented, they were told that because federal regulation might require eating establishments to provide nutritional information for their foods, we were interested in how it might influence food choices and preferences. So that participants were not thinking about calories when none were present, all calorie-present trials came after the calorie-absent trials.

Following the scan, participants again viewed all of the same food images and were asked to estimate the number of calories for each food. To obtain true estimates of food calories, rather than relying on participants’ memory for the previously presented calorie information, we falsely informed participants that the calories they had observed during the task may have been altered for the study (and could be inaccurate) and should be disregarded when making their estimates.

Behavioral data analysis

To analyze the difference in behavioral food ratings across condition (CALORIES/NO CALORIES), a linear mixed-effects model was used with the lme4 package [49] in R [50]. Random intercepts for both subject and item (food image) were entered into the model to account for variability in ratings attributable to the particular food or person-level preferences. Pooled degrees of freedom using the Satterthwaite approximation are reported for each effect.

Imaging apparatus

Imaging was performed on a Philips Intera Achieva 3.0 Tesla scanner (Phillips Medical Systems, Bothell, WA) using a 32-channel SENSE (SENSitivity Encoding) head coil. Stimuli were presented from an Apple MacBook Pro laptop computer running PsychoPy v1.80 software [51]. An Epson (model ELP-7000) LCD projector displayed the stimuli on a screen at the head end of the scanner bore. Subjects viewed that screen through a mirror mounted on top of the head coil. An MR compatible keypress interfacing with the Cedrus Lumina Box recorded participant’s responses.

Image acquisition

Anatomical images were acquired using a high-resolution 3-D magnetization-prepared rapid gradient-echo sequence (TR = 9.9ms; TE = 4.6 ms; flip angle = 8°; 1x1x1mm³ voxels). Functional images were collected using T2* fast field echo, functional EPIs sensitive to BOLD contrast (TR = 2.5 seconds, TE = 35 ms, flip angle = 90°, 3x3x3mm³ voxels). During each of the four functional runs, 142 axial images (35 slices) were acquired allowing complete brain coverage. The presentation sequence of food images and jittered fixation trials (inter-trial interval of 0-10s; 30% of total duration), were pseudo-randomized to increase the efficiency of estimating task-related BOLD activation.

Image preprocessing

Neuroimaging data were analyzed using SPM8 (Wellcome Department of Cognitive Neurology, London, UK) in conjunction with a suite of preprocessing and analysis tools (https://github.com/ddwagner/SPM8w). The functional data were slice time corrected, realigned within and across runs to correct for head movement and transformed into a standard anatomic space (3-mm isotropic voxels) based on the ICBM 152 brain template space [Montreal Neurological Institute (MNI)]. Normalized data were then smoothed spatially using a 6-mm Gaussian kernel. To further account for motion artifact, participants that demonstrated substantial movement (> 3-mm in translation or 2 degrees in rotation) were discarded.

Whole brain comparison of food images > calorie-labeled food images

For each participant, a general linear model (GLM) incorporating task effects (modeled as onsets of events of interest convolved with the canonical hemodynamic response function), were used to compute beta images (parameter estimates) representing task-related effects for each voxel in the brain. Nuisance regressors included six motion parameters (x, y, z directions and roll, pitch, yaw rotations), a linear drift, and run constants. The resulting beta images were used to compute a whole-brain voxelwise contrast comparing the food images to the calorie-labeled food images. Subsequent contrast maps were used as input into a group-level random-effects analysis to identify brain areas that were consistently active across participants. The resulting group-level map was voxelwise thresholded at p < 0.005, and cluster corrected to p < 0.005 (minimum extent threshold: k = 148 contiguous voxels) using AFNI’s 3dClustSim with the spatial autocorrelation function to control false positive rates (see [52,53]). The minimum cluster size required for whole-brain multiple comparisons correction was calculated from Monte Carlo simulations (10,000 iterations).

Region of interest (ROI) analyses

Because research on the self-regulation of food reward has pointed to the opposing influence of reward and cognitive control systems on food decisions [32,38,62], we predicted that calorie information might modulate both of these systems. To address these predictions, we extracted parameter estimates from both brain systems by applying two respective ROI masks. To examine reward system activation to food cues, we defined an ROI mask from a large-scale online meta-analytic search for the term “food” (reverse inference, p < 0.01 [54]). The activation map was downloaded from neurosynth.org and converted to a binary inclusion mask to target activation associated with food reward. This approach yielded a spatially constrained set of cortical and subcortical regions that were more likely to appear in studies related to food than those that unrelated to food, including the left OFC and the NAcc. The cognitive control system mask was assessed by averaging activation across eight nodes within the fronto-parietal control system ([55,56]; nodes listed in Table 2). To create this mask, an 8-mm sphere was centered over each of the eight nodes, and parameter estimates were extracted and averaged across all eight regions to derive a single composite measure of FP control activation for each participant. Linear mixed-effects models with random intercepts for subjects, to account for individual differences in the recruitment of brain regions when viewing food images, were used to compare neural responses in these ROIs during food evaluations across condition and dieting status.

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Table 2. Table of eight fronto-parietal control system nodes used in a priori analysis [55,56].

An 8-mm sphere was centered over each node and parameter estimates averaged across regions to calculate the aggregate mean FP activation for each participant.

https://doi.org/10.1371/journal.pone.0204744.t002

Parametric modulation by food ratings

To compare the relative contribution of whole-brain activations to food ratings across the two conditions, a parametric modulation analysis was conducted. Using the food image ratings (1–4) collected during the scan, a subject-level regressor was entered into a first-level GLM to identify brain regions whose activity increased linearly with increasing food preference ratings. One regressor corresponding to the food preference rating was entered for each subject at each timepoint. Specifically, though each food was presented twice (once per condition), food preference ratings were collected both times; and brain activation was regressed with the ratings acquired during the same trial. Next, a second-level t-test was conducted to compare parametric modulation for the two conditions (NO CALORIES > CALORIES). The resulting group-level map was voxelwise thresholded at p < 0.005, and cluster corrected to p < 0.005 (minimum extent threshold: k = 38 contiguous voxels) as recommended by AFNI’s 3dClustSim with the spatial autocorrelation function (see [52,53]). Parameter estimates were extracted from the reward system (Neurosynth-defined “food” mask) ROI to determine whether the association between reward activation and food preferences [33] were attenuated in the presence of calorie information.

Representational similarity analysis

A whole brain searchlight (radius = 3 voxels) representational similarity analysis (RSA; [45,57]) using multivariate pattern analysis (MVPA) with the PyMVPA software package [58] was conducted to compare the similarity in activation patterns across the CALORIE and NO CALORIE conditions. Voxel-level Fisher-z transformed correlation values (1 –correlation distance) representing similarity across the CALORIE and NO CALORIE conditions were submitted to a group t-test comparing dieters and non-dieters to identify regions where dieters more similarly represented foods regardless of condition (CALORIES/NO CALORIES) than non-dieters. The resulting group-level map was voxelwise thresholded at p < 0.01. Though no clusters survived, the minimum extent threshold required for whole-brain cluster correction to p < 0.01 was 459 contiguous voxels.

Behavioral results

Food ratings decreased when images were paired with calorie information than when paired with an arbitrary image number, B = 0.13, 95% CI [0.12, 0.15], t(14180) = 17.24, p < 0.001 (Fig 2A). A second model tested a difference in behavioral food ratings across condition and dieting status (dieters vs. non-dieters), with a random intercept for subject and item (to control for individual preferences and variations in baseline ratings of individual foods, respectively). Overall, the reduction in food ratings when calories were present was stronger for dieters than non-dieters, B = 0.06, 95% CI [0.03, 0.09], t(14180) = 3.71; p = 0.0002 (Fig 2B). The absolute proportional deviation in calorie estimates from actual calories deviation [i.e., abs(estimated calories—actual calories) / actual calories] did not differ for dieters and non-dieters, B = -0.06, 95% CI [-0.14, 0.01], t(40) = 1.66; p = 0.10.

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Fig 2.

(A) Ratings of willingness to eat foods presented in images with NO CALORIES and with CALORIES. Willingness to eat was lower when presented with calories—and this difference was greater for dieters than non-dieters. (B) Food ratings by dieting status.

https://doi.org/10.1371/journal.pone.0204744.g002

fMRI results

There was a system (reward vs. FP control) by condition (CALORIES vs. NO CALORIES) interaction, B = -0.22, 95% CI [-0.31, -0.14], t(123) = -5.19, p < 0.001, with calorie-labeled food images eliciting less reward and more control activation. Compared to food images presented without calories, those presented with calories elicited less reward activation in the a priori mask defined from a meta-analysis of the term “food” ([54]; Fig 3), B = 0.08, 95% CI [0.02, 0.14], t(40) = 2.64, p = 0.01. A similar relationship was found in an a priori defined region of the OFC (S2 Fig). In contrast, activation in the FP control system was greater for food images paired with calorie information, B = 0.11, 95% CI [-0.003, 0.19], t(40) = 2.80, p = 0.008. Moreover, there was a marginally significant interaction of condition (CALORIES vs. NO CALORIES) and dieting status (DIETERS vs NON-DIETERS) on the FP control system, B = 0.11, 95% CI [-0.00003, 0.22], t(40) = 1.96, p = 0.06, but not the reward system, B = -0.04, 95% CI [-0.12, 0.04], t(40) = -0.97, p = 0.34. Dieters showed a greater increase in FP control activation in the context of calories, B = 0.22, 95% CI [0.15, 0.30], t(21) = 5.67, p < 0.001, than non-dieters, B = 0.11, 95% CI [0.03, 0.19], t(19) = 2.87, p = 0.01.

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Fig 3.

(A) ROI masks of the reward system (meta-analytic map of “food” [54]) and fronto-parietal control system [55,56]. (B) Greater activation in the reward system for NO CALORIES > CALORIES, and greater activation in the fronto-parietal control system for CALORIES > NO CALORIES. (C) No difference in non-dieters’ and dieters’ recruitment of the reward system but marginally significant difference in recruitment of control system when evaluating calorie-labeled and unlabeled foods. Dieters more strongly activated the fronto-parietal control system in response to calorie-labeled food images. Error bars represent standard error of the mean.

https://doi.org/10.1371/journal.pone.0204744.g003

Additional peak activations from the whole-brain contrast of NO CALORIES > CALORIES (voxelwise p < 0.005, cluster-corrected to p < 0.005; Fig 4) were identified in the left middle occipital gyrus, left supramarginal gyrus, right postcentral gyrus, right inferior frontal gyrus, and right insula (Table 3A); activations associated with CALORIES > NO CALORIES were identified in the right inferior parietal lobule, left inferior parietal lobule, right precuneus, left lingual gyrus, right middle frontal gyrus, and right middle temporal cortex (Table 3B). Additional sub-cluster peaks for the contrast of NO CALORIES > CALORIES appeared in regions of the reward system, including the left OFC (-30, 27, -6: t = 5.44), left NAcc (-9, 3, -6: t = 4.15) and right NAcc (6, 3, -6: t = 3.57).

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Fig 4. Brain regions showing greater activation to food presented with NO CALORIES > CALORIES.

Whole brain cortical activations (voxelwise p < 0.005, cluster-corrected to p < 0.005, 148 contiguous voxels) are projected onto a 3D rendering of an inflated brain using Workbench [59,60] for data visualization. Brain regions responding to NO CALORIES > CALORIES (represented by yellow-red color scale) include the left middle occipital gyrus, left supramarginal gyrus, right postcentral gyrus, right inferior frontal gyrus, and right insula, and brain regions responding to CALORIES > NO CALORIES (represented by blue color scale) include the right inferior parietal lobule, left inferior parietal lobule, right precuneus, left lingual gyrus, right middle frontal gyrus, and right middle temporal cortex.

https://doi.org/10.1371/journal.pone.0204744.g004

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Table 3. A) Regions activating to NO CALORIES > CALORIES (voxelwise p < 0.005, cluster-corrected to p < 0.005, 148 contiguous voxels). B) Regions activating to CALORIES > NO CALORIES (voxelwise p < 0.005, cluster-corrected to p < 0.005, 148 contiguous voxels).

https://doi.org/10.1371/journal.pone.0204744.t003

Reward activation linearly increased with food preferences for foods presented without calories

A parametric modulation analysis revealed a stronger positive relationship between activation of the reward system and food preferences when evaluating food images with NO CALORIES than when evaluating food images with CALORIES in the reward system, B = 0.08, 95% CI [0.006, 0.15], t(40) = 2.12, p = 0.04 (Fig 5). This relationship did not differ for dieters and non-dieters, B = -0.06, 95% CI [-0.16, 0.04], t(40) = -1.13, p = 0.26. A similar relationship was found in an a priori defined region of the OFC (S1 Fig).

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Fig 5. Parametric modulation by food preferences in the reward system.

A) There was a stronger positive association between reward system activation and food preferences when evaluating food images with NO CALORIES than when evaluating food images with CALORIES in the reward system, B = 0.08, 95% CI [0.006, 0.15], t(40) = 2.12, p = 0.04. B) This relationship did not differ for dieters and non-dieters, B = -0.06, 95% CI [-0.16, 0.04], t(40) = -1.13, p = 0.26.

https://doi.org/10.1371/journal.pone.0204744.g005

Several additional brain regions revealed a stronger positive relationship between brain activation and food preferences when evaluating food images with NO CALORIES than when evaluating food images with CALORIES, including left OFC, left cuneus, left medial OFC, left occipitotemporal cortex, and left precentral gyrus (voxelwise p < 0.005, cluster-corrected to p < 0.005; Table 4, Fig 6). Importantly, though this analysis was exploratory in nature, food ratings were more robustly associated with activation in regions of the left OFC (-24, 45, -18) and left medial OFC (-6, 42, -12) in the absence of calorie information. Though these regions have previously been associated with tracking food preferences [33,34,38], here we demonstrate that this relationship is attenuated in the context of additional calorie information. Additional correspondence between precentral gyrus activation and food ratings might reflect overall differences in the ratings—and associated button presses—of unlabeled and calorie-labeled foods. Widespread brain activation, including within regions involved in reward and valuation, scaled with the desirability of the foods, but this relationship was largely was diminished in the presence of calorie information (Fig 7).

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Fig 6. Parametric modulation by food preferences in the whole brain.

(A) Brain regions whose activation when evaluating food images presented with NO CALORIES linearly increased with food preference ratings (voxelwise p < 0.005, cluster-corrected to p < 0.005, 114 contiguous voxels). (B) Brain regions whose activation when evaluating food images presented with CALORIES linearly increased with food preference ratings (voxelwise p < 0.005, cluster-corrected to p < 0.005, 127 contiguous voxels). (C) Greater association between brain activation and food preferences when evaluating food images with NO CALORIES than when evaluating food images with CALORIES in the left OFC, left cuneus, left medial OFC, left occipitotemporal cortex, and left precentral gyrus (voxelwise p < 0.005, cluster-corrected to p < 0.005, 38 contiguous voxels).

https://doi.org/10.1371/journal.pone.0204744.g006

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Fig 7. Parametric modulation by food preferences for NO CALORIES and CALORIES.

Regions whose activation linearly increased with food preferences when NO CALORIES were present (but not when calories were present) depicted in blue. Regions whose activation linearly increased with food preferences when CALORIES were present (but not when calories were absent) depicted in yellow. Overlapping regions whose activation linearly increased with food preferences for both conditions depicted in green.

https://doi.org/10.1371/journal.pone.0204744.g007

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Table 4. Brain regions whose responses were more related to food preferences when evaluating food images with NO CALORIES than when evaluating food images with CALORIES (voxelwise p < 0.005, cluster-corrected to p < 0.005, 38 contiguous voxels).

https://doi.org/10.1371/journal.pone.0204744.t004

Multivariate activation patterns across conditions differed more for dieters than non-dieters

A whole-brain searchlight RSA was performed to identify regions of the brain whose activation patterns were most similar between conditions (CALORIES/NO CALORIES; Fig 8). A group t-test compared dieters to non-dieters to identify regions where dieters showed more similar activation patterns for food images presented with and without calories. Though no clusters survived the cluster-correction threshold recommended by 3dClustSim, at a voxelwise threshold of p < 0.01, the largest cluster (67 contiguous voxels) was in the left OFC (-18, 42, -15).

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Fig 8.

(A) Schematic of multivariate searchlight RSA comparing activation to foods images presented with and without calories. All voxels within a 9-mm searchlight sphere contribute to the cross-condition correlation value at that location. This process is iterated across the entire brain to generate a whole-brain cross-condition correlation map for each subject. Between subjects, a whole-brain group comparison reveals any group differences in cross-condition correlation values. (B) Statistical maps of whole-brain cross-condition correlations for non-dieters and dieters (voxelwise t > 10, cluster correction p < 0.001). (C) Statistical map representing the interaction between group (DIETERS > NON-DIETERS) and condition (NO CALORIES/CALORIES) pattern similarity from multivariate searchlight RSA (voxelwise p < 0.01, thresholded to 60 contiguous voxels), featuring one cluster in the left OFC (-18, 42, -15, ventral view).

https://doi.org/10.1371/journal.pone.0204744.g008