PMC:4428493 / 25415-43097 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4428493","sourcedb":"PMC","sourceid":"4428493","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4428493","text":"Discussion\nThis is the first systematic review to investigate neural responses to visual food cues across all weight categories and provide a meta-analysis of neural changes before and after weight loss. This review highlights that neural responses to visual food cues differ depending on the weight status of individuals, and changes in neural activation patterns are observed pre- to post-weight loss. More specifically, obese individuals compared to healthy weight had increased activation to foods compared to non-foods, and this was most pronounced in response to energy dense foods. This was evident in brain regions associated with the anticipation of the rewarding value of the food, emotion and memory associated with previous experiences with the food, and visual processing of the food cues. Obese individuals were more responsive to food cues in the satiated state compared to healthy weight individuals. Additionally, weight loss reduced neural responses in areas related to executive functioning, impulsivity, and reinforcement learning to visual food cues, despite differences in the modality of the intervention. However, inconsistent activation patterns were reported across studies, which may be attributable to the variety of participant groups recruited, pre-scan preparation, and the chosen fMRI parameters (e.g., block design vs. event-related). The fMRI findings of the current review are consistent with previous published reviews in the area (19–21).\nWhen comparing healthy weight and overweight/obese participants, increased reward-related responses to food (e.g., insula and OFC), particularly high-calorie foods, compared to non-food were found in obese participants. It has been suggested that obesity may be linked to an increase in neural-related reward anticipation from food cues, and a decrease in reward during food consumption. This could potentiate overeating to compensate for imbalances in the neural reward pathways and subsequent diminished experience of reward (7, 97). Although only three studies reported a correlation between neural responses and BMI (37, 44, 47), the increased responses reported in these studies of obese participants compared to normal-weight participants may explain some individual’s vulnerability to overeating, food cues, and possible diet failures (7, 10). Similar findings have been reported by Garcia-Garcia et al. and Asmaro et al. who noted differential activation patterns between obese and healthy weight individuals to the sight of food, particularly in reward-related regions. However, individuals with binge-eating disorder and studies using taste stimuli were also included in previous reviews, which may have recruited additional areas of the brain and potentially confound the findings of these studies.\nThis review extends current literature regarding neural responses to visual food cues by examining brain activation by weight status category (i.e., healthy weight, overweight/obese) as well as pre- and post-weight-loss responses. Results from weight loss studies suggest that gastric bypass surgery reduces reward responses from pre- to post-surgery (49, 52, 57–59). Although consistent reductions in neural activation were observed irrespective of method of weight loss, changes in activation via surgical weight loss differed to activation changes observed in behavioral weight loss that focused on diet and exercise (53). Interestingly, in both behavioral and surgical interventions, brain activity prior to weight loss in areas related to reward anticipation and impulsivity (e.g., anterior cingulate cortex and nucleus accumbens), and decision making (dorsolateral PFC) predicted degree of weight loss success. This provides insight that both surgical and behavioral weight loss may be underpinned by a neural mechanism as well as restriction of the amount of food consumed. In addition, individuals who had maintained successful weight loss showed increased neural activity in regions associated with inhibitory control compared to obese individuals and increased responses in areas related to memory compared to healthy weight individuals (50, 51, 55, 56). The findings of the review highlight that neural-related mechanisms may make some people more predisposed to weight regain, despite successful loss of weight. This may have important implications for obesity follow up and treatment, and provides evidence that neural mechanisms may affect weight loss success or predict proneness to relapse. The high cost of MRI precludes large scale scanning of subjects engaged in weight-loss programs, but use of fMRI in focused clinical trials could be used to validate changes in neurocircuitry patterns associated with successful maintenance of weight loss.\nThe results of the meta-analysis revealed that there were some small regions of convergence of brain responsivity across weight-loss interventions. Deactivation was observed from pre- to post-weight loss in areas involved in emotion and memory (e.g., cingulate gyrus and precuneus), visual processing (e.g., superior occipital gyrus), learning centers (e.g., lentiform nucleus and cingulate gyrus), and motor regions (e.g., precentral gyrus and lentiform nucleus) (96). This may imply that individuals who have experienced weight loss regardless of modality also have changes in neural activation associated with memory and emotion of previous experiences with the food as well as alterations in the processing of external food inputs. This also suggests that individuals who have experienced weight loss have corresponding changes in activation based on behaviors that are implicitly learned or reinforced during weight-loss interventions. Additionally, it appears that weight loss could result in changes in the planning and regulation of movements associated with eating such as reaching to obtain the food, chewing, and swallowing. Although several areas survived statistical thresholds, volume of activation was small. This suggests that while there were commonalities across studies regarding reductions in neural activity to food cues across weight-loss interventions, the areas of congruence are minimal. This is likely due to the overall number of studies included and the pooling of different modalities of weight loss into a single meta-analysis and thus results should be interpreted accordingly.\nSignificant differences were found depending on the participant’s motivational state across all weight categories, although fasting times were inconsistent across studies. Obese individuals were found to have activation consistent with continued reward processing (e.g., PFC, OFC, caudate) and emotional responses (e.g., insula, caudate, amygdala) to food cues following a meal compared to healthy weight controls. That is, obese individuals appear to be more reactive to high-calorie foods and have continued reward processing even following a meal. These findings are consistent with previous literature in this area (21, 23, 24). This is significant as it could provide a neural mechanism for overeating in obese individuals, with neural stimulation even in the state of satiety. In the fasting state, obese individuals were found to have increased activation in areas associated with the anticipation of reward while healthy weight controls were found to have greater activation in areas associated with cognitive control. This suggests that BMI and the hunger-state will greatly affect an individual’s natural desire for food and food reward responsiveness, as well as food choices and subsequent caloric intake (14). This provides preliminary data that could be used in weight interventions regarding meal timing to avoid excessive anticipation for food and subsequent overeating. In addition, notable gender bias was found in the current review, with females displaying differential activation patterns compared to males in regions associated with reward anticipation, food motivation, and inhibitory control based on motivational state, i.e., fasted or satiated. (67, 68, 74, 88). This may indicate that gender influences an individual’s susceptibility to addictive-like eating behaviors.\nThe term ‘food addiction’ has emerged and is being used increasingly in lay literature (98, 99) in association with specific eating patterns and rising levels of obesity (22, 30, 77, 96, 100). It has been postulated that food addiction is associated with specific food-related behaviors including: tolerance to large amounts of food, persistent desire, or craving for specific foods and lack of control over the amount of food consumed (77). In this way food addiction would share similar clinical characteristics that overlap with drug dependence and other common types of addiction, as defined by the Diagnostic and Statistical manual of Mental Disorders version 5 (DSM-V) (29, 77, 101–103). Although, there is debate regarding to the inclusion of food addiction as a DSM-V eating and feeding disorder (3), currently no universally accepted definition for food addiction exists. Findings from this review provide preliminary support for the food addiction hypothesis. Areas of the reward network including the mesolimbic and nigrostriatal regions, which have been implicated in other addictions including drug and alcohol abuse, were activated in response to visual food cues. However, there is a great deal of variation in the brain areas activated across reviewed studies, as well as the experimental conditions used (e.g., motivational state, images presented, fMRI parameters). This indicates that further research utilizing a standardized approach is required to substantiate study findings to either support or refute the existence of food addiction as a distinct phenomenon. This is in line with the findings of Ziauddeen et al. (19) who concluded that while neural activation in healthy weight individuals is somewhat consistent across studies, activation in obese, and binge eaters is too varied to categorically confirm the existence of food addiction.\nAlthough little evidence exists to confirm the types of food associated with food addiction, it is commonly assumed that “junk foods” high in calories, sugar, and fat such as chocolate and potato chips are typical foci of addictive-like tendencies such as craving (27, 76, 104). The range of classifications given to food images in this review such as “high” and “low” calorie foods and “palatable” and “bland” foods appears subjective and arbitrary, with no studies consulting a dietitian or nutritionist. Only seven studies specifically identified actual calorific values of foods and the majority of studies did not pilot test images or provide rationale for their selection of presented foods. The selection of foods in research studies is important to ascertain if it is a universal chemical component of food (e.g., selection based sugar or fat content), a whole category of food (e.g., selection of ‘junk food’), or more simply a personal preference (e.g., selection by administering a pre-scan survey) that could make a food more likely to be overeaten. This could provide further insight into whether food addiction is a plausible phenomenon. Furthermore, the range of control images presented were variable and included cars, landscapes, and blurred images. As these images were not standardized across studies, it is unknown as to whether these assorted images may elicit different neural patterns from one another based on their perceived valence and arousal. The variability of food images used in studies has been previously acknowledged and a database of standardized pictures based on image characteristics and nutrient composition has recently been developed (105). Future studies should consider the use of such a database to facilitate the standardization of images and comparability across studies.\nThe design of an experimental paradigm for fMRI studies requires extensive planning including behavioral predictions of cognitive tasks and the formulation of a hypothesis to inform the task conditions and image acquisition parameters. Ideally, fMRI experiments cover the whole brain with the highest spatial resolution achievable in the shortest time. The trans-axial plane is usually chosen to cover the whole brain in as few slices as possible. In some cases, the acquisition plane is angled to avoid regions of high magnetic susceptibility (e.g., air-tissue and bone-tissue interfaces) that can adversely affect image quality, such as in targeting the reward network. The block design has dominated fMRI due to its ease of implementation, robustness of results, increased statistical power, and relatively large BOLD signal relative to baseline (106, 107). However, blocked paradigms have poorer temporal resolution and are susceptible to stimulus correlated movement artifacts. Event-related paradigms measure responses to single events (typically over 0.5 s), and then combine a large number of those events to improve statistical power. This is advantageous to detect transient variations in hemodynamic responses, reduce the subject’s ability to predict the next event, and allows for post hoc sorting of trials and correlations with other variables (106, 107). Generally, blocked paradigms are most useful to localize activation in brain regions associated with a particular task while event-related paradigms allow for a more in depth investigation of the response profile in an identified brain region.\nAcross the reviewed studies, fMRI results were not reported in a consistent method. Talairach or MNI coordinates, used to describe the location of brain structures independent from individual differences in the size and overall shape of the brain, were reported in the majority of studies. Using the standardized coordinates allows the comparison of brain region activation with other studies, increasing the power of results when combined and can provide a method for meta-analysis (25). Cluster size or volume indicates that the area of neural activation reported in the study is large enough to be statistically plausible rather than just an error in measurement, improving the quality of the study (108). Only half of the studies included in the review reported cluster size or volume.\nThis review is limited by the heterogeneity of study variables used across the reviewed articles, making direct comparisons between studies difficult. As a limited number of studies met the inclusion criteria for the meta-analysis comparing pre- and post-weight loss activation changes, a single exploratory meta-analysis was undertaken combining both surgical and behavioral weight-loss interventions. The pooling of different methods of weight loss could impact the findings of the meta-analysis. However, this data provides insight into neural activation following weight loss, regardless of modality, which needs to be substantiated in future research when more studies have been published in this area. The quality of reporting BMI was inconsistent across studies, with participant groups containing BMI ranges that corresponded with more than one of the WHO categories (54). This could affect possible relationships between fMRI outcomes and weight status. As BMI can be affected by numerous factors including muscle mass, adiposity or body fat percentage is a more reflective measure of obesity assessment and should be considered for use in future studies. The majority of reviewed articles studied adult female participants exclusively, potentially limiting the generalizability of the study findings to other population groups. Finally, the broad age range of the study participants could be a potential limitation of the review, with previous studies showing age-related changes in neural activity including reduced sensitivity of brain areas associated with satiety (109).\nStrengths of this review include the standardization of populations studied and stimuli used to visual food cues exclusively. The use of different stimuli modalities including food consumption (104, 110–122), odors (123–125), and intravenous infusion (126) recruits additional areas of the brain including taste, texture, olfactory, and food intake centers, potentially confounding results. Only visual food cues have been included in the current review in order to minimize the activation of additional areas of the brain. Additionally, the exclusion of different populations, including children and adolescents (127, 128), eating disorders including binge-eating disorder, anorexia nervosa and bulimia nervosa (129–134), and neurological disorders (39, 135), reduces the chance of additional confounding factors contributing to the variations in neural responses to visual food cues. This exclusion criterion is consistent with previous literature in the field (28, 38, 43).\nIt is recommended that future studies report detailed nutritional information of images presented during scanning and BMI classification using the WHO guidelines. This field of research requires the involvement of a multidisciplinary team including imaging specialists, neuroscientists, psychologists, and dietitians to ensure high quality study design. Further, studies should use validated eating behavior questionnaires and use within participant cross-over study design to investigate the impact of motivational state on neural responses. Weight-loss studies using fMRI as an outcome measure should routinely report brain coordinates, cluster size and threshold. Future meta-analyses in this area should investigate responsivity to food cues by specific mode of weight loss (i.e., surgical or behavioral) as these are likely to elicit different changes in neural 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