Prediction of daily food intake as a function of measurement modality and restriction status

Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment

Social neuroscientists often use magnetic resonance imaging (MRI) to understand the relationship between social experiences and their neural substrates. Although MRI is a powerful method, it has several limitations in the study of social experiences, first and foremost its low ecological validity. To address this limitation, researchers have conducted multimethod studies combining MRI with Ecological Momentary Assessment (EMA). However, there are no existing recommendations for best practices for conducting and reporting such studies. To address the absence of standards in the field, we conducted a systematic review of papers that combined the methods. A systematic search of peer-reviewed papers resulted in a pool of 11,558 articles. Inclusion criteria were studies in which participants completed (a) Structural or functional MRI and (b) an EMA protocol that included self-report. Seventy-one papers met inclusion criteria. The following review compares these studies based on several key parameters (e.g., sample size) with the aim of determining feasibility and current standards for design and reporting in the field. The review concludes with recommendations for future research. A special focus is given to the ways in which the two methods were combined analytically and suggestions for novel computational methods that could further advance the field of social neuroscience.

Brain Activity Associated With Regulating Food Cravings Predicts Changes in Self-Reported Food Craving and Consumption Over Time

Neural patterns associated with viewing energy-dense foods can predict changes in eating-related outcomes. However, most research on this topic is limited to one follow-up time point, and single outcome measures. The present study seeks to add to that literature by employing a more refined assessment of food craving and consumption outcomes along with a more detailed neurobiological model of behavior change over several time points. Here, a community sample of 88 individuals (age: M = 39.17, SD = 3.47; baseline BMI: M = 31.5, SD = 3.9, range 24–42) with higher body mass index (BMI) performed a food craving reactivity and regulation task while undergoing functional magnetic resonance imaging. At that time—and 1, 3, and 6 months later—participants reported craving for and consumption of healthy and unhealthy foods via the Food Craving Inventory (FCI) and ASA24 (N at 6 months = 52–55 depending on the measure). A priori hypotheses that brain activity associated with both viewing and regulating personally desired unhealthy, energy-dense foods would be associated with self-reported craving for and consumption of unhealthy foods at baseline were not supported by the data. Instead, regression models controlling for age, sex, and BMI demonstrated that brain activity across several regions measured while individuals were regulating their desires for unhealthy food was associated with the self-reported craving for and consumption of healthy food. The hypothesis that vmPFC activity would predict patterns of healthier eating was also not supported. Instead, linear mixed models controlling for baseline age and sex, as well as changes in BMI, revealed that more regulation-related activity in the dlPFC, dACC, IFG, and vmPFC at baseline predicted decreases in the craving for and consumption of healthy foods over the course of 6 months.

Multivariate neural signatures for health neuroscience: assessing spontaneous regulation during food choice

Establishing links between neural systems and health can be challenging since there is not a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two 'neural signatures' of food craving regulation (CR) using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring CR ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded CR from food viewing and higher signature expression was associated with less craving. During food choice, expression of the regulation signature was stronger for unhealthy foods and inversely related to subjective value, indicating that participants engaged in CR despite never being instructed to control their cravings. Neural signatures thus have the potential to measure spontaneous engagement of mental processes in the absence of explicit instruction, affording greater ecological validity. We close by discussing the opportunities and challenges of this approach, emphasizing what machine learning tools bring to the field of health neuroscience.

Neural predictors of eating behavior and dietary change

Recently, there has been an increase in the number of human neuroimaging studies seeking to predict behavior above and beyond traditional measurements such as self-report. This trend has been particularly notable in the area of food consumption, as the percentage of people categorized as overweight or obese continues to rise. In this review, we argue that there is considerable utility in this form of health neuroscience, modeling the neural bases of eating behavior and dietary change in healthy community populations. Further, we propose a model and accompanying evidence indicating that several basic processes underlying eating behavior, particularly reactivity, regulation, and valuation, can be predictive of behavior change. We also discuss future directions for this work.

Cognitive reappraisal of low-calorie food predicts real-world craving and consumption of high- and low-calorie foods in daily life

In an increasingly obesogenic environment, an individual's regulatory capacity to pursue nutrient-rich, low-calorie foods over palatable, energy-dense items is essential to maintaining a healthy weight and preventing the detrimental health risks of obesity. Cognitive reappraisal, the process by which one changes the meaning of a stimulus by altering its emotional impact (or in this case, its appetitive value) demonstrates promise as a regulatory strategy to decrease obesogenic food consumption, but little research has directly addressed the relationship between cognitive reappraisal of food cravings and real-world eating behaviors. Additionally, research examining self-regulation of eating has typically focused exclusively on diminishing cravings and consumption of unhealthy, high-calorie foods, rather than examining, in tandem, ways to strengthen (or, up-regulate) cravings for healthier, low-calorie alternatives. In the present study, fifty-seven college aged participants first completed a cognitive reappraisal task in the laboratory in which they practiced regulating their craving responses to high- and low-calorie food items by focusing on the long-term health consequences of repeatedly consuming the pictured foods. Next, for a week following the laboratory session, participants reported daily eating behaviors via ecological momentary assessment. Participants who reported greater up-regulatory success during the reappraisal task also reported increased craving strength for low-calorie foods as well as decreased consumption of high-calorie foods in their daily lives. Greater overall regulation success also predicted more frequent consumption of craved low-calorie foods. These findings substantiate the association between cognitive reappraisal ability and real-world appetitive behaviors, and suggest that future interventions may benefit from specifically targeting individuals' evaluations of low-calorie foods.

A balance of activity in brain control and reward systems predicts self-regulatory outcomes

Previous neuroimaging work has shown that increased reward-related activity following exposure to food cues is predictive of self-control failure. The balance model suggests that self-regulation failures result from an imbalance in reward and executive control mechanisms. However, an open question is whether the relative balance of activity in brain systems associated with executive control (vs reward) supports self-regulatory outcomes when people encounter tempting cues in daily life. Sixty-nine chronic dieters, a population known for frequent lapses in self-control, completed a food cue-reactivity task during an fMRI scanning session, followed by a weeklong sampling of daily eating behaviors via ecological momentary assessment. We related participants’ food cue activity in brain systems associated with executive control and reward to real-world eating patterns. Specifically, a balance score representing the amount of activity in brain regions associated with self-regulatory control, relative to automatic reward-related activity, predicted dieters’ control over their eating behavior during the following week. This balance measure may reflect individual self-control capacity and be useful for examining self-regulation success in other domains and populations.