Neural indicators of food cue reactivity, regulation, and valuation and their associations with body composition and daily eating behavior

Abstract

Exposure to food cues activates the brain’s reward system and undermines efforts to regulate impulses to eat. During explicit regulation, lateral prefrontal cortex activates and modulates the activity in reward regions and decreases food cravings. However, the extent to which between-person differences in the recruitment of regions associated with reward processing, subjective valuation and regulation during food cue exposure—absent instructions to regulate—predict body composition and daily eating behaviors is unclear. In this preregistered study, we pooled data from five functional magnetic resonance imaging (fMRI) samples (N = 262) to examine whether regions associated with reward, valuation and regulation, as well as whole-brain pattern expression indexing these processes, were recruited during food cue exposure and associated with body composition and real-world eating behavior. Regression models for a single a priori analytic path indicated that univariate and multivariate measures of reward and valuation were associated with individual differences in body mass index and the enactment of daily food cravings. Specification curve analyses further revealed reliable associations between univariate and multivariate neural indicators of reactivity, regulation and valuation and all outcomes. These findings highlight the utility of these methods to elucidate brain–behavior associations and suggest that multiple processes are implicated in proximal and distal markers of eating behavior.

Associations between use of self-regulatory strategies and daily eating patterns: An experience sampling study in college-aged women

“…We conducted secondary analyses on data that were collected as part of two separate projects (n = 31, n = 75) that tested independent hypotheses pertaining to brain-behavior relationships in the eating domain that have been previously reported (Cosme & Lopez, 2020;Lopez et al, , 2014Lopez et al, , , 2017. Since we are using secondary data, the current study design was not preregistered; however, we preregistered the analytical plan for this particular paper prior to conducting any analyses (https:// osf.…”

Revisiting the Neural Architecture of Adolescent Decision-Making: Univariate and Multivariate Evidence for System-Based Models

“…Although it is not a given that psychological modularity necessitates neural modularity, this assumption has been preserved in many neuroscientific implementations of system-based theories (Strang et al, 2013;Shulman et al, 2016), despite evidence in adults that multivariate patterns reflect meaningful information about decision-making (Hampton and O'Doherty, 2007). Our univariate and multivariate results, somewhat surprisingly, provide support for both modularity and population coding, respectively (Cosme and Lopez, 2020); specifically, results revealed that univariate NAcc and lPFC activity was associated with decision behavior, and also that PE of a multivariate cognitive control (but not value) signature predicted decisions. The former (evidence of modularity) is surprising, given the limited support for neural modularity that prior studies have found (Erickson, 2001), whereas the latter (population coding) is notable because no prior studies, to our knowledge, have found evidence of such in the context of brain modeling decision behavior (i.e., using multivariate metrics to model behavior/cognition).…”

“…In other words, the multivariate patterns code for a given psychological process, whereas the univariate activity of the modules controls the intensity of the process. Indeed, such multidimensional coding schemes appear to support decision behaviors in monkeys (Zhang et al, 2019), and similar findings from human samples in other domains (eating behavior) further hint at the neural plausibility of a modular-population hybrid scheme (Cosme and Lopez, 2020). Further work could also examine whether there is a qualitative shift between coding schemes across development (Gee et al, 2013).…”

“…An alternative to brain mapping is brain modeling (Kragel et al, 2018), which uses neuroscientific data to predict cognitions and behavior (i.e., any kind of neural metric predicting behavior). Having recently grown in popularity, brain modeling approaches have seen broad applications, some of which involve within-person modeling, including prediction of food craving (Cosme et al, 2019;Cosme and Lopez, 2020), emotion regulation tendencies (Doré et al, 2017), negative affect (Chang et al, 2015), chronic pain (Wager et al, 2013), and vision (Liu et al, 2018;Gardner and Liu, 2019). While brain mapping has generated key discoveries in neuroscience (e.g., Kanwisher, 2017), it can be problematic for evaluating system-based theories.…”

Age-related change in task-evoked amygdala—prefrontal circuitry: a multiverse approach with an accelerated longitudinal cohort aged 4-22 years

“…Despite not being completely comprehensive, this approach still allowed for thorough investigation into the robustness of results. For all multiverse analyses, we constructed specification curves by ranking models by their beta estimates (ascending) for parameters of interest for interpretation and visualization (Cosme & Lopez, 2020;Klapwijk et al, 2019;Orben & Przybylski, 2019;Simonsohn et al, 2015Simonsohn et al, , 2020. Because specification choices were not preregistered, we did not conduct formal null hypothesis testing of specification curves.…”