Functional Brain Networks: Unique Patterns with Hedonic Appetite and Confidence to Resist Eating in Older Adults with Obesity

Abstract

Objective: The purpose of this study was to determine whether baseline measures of hedonic hunger-the Power of Food Scale-and selfcontrol for food consumption-the Weight Efficacy Lifestyle Questionnaire-were associated with network topology within two sets of brain regions (regions of interest [ROIs] 1 and 2) in a group of older adults with obesity. These previously identified brain regions were shown in a different cohort of older adults to be critical for discriminating weight loss success and failure. Methods: Baseline functional magnetic resonance imaging data (resting state and food cue task) were collected in a novel cohort of 67 older adults with obesity (65-85 years, BMI = 35-42 kg/m 2) participating in an 18-month randomized clinical trial on weight regain. Results: The Power of Food Scale was most related to ROI 1, which includes the visual cortex and sensorimotor processing areas during only the food cue state. During both the food cue and resting conditions, the Weight Efficacy Lifestyle Questionnaire was associated with ROI 2, which includes areas of the attention network and limbic circuitry. Conclusions: Our findings show critical, distinct links between brain network topology with self-reported measures that capture hedonic hunger and the confidence that older adults have in resisting the consumption of food because of both intrapersonal and social/environmental cues.

Longitudinal relationship of baseline functional brain networks with intentional weight loss in older adults

“…Within the context of weight loss, we would argue that connectivity within this network is a manifestation of reward valuation and incentive sensitization that have been emphasized by Stice and Burger as central theories in obesity and overeating (11). This interpretation is supported, in part, by prior cross‐sectional data on this cohort that we have published previously, in which FN1 was found to be related to self‐reported ratings on the Power of Food Scale (23).…”

“…In combination with the data from FN1, our findings suggest that concepts important to weight loss are distributed throughout the brain, as opposed to being associated with specific regions of the brain. Of note is that, in cross-sectional analysis on this cohort mentioned earlier in this article, we also found that connectivity within both FN1 and FN2 was significantly associated with a measure of self-efficacy that assesses people's ability to resist consuming favorite foods when exposed to internal states, environmental cues, and social contexts that pose a risk for promoting hedonic eating (23). As mentioned in the introduction, within the field of network science, a consistent observation has been that there is a decrease in the connectivity of brain networks with aging (6,7).…”

Weaker connectivity in resting state networks is associated with disinhibited eating in older adults

“…Thirdly, although our choice of target networks was theoretically driven, the observed effects may extend beyond those networks. For example, a wider network of brain regions (bilateral frontal and parietal regions, amygdala, temporal pole, hippocampus, fusiform gyrus, and inferior insula) were previously associated with confidence in resisting eating in the absence of hunger [ 16 ]. Future research comparing older and younger adults might profit from assessing global brain connectivity and / or applying graph theoretic metrics that are able to provide a more comprehensive understanding of the network topology and their interactions.…”

“…In this context, it is plausible that changes in functional connectivity in older adults might be linked to their differing eating behaviour. For example, it was recently observed that in older adults with obesity, confidence to resist eating was associated with connectivity between attentional control regions of the brain, and the limbic circuitry involved in interoceptive, emotional, and hedonic responses [ 16 ]. Although similar effects are reported in young samples [ 17 , 18 ], researchers have used various scanning/analysis techniques making comparison between ages difficult.…”

A Mixed-Modeling Framework for Whole-Brain Dynamic Network Analysis

“…Several extensions of the original framework (Bahrami, Laurienti et al 2019, Simpson, Bahrami et al 2019), as well as a Matlab toolbox with user-friendly graphical user interfaces (GUIs) (Bahrami, Laurienti et al 2019) have recently been introduced. The original model and its extensions have been used in several studies (Bahrami, Laurienti et al 2017, Burdette, Laurienti et al 2020). However, it has yet to be extended to the dynamic network context.…”