Monday, May 6, 2024
5/6/2024
PROJECT SUMMARY Overweight (OW) and obesity (OB) are prevalent, unhealthy, and costly. The obesogenic environment is a main contributor to OW and OB, filled with visual cues signaling highly varied, highly palatable, and fattening foods, that can be potential triggers for approach motivation and consummatory behavior. Food cues additionally lead to cue-induced craving, and associations between food cue-reactivity, craving, and weight gain make craving a probable contributor to the OB epidemic. Personalized treatment innovations for OW and OB that target food cue-reactivity and craving are sorely needed for prevention and treatment. One promising approach to alleviate food cue-reactivity and craving is real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF). Beginning rtfMRI-NF work related to craving is promising, including studies showing that this approach can be used to reduce food and drug cue-related brain activity and craving. However, prior rtfMRI-NF studies of food cues are limited by 1) no studies provided feedback from complex brain networks, 2) no studies used a viable clinical brain biomarker for feedback, and 3) no studies linked feedback training to eating behavior. To address these gaps, we have developed and tested connectome-based rtfMRI-NF to provide feedback based on functional connectivity within complex whole brain networks. We used connectome-based predictive modeling, a well-validated machine learning approach for generating predictive models of brain-phenotype associations from functional connectivity data, to identify a transdiagnostic brain biomarker of craving, the “craving network,” that can be targeted for rtfMRI-NF. This proposed project will use connectome-based rtfMRI-NF to train volitional control of the “craving network” in response to food cues among individuals with OW or OB and high craving and measure effects on craving and eating behavior. Individuals with OW or OB and high craving (N=50) will be randomized to receive either active or sham (yoked) connectome-based rtfMRI-NF at 3 weekly training scans including feedback, transfer (no feedback), and resting state runs. Food craving and eating behavior will be measured at each scan and at 1 month. Aim 1 will test the hypothesis that individuals with OW and OB can learn to reduce craving network strength using rtfMRI-NF. Aim 2 will test the hypothesis that craving network feedback is associated with reduced food craving and positive changes in eating behavior, and that reduced craving network strength will be correlated with reduced craving. Aim 3 will test the hypothesis that craving network feedback is associated with decreased craving network strength during resting state, a measure of basal changes without explicit engagement of the craving network. Overall, this project builds on important prior research and preliminary data demonstrating the significance and feasibility of testing a highly innovative approach to reduce food cue-reactivity and craving using connectome-based rtfMRI-NF. Findings will inform a fully powered larger trial, and should impact research on OW and OB and improve our understanding of craving which is generalizable.
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