Effects of Processed Foods on Brain Reward Circuitry and Food Cue Learning

Examine if ultra-processed (UP) foods are more effective in activating reward, attention, and memory brain regions and in promoting food cue learning than minimally-processed foods. Assess individual differences in neurobehavioral responses to UP foods.

Study Overview

• Status: Recruiting
• Conditions: Ultra-processed Foods (UP)

Detailed Description

Obesity is the second leading cause of premature death. Consumption of ultra-processed foods is theorized to be a key cause of obesity. Ultra-processed foods are formulations of cheap industrial sources of dietary energy and nutrients plus additives such as fat, sugar, and flavors that enhance acceptability of the foods.

A cross-over experiment with overweight adults found that ad lib access to an ultra-processed diet for 2-weeks resulted in increased caloric intake (508 kcal/day) and more weight gain versus ad lib access to a minimally-processed diet matched for presented calories, energy density, macronutrients, sugar, sodium, and fiber. The fact that ad lib access to ultra-processed foods resulted in a large increase in caloric intake and weight gain implies that ultra-processed foods may more effectively activate brain regions implicated in reward processing, attention/salience, and memory that influence eating behavior.

However, no brain imaging study has experimentally tested whether ultra-processed foods are more effective in activating brain regions implicated in reward, attention, and memory than minimally-processed foods or experimentally investigated the relative role of the elevated caloric density versus the flavor enhancers of ultra-processed foods in driving greater activation of these brain regions. Preliminary data showed that tastes of ultra-processed high-calorie chocolate milkshake produced greater activation in regions implicated in reward valuation (caudate, nucleus accumbens), attention/salience (precuneus), and memory retrieval (medial temporal gyrus, dorsomedial prefrontal cortex) than tastes of ultra-processed low-calorie chocolate milkshake.

The investigators propose to evaluate the efficacy of ultra-processed foods to activate reward, attention, and memory regions compared to minimally-processed foods, investigate the relative role of the higher caloric content versus the flavor additives/enhancers of ultra-processed foods to engage this circuitry using a 2 x 2 experimental design, test whether ultra-processed foods are more effective in increasing the incentive salience of food cues than minimally-processed foods, which is important because elevated reward region response to food cues/images increases risk for excess weight gain, and test whether individuals who show the greatest responsivity of reward, attention, and memory regions to ultra-processed foods and stronger food reward cue learning are at risk for greater ad lib intake of ultra-processed foods and future body fat gain.

Aim 1: Test the hypothesis that tastes, anticipated tastes, and images of ultra-processed foods activate reward, attention, and memory brain regions more than tastes, anticipated tastes, and images of minimally-processed foods, and evaluate the relative role of the higher caloric content versus flavor additives/enhancers in activating these regions using a 2 x 2 experimental design.

Aim 2: Test the hypothesis that ultra-processed foods foster stronger learning of cues that predict impending tastes of ultra-processed foods than minimally-processed foods, reflected by greater increases in striatal response over the course of cue exposure and quicker responses to cues for tastes of ultra-processed foods.

Aim 3: Test the hypothesis that participants who show greater activation in reward/attention/memory regions in response to tastes, anticipated tastes, and images of ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 3a) and show greater future body fat gain (Aim 3b). Exploratory analyses will establish neural fingerprints that predict ad lib intake of ultra-processed foods and body fat gain (Aim 3c).

Aim 4: Test the hypothesis that participants who show the most pronounced reward cue learning in response to ultra-processed foods will consume more ultra-processed foods ad libitum (Aim 4a) and show greater future body fat gain (Aim 4b).

• Study Type: Observational
• Enrollment (Estimated): 162

Contacts and Locations

• Study Contact: Name: Eric Stice, PhD; Phone Number: 541-222-0615; Email: estice@stanford.edu
• Study Contact Backup: Name: Teena Ambrose, BS; Phone Number: 310-658-6193; Email: tambrose@stanford.edu

Study Locations

• United States
  • California
    • Stanford, California, United States, 94305
      • Recruiting
      • Stanford University
      • Principal Investigator: Eric Stice, PhD
      • Contact: Eric Stice, PhD; Phone Number: 541-222-0615; Email: estice@stanford.edu
      • Contact: Teena Ambrose, BS; Phone Number: 310-658-6193; Email: tambrose@stanford.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Community sample

Description

Inclusion Criteria:

• female and male adolescents 13-15 years of age
• age- and sex- adjusted zBMI scores between the 25th and 75th percentile
• participant and their guardian must be able to read and speak English to gather valid consent

Exclusion Criteria:

• current eating disorders or other major psychiatric disorders (e.g., depression, bipolar, schizophrenia, substance use disorder)
• fMRI contra-indicators (e.g., metal implants, braces, claustrophobia, pregnancy)
• serious medical problems (e.g., Type 2 diabetes, cancer)
• history of food allergies or restrictive dietary requirements (e.g., lactose intolerance, vegan)
• use of psychoactive drugs more than once weekly
• medications that impact appetite or reward functioning (e.g., metformin, anti-psychotic medication, insulin)

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Adolescent youth age 13-15; age- and sex- adjusted body mass index (zBMI) scores between the 25th and 75th percentile

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Baseline functional magnetic resonance imaging (fMRI) Ultra-Processed Foods Taste, Anticipated Taste, and Picture Paradigm	Participants will complete four rounds of an adapted version of a milkshake paradigm to assess activation in response to receipt (taste) and anticipated receipt of 1) high-calorie, ultra-processed food taste, 2) low-calorie, ultra-processed food taste, 3) high-calorie minimally-processed food taste, 4) low-calorie, minimally-processed food taste, and 5) a tasteless odorless solution containing the main ionic components of saliva. Participants will be cued with a picture of the specific food and label (high-calorie milkshake, low-calorie milkshake, high-calorie smoothie, low-calorie smoothie) before receipt of the tastes.	Baseline
Baseline fMRI Ultra-Processed Food Picture Paradigm	Participants will complete two rounds of a block version of the food picture paradigm to examine activation in response to 40 high-calorie ultra-processed food pictures, 40 low-calorie ultra-processed food pictures, 40 high-calorie minimally-processed food pictures, 40 low-calorie minimally-processed food pictures, and 40 bottles of water. Fifty percent of each category will be branded foods/bottles to increase ecological validity. Pictures will be matched for complexity, valence and intensity across categories. After the scan, participants will be shown 40 food pictures, including pictures that were and were not used in the paradigm, and will be asked whether they had seen the foods to assess aided recall. Participants will also be asked to rate palatability of 20 food pictures per food category.	Baseline
Baseline fMRI Ultra-Processed Food Cue Learning Paradigm	Participants will complete two rounds of an adapted version of the food reward learning paradigm wherein four arbitrary fractal cues will signal the delivery of either 0.7 ml of an ultra-processed milkshake, a minimally-processed smoothie, a tasteless solution, and no taste. To make learning more challenging, the paradigm includes both paired cue trials in which the taste is delivered as cued and unpaired cue trials in which the taste is not delivered at an 80:20 ratio. There will be one fractal cue that is not consistently paired with a taste. Participants will be asked to respond with a button press as soon as possible to indicate on which side of the fixation cross the stimuli appeared (providing a behavioral measure of reaction time to each cue).	Baseline
Body Fat	Investigators will use air displacement plethysmography (ADP) via the Bod Pod and bioelectrical impedance (BIA) via the SECA medical body composition analyzer (SECA mBCA) to assess % body fat. The investigators will average the values of both measures. Body density is calculated as body mass divided by body volume; body density is used to calculate % body fat. ADP % body fat shows high test-retest reliability (r = .92-.99) and correlates with dual energy x-ray absorptiometry (DEXA) and hydrostatic weighing estimates (r = .98-.99), with ADP estimate of % body fat falling an average of only 1.7% different relative to DEXA estimates. Height will be measured using a direct reading stadiometer. Weight will be assessed using digital scales with participants wearing light clothing without shoes or coats. Body mass index (BMI)(Kg/M2) will be used to confirm participants are initially at a healthy weight (BMI scores between 25th to 75th age- and sex-adjusted BMI percentile).	Baseline, 1-year, 2-year, 3-year, 4-year follow-up
Baseline Post-fMRI Ad Lib Food Intake	Participants will be presented with a 20-item buffet spread that includes ultra-processed foods (high- and low-calorie), and minimally processed foods (high- and low-calorie). Participants first perform a taste test of 1g of each of the foods. They will then complete perceptual hedonic ratings of the pleasantness, sweetness, savoriness, and desire to consume on generalized labeled magnitude scales. After the taste test, participants will be told that they are free to eat as much as they like because the investigators have to discard the food after each participant. Participants will be alone during the 15-min tasting to minimize demand characteristics. Food will be pre- and post-weighed to determine ad lib intake. Total caloric intake will be calculated and translated to % of calorie needs.	Baseline

Collaborators and Investigators

• Sponsor: Stanford University
• Collaborators: University of North Carolina, Chapel Hill; Oregon Research Institute
• Investigators: Principal Investigator: Eric Stice, PhD, Stanford University

Publications and helpful links

General Publications

• Demos KE, Heatherton TF, Kelley WM. Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. J Neurosci. 2012 Apr 18;32(16):5549-52. doi: 10.1523/JNEUROSCI.5958-11.2012.
• Yokum S, Gearhardt AN, Harris JL, Brownell KD, Stice E. Individual differences in striatum activity to food commercials predict weight gain in adolescents. Obesity (Silver Spring). 2014 Dec;22(12):2544-51. doi: 10.1002/oby.20882. Epub 2014 Aug 25.
• Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, Flegal KM, Guo SS, Wei R, Mei Z, Curtin LR, Roche AF, Johnson CL. CDC growth charts: United States. Adv Data. 2000 Jun 8;(314):1-27.
• Stice E, Burger KS, Yokum S. Reward Region Responsivity Predicts Future Weight Gain and Moderating Effects of the TaqIA Allele. J Neurosci. 2015 Jul 15;35(28):10316-24. doi: 10.1523/JNEUROSCI.3607-14.2015.
• Joyner MA, Gearhardt AN, Flagel SB. A Translational Model to Assess Sign-Tracking and Goal-Tracking Behavior in Children. Neuropsychopharmacology. 2018 Jan;43(1):228-229. doi: 10.1038/npp.2017.196. No abstract available.
• Stice E, Yokum S, Rohde P, Cloud K, Desjardins CD. Comparing healthy adolescent females with and without parental history of eating pathology on neural responsivity to food and thin models and other potential risk factors. J Abnorm Psychol. 2021 Aug;130(6):608-619. doi: 10.1037/abn0000686.
• O'Doherty JP, Buchanan TW, Seymour B, Dolan RJ. Predictive neural coding of reward preference involves dissociable responses in human ventral midbrain and ventral striatum. Neuron. 2006 Jan 5;49(1):157-66. doi: 10.1016/j.neuron.2005.11.014.
• Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, Chung ST, Costa E, Courville A, Darcey V, Fletcher LA, Forde CG, Gharib AM, Guo J, Howard R, Joseph PV, McGehee S, Ouwerkerk R, Raisinger K, Rozga I, Stagliano M, Walter M, Walter PJ, Yang S, Zhou M. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019 Jul 2;30(1):67-77.e3. doi: 10.1016/j.cmet.2019.05.008. Epub 2019 May 16.

Helpful Links

• Nutrition Data System for Research (NDSR)

Study record dates

Study Major Dates

• Study Start (Actual): October 4, 2024
• Primary Completion (Estimated): February 1, 2029
• Study Completion (Estimated): February 28, 2029

Study Registration Dates

• First Submitted: December 1, 2023
• First Submitted That Met QC Criteria: December 1, 2023
• First Posted (Actual): December 12, 2023

Study Record Updates

• Last Update Posted (Actual): April 24, 2026
• Last Update Submitted That Met QC Criteria: April 21, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Other Study ID Numbers: 71541

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No