Poster No:
710
Submission Type:
Abstract Submission
Authors:
Ruiqi FU1, Yan Yuan1, Werner Sommer2, Anoushiravan Zahedi3, Changsong Zhou1, Liang Tian1
Institutions:
1Hong Kong Baptist University, Hong Kong, Hong Kong, 2Humboldt-Universität zu Berlin, Berlin, Berlin, 3University of Münster, Münster, Münster
First Author:
Ruiqi FU
Hong Kong Baptist University
Hong Kong, Hong Kong
Co-Author(s):
Yan Yuan
Hong Kong Baptist University
Hong Kong, Hong Kong
Liang Tian
Hong Kong Baptist University
Hong Kong, Hong Kong
Introduction:
Obesity, global warming, and biodiversity loss are linked to overconsumption of foods like red meat, ultra-processed foods, and sugar. To address these issues, it's crucial to understand and shift food preferences towards healthier, sustainable options. The gut microbiota, consisting of trillions of microbes, plays a key role in health and may influence food choices, although human evidence is limited.
The gut microbiome is divided into enterotypes-Bacteroides (ENT-B), Prevotella (ENT-P), and Ruminococcus-based on diet, impacting microbial diversity and health (Arumugam, 2011). Diets high in protein increase Bacteroides, high-fat diets raise ENT-B and lower Firmicutes, while high-fiber diets boost ENT-P. The brain-gut axis suggests microbiota could affect food preferences by modulating brain reward systems (Singh, 2017).
Food preferences, shaped early and influenced by environmental factors, affect diet quality and health. These preferences can change over time, guided by neural responses to food cues (Hebden, 2015). This study uses EEG and Multi-echo fMRI (ME-fMRI) to explore brain activity related to food preferences, focusing on neural indicators like the late positive potential (LPP) and P1 component. These measures help reveal brain systems involved in food evaluation (Hickey, 2010; Schacht, 2012; Zahedi, 2020). Activation in brain reward regions, such as the orbitofrontal cortex and ventral striatum, in response to food cues, can increase the motivation to consume food, contributing to overeating and obesity. This study aims to explore the relationship between gut enterotype, brain function, and food preferences to develop interventions for obesity prevention and treatment.
The research addresses three key questions:
1. The relationship between food consumption over two weeks and gut enterotypes;
2. The relationship between gut enterotypes and self-reported food preferences;
3. The relationship between two specific gut enterotypes (ENT-B and ENT-P) and neural indicators (measured by EEG and fMRI) of food preferences.
Methods:
This study involves 300 healthy volunteers aged 20-40 with normal BMI (20-25) and no chronic health issues. Participants will track their food consumption for two weeks via a smartphone app, rate their food preferences online, and provide fecal samples for enterotyping. Key steps include:
1. Enterotyping: Categorizing gut microbiota into enterotypes ENT-B and ENT-P.
2. Food Preference Questionnaire: Rating 170 food items across six categories.
3. Food Consumption Tracking: Recording actual consumption using a smartphone app over two weeks.
4. Neuroimaging Subsamples: Selecting 60 participants from each enterotype, matched for age, sex, and education, to undergo ME-fMRI and EEG recordings using an eat/not-eat decision task.
The sample size, determined via G*Power 3.1, aims for 60 participants per group, with 140 recruited to account for dropouts. We expect 80% valid data post-exclusions.
In the EEG/fMRI experiments, we will measure:
1. LPP Amplitude: To assess motivated attention to food pictures.
2. P1 Amplitude: A visual evoked potential sensitive to reward-related stimuli.
3. Choice Region Activity: Brain activity in regions like the medial prefrontal cortex and ventral striatum during food choices.
4. Preference-Related Activity: Activity in the ventromedial prefrontal and orbitofrontal cortex in response to subjective food preferences.
Results:
The EEG provides timing information, while ME-fMRI will reveal specific brain systems involved. Using representational similarity analysis on isomorphic tasks, we determine when and where differences occur in the neurocognitive processing of food preferences based on enterotype.
Conclusions:
This study examines the link between individual food consumption habits, gut microbiome and individual food preferences as measured by subjective ratings and brain activity in healthy participants.
Higher Cognitive Functions:
Decision Making 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
EEG/MEG Modeling and Analysis
Multivariate Approaches 2
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Keywords:
Data analysis
Electroencephaolography (EEG)
FUNCTIONAL MRI
Multivariate
Other - decision making
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
EEG/ERP
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
SPM
FSL
Free Surfer
Provide references using APA citation style.
Arumugam M. (2011). Enterotypes of the human gut microbiome. Nature, 473 (7346), 174-180.
Singh RK. (2017). Influence of diet on the gut microbiome and implications for human health. Journal of Translational Medicine, 15(1), 1-7.
Hebden L. (2015). You are what you choose to eat: Factors influencing young adults' food selection behaviour. Journal of Human Nutrition and Dietetics, 28(4), 401-408.
Hickey C. (2010). Reward changes salience in human vision via the anterior cingulate. Journal of Neuroscience, 30(33), 11096-11103.
Schacht A. (2012). Association with positive outcome induces early effects in event-related brain potentials. Biological Psychology, 89(1), 130-136.
Zahedi A. (2020). Modification of food preferences by posthypnotic suggestions: An event-related brain potential study. Appetite, 151, 104713.
No