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type project
date 2025-06-15
title The Transition from Physical to Abstract Taste: An fMRI Study on Sweet, Sour, and Salty
names
Tran Cao Bang Trinh
github_repo https://github.com/annamarie1509/Annamarie_BHS_2025_project
website
tags
fmri
cognition
taste
visual stimuli
summary This fMRI study compares two datasets: (1) taste imagery and food pictures, and (2) taste mapping (actual tastes). It examines shared neural patterns for sweet, salty, and sour across these conditions, testing whether imagined and perceived tastes activate overlapping brain regions. Visual inspections of fMRI contrasts aim to reveal universal taste representations, bridging abstract (imagery/pictures) and physical (direct tasting) processing [website](https://github.com/annamarie1509/Annamarie_BHS_2025_project).
image

Project definition

Background

This fMRI study investigates how the brain processes both physical and abstract taste experiences. While we readily perceive tastes like sweet and sour, we also use taste metaphors in language (e.g., "sweet person"). Embodiment theory suggests these abstract concepts might share neural mechanisms with actual taste perception.

We analyze two datasets:

  1. Abstract Taste:
    • task-tasteimagine: Participants imagined sweet/sour/salty tastes
    • task-foodpicture: Viewed food images representing these tastes
  2. Physical Taste:
    • task-tastemapping: Direct taste stimulation (from prior study)

Key questions:

  • Do imagined/viewed tastes activate similar regions as physical tastes?
  • Can we identify consistent patterns despite individual variability in taste maps?

Preliminary findings suggest partial overlaps in gustatory areas, though food images also engage object recognition networks. This project systematically compares these conditions to test embodiment in taste processing.

Tools

This project uses SPM12 (Statistical Parametric Mapping) for fMRI data preprocessing and analysis:

  • SPM12 Documentation:
    📖 Official Manual
    ▶️ Tutorial Playlist

  • Key Functions:

    spm('FMRI');        % Initialize fMRI pipeline  
spm_preproc();      % Preprocessing (realign, coregister, normalize)  
spm_stats();        % Statistical analysis (GLM, contrasts)

Data

This project analyzes two fMRI datasets:

Study 1: Abstract Taste Processing

Study 2: Physical Taste Processing

Matched Participant Pair:

Dataset 1 (sub-007) Dataset 2 (sub-072)
Sex F F
Age 24 26 (+2 yrs)
Weight 68 kg 65.5 kg (-2.5 kg)

Selected for minimal demographic variance in gender/age/weight

Deliverables

At the end of this project, we will have:

  • The current markdown document, completed and revised.
  • A gallery of the student projects at Brainhack 2020.
  • Instructions on the website about how to submit a pull request to the brainhack school website in order to add the project description to the website.

Results

Progress overview

The project was swiftly initiated by P Bellec, based on the existing template created in 2019 by Tristan Glatard and improved by different students. It was really not that hard. Community feedback is expected to lead to rapid further improvements of this first version.

Tools I learned during this project

  • Meta-project P Bellec learned how to do a meta project for the first time, which is developping a framework while using it at the same time. It felt really weird, but somehow quite fun as well.
  • Github workflow- The successful use of this template approach will demonstrate that it is possible to incorporate dozens of students presentation on a website collaboratively over a few weeks.
  • Project content Through the project reports generated using the template, it is possible to learn about what exactly the brainhack school students are working on.

Results

Deliverable 1: Code Repository

Preprocessing Pipeline Implementation (MATLAB/SPM12)

Processing Step Study 1 Status Study 2 Status
Check Reg (Motion Correction) ✅ Completed ✅ Completed
Realign & Reslice ✅ Completed ✅ Completed
Slice Timing ✅ Completed ✅ Completed
Coregister T1 EPI ❌ Pending ✅ Completed
Segmentation ❌ Pending ✅ Completed

Included Files:

  • Configuration .mat files.
  • Experimental conditions:
    • 8 runs * 1 trials condition files for Study 2.

Deliverable 2: Quality Control Visualizations

Study 1 Outputs:

  • Motion correction results

Study 2 Outputs:

  • Motion correction results
  • Structural-functional coregistration
  • GLM model specification

Deliverable 3: First-Level Analysis Specifications

Model Configuration:

  • 1stlevel/sub-007/results/SPM.mat/ - Complete first-level model specification

Condition Files:

  • Trial 1: 1stlevel/sub-007/results/Trial1
  • Trial 2: 1stlevel/sub-007/results/mat_conditions

Conclusion and Acknowledgments

Project Status

This remains an ongoing project with current progress including:

  • Completed preprocessing pipelines for both datasets
  • Quality control visualizations
  • First-level model specifications

Future Directions

Following valuable feedback from Brainhack School 2025, next steps will focus on:

  1. Advancing analysis:
    • 1st → 2nd level analysis
    • Statistical comparison across studies
  2. Moving beyond visual inspection to:
    • Quantitative pattern analysis
    • Cross-dataset multivariate approaches
  3. Ultimate goal:
    • Demonstrate universal neural patterns in taste perception
    • Bridge abstract and physical taste representations

Acknowledgments

Special thanks to:

  • The BHS 2025 instructors and participants for their:
    • Technical guidance on preprocessing
    • Innovative analysis suggestions
    • Encouragement to push beyond initial scope
  • My BHS 2025 colleagues for their:
    • Conceptual feedback
    • Dataset recommendations
  • OpenNeuro for making the datasets publicly available

"This project was developed as part of Brainhack School 2025"