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A Feasibility Study of Navigating Emotional States Using Real-Time Representational Similarity Analysis fMRI
Xuelei Wang1, Assunta Ciarlo2,3, Michael Lührs2,3
1Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School RWTH Aachen University, Aachen, Germany.
International Journal of Neural Systems
|March 16, 2026
Summary
This study introduces real-time fMRI semantic neurofeedback (rt-fMRI-sNF) using representational similarity analysis (RSA) to help people regulate emotions. This novel brain computer interface (BCI) approach successfully guided brain activity patterns for distinct emotional states.
Area of Science:
- Neuroscience
- Cognitive Science
- Brain Computer Interfaces
Background:
- Real-time functional magnetic resonance imaging neurofeedback (rt-fMRI-NF) offers a noninvasive method for self-regulating affective brain states via brain computer interfaces (BCIs).
- Traditional univariate rt-fMRI-NF methods face challenges in differentiating the complex, distributed neural patterns associated with various emotions.
Purpose of the Study:
- To develop and assess a novel rt-fMRI semantic neurofeedback (rt-fMRI-sNF) paradigm.
- To enable participants to navigate between distinct emotional states using real-time representational similarity analysis (rt-RSA).
Main Methods:
- Implemented an rt-fMRI-sNF system utilizing rt-RSA to distinguish emotion-specific neural patterns.
- Derived four emotion-based neural patterns from functional localizer runs and employed them as neurofeedback targets.
- Utilized a circular semantic map (CSM) for real-time visual feedback on brain activity similarity and intensity relative to target patterns.
- Instructed participants to employ mental imagery to modulate their brain activity towards specific target patterns.
Main Results:
- Representational similarity analysis (RSA) effectively differentiated between emotional states, despite overlapping regional activations.
- Neurofeedback performance showed significant within-run improvements across participants.
- Participants demonstrated higher initial performance in the second neurofeedback run compared to the first.
Conclusions:
- The study validates the methodological feasibility of an RSA-informed rt-fMRI-NF framework for modulating multivariate brain states.
- This approach provides a foundation for future research into the transferability and clinical applications of emotion regulation through neurofeedback.
- rt-fMRI-sNF represents a significant advancement in BCI technology for targeted affective brain state modulation.
