Searchlight Optimization Using Representational Similarity Analysis for Subject-Level Voxel Selection in Emotional State Decoding

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

ABSTRACT Identifying informative voxels is a critical, yet challenging step in functional magnetic resonance imaging (fMRI), particularly for multivariate analyses involving multiple related conditions. Existing approaches often rely on predefined regions of interest (ROIs) or activation-based criteria, which may be insufficient for capturing fine-grained representational differences. This challenge becomes particularly relevant in experimental settings and interventions such as neurofeedback training, where voxels are not only measured as neural responses but also used as targets for intervention based on their previously observed activity patterns. In this study, we propose a subject-level searchlight optimization framework that integrates voxel-wise general linear model (GLM)–based univariate analysis with representational similarity analysis (RSA)–based multivariate refinement to identify voxels that are both task-relevant and condition-sensitive. To enhance practical applicability, the framework further incorporates a data-driven hyperparameter tuning step based on Bayesian optimization, enabling efficient identification of high-performing configurations from small pilot datasets, with consistent performance when applied to larger samples. The proposed framework was evaluated using an emotion imagery fMRI dataset with four affective conditions. Results demonstrate that the multivariate refinement improves alignment between empirical and target representational structures compared with univariate selection alone. Compared with a classifier-based voxel selection approach, the RSA-based approach better preserves the representational geometry of emotional states while maintaining discriminative capacity. These findings highlight the effectiveness, efficiency, and robustness of the proposed RSA framework, providing a practical solution for identifying condition-sensitive voxels and supporting more precise multivariate investigation of affective brain states in multi-condition fMRI studies.

Authors 9

1. Xuelei Wang Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Medical School

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany

2. Jana Zweerings Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Medical School

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany

3. Michael Lührs

   Maastricht University · Brain Innovation (Netherlands)

   Affiliation as printed

   Brain Innovation B.V., Maastricht, The Netherlands

   Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands

4. Fengyu Cong

   Dalian University of Technology · Dalian University · University of Jyväskylä

   Affiliation as printed

   Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland

   Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education, China

   School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, China

   School of Software Engineering, Dalian University, Dalian, China

5. Klaus Mathiak Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Medical School JARA-Translational Brain Medicine

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany

   JARA-Translational Brain Medicine, RWTH Aachen University, Aachen, Germany

6. David E.J. Linden

   Maastricht University

   Affiliation as printed

   Mental Health and Neuroscience Research Institute, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands

7. Rainer Goebel

   Maastricht University · Brain Innovation (Netherlands)

   Affiliation as printed

   Brain Innovation B.V., Maastricht, The Netherlands

   Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands

8. Assunta Ciarlo

   Maastricht University · Brain Innovation (Netherlands)

   Affiliation as printed

   Brain Innovation B.V., Maastricht, The Netherlands

   Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands

9. David M. A. Mehler corresponding Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Medical School

   University of Münster · Cardiff University · RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany

   Institute of Translational Psychiatry, Medical Faculty, University of Münster, Münster, Germany

   School of Psychology, Cardiff University Brain Research Imaging Center (CUBRIC), Cardiff University, Cardiff, U.K