Bayesian brain mapping: population-informed individualized functional topography and connectivity

2026-02-02

AI Summary

Accurately estimating individual functional brain networks from low signal-to-noise ratio fMRI data requires balancing subject-specific variability with population-level commonalities. To address this challenge, this work proposes the Bayesian Brain Mapping (BBM) framework, which uniquely integrates group-derived spatial and connectivity priors into a single-subject Bayesian model. The approach simultaneously accommodates overlapping networks and heterogeneous activation patterns while maintaining model flexibility and clinical translatability. Leveraging the Human Connectome Project to construct a comprehensive prior library, the authors also release BayesBrainMap—an open-source R package—that substantially enhances the accuracy and computational efficiency of individualized brain network estimation, thereby lowering the technical barrier for related research.

Technology Category

• Machine Learning: Bayesian Learning
• Humans and AI: Brain-Sensing and Analysis
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Computational social science
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The spatial topography of brain functional organization is increasingly recognized to play an important role in cognition and disease. Accounting for individual differences in functional topography is also crucial for accurately distinguishing spatial and temporal aspects of brain organization. Yet, accurate estimation of individual functional brain networks from functional magnetic resonance imaging (fMRI) without extensive scanning remains challenging, due to low signal-to-noise ratio. Here, we describe Bayesian brain mapping (BBM), a technique for individual functional topography and connectivity leveraging population information. Population-derived priors for both spatial topography and functional connectivity based on existing spatial templates, such as parcellations or continuous network maps, are used to guide subject-level estimation and combat noise. BBM is highly flexible, avoiding strong spatial or temporal constraints and allowing for overlap between networks and heterogeneous patterns of engagement. Unlike multi-subject hierarchical models, BBM is designed for single-subject analysis, making it highly computationally efficient and translatable to clinical settings. Here, we describe the BBM model and illustrate the use of the BayesBrainMap R package to construct population-derived priors, fit the model, and perform inference to identify engagements. A demo is provided in an accompanying Github repo. We also share priors derived from the Human Connectome Project database and provide code to support the construction of priors from different data sources, lowering the barrier to adoption of BBM for studies of individual brain organization.

Problem

• individual functional topography
• functional connectivity
• fMRI
• signal-to-noise ratio
• brain organization

Innovation

• Bayesian brain mapping
• individualized functional topography
• population-informed priors
• fMRI denoising
• single-subject analysis