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

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.

💡 Research Summary

This paper introduces Bayesian Brain Mapping (BBM), a novel framework for estimating individual‑level functional topography and functional connectivity (FC) from relatively short resting‑state fMRI scans by leveraging population‑derived priors. The authors begin by emphasizing that individual differences in the spatial configuration of brain networks are predictive of behavior, cognition, and disease, yet traditional approaches require long scanning sessions to obtain reliable maps. Moreover, applying group‑level parcellations or ICA components directly to single subjects can mix signals from adjacent but distinct functional areas, biasing FC estimates and obscuring true spatial versus temporal effects.

BBM addresses these challenges by separating the prior‑learning stage from the subject‑specific inference stage. First, a large, high‑quality dataset (the Human Connectome Project, filtered to ~350 subjects with two sessions each) is used to construct priors for a chosen template. Templates can be hard parcellations (e.g., Yeo 17‑network, MSC), group ICA maps (15–50 components), or PROFUMO modes, and can be generated with or without global signal regression (GSR). For each template, dual‑regression is applied to the training data to obtain noisy spatial maps and time courses; the median within parcels is used instead of the mean to reduce bias from mis‑alignment. The resulting population mean (s₀) and variance (σ²) for each network at each voxel constitute the spatial prior, while a separate prior on the between‑network covariance (G) captures typical FC structure. Two options are offered for the FC prior: the conjugate inverse‑Wishart distribution (fast) and a newly proposed Cholesky‑factor‑based prior (more expressive of variance patterns but computationally heavier).

The subject‑level model assumes the observed BOLD signal yₜᵥ can be expressed as a linear combination of Q network time courses aₜ and spatial engagement maps sᵥ plus voxel‑specific Gaussian noise: yₜᵥ = aₜᵀ sᵥ + εₜᵥ, εₜᵥ ~ N(0, τ²ᵥ). Spatial engagement for network q at voxel v follows s_{qv} = s_{0,qv} + δ_{qv}, with δ_{qv} ~ N(0, σ²_{qv}) representing individual deviation. The time courses aₜ have a multivariate normal prior N(0, G), where G itself follows the chosen hyper‑prior. This hierarchical Bayesian formulation allows the model to shrink noisy background regions toward the population mean while preserving genuine individual differences in regions where the population variance is low. Importantly, the model imposes no hard spatial independence or temporal independence constraints, permitting overlapping networks and heterogeneous activation patterns.

Implementation is provided through the open‑source R package BayesBrainMap. The workflow consists of (1) estimate_prior() to build spatial and FC priors from training data, (2) BrainMap() to fit the single‑subject model via MCMC sampling, and (3) engagements() to extract posterior mean engagement maps and connectivity estimates. The authors release the HCP‑derived priors on OSF, enabling immediate application to healthy young adults, and supply scripts for generating custom priors for other populations.

Although detailed quantitative results are not reproduced in the excerpt, the authors report that BBM yields more reliable individual network maps and FC estimates than standard ICA, PROFUMO, or hard‑parcellation approaches, especially when only ~10 minutes of data are available. The method demonstrates robustness across templates and GSR choices, and its single‑subject inference is computationally efficient compared with full hierarchical models, making it attractive for clinical settings where scan time and computational resources are limited.

Limitations include the dependence on a large, high‑quality dataset for prior construction, potential challenges when applying HCP‑derived priors to markedly different cohorts (e.g., children, elderly, patients), and the need for careful MCMC convergence diagnostics, particularly when using the more expressive Cholesky FC prior. Future work may explore transfer‑learning strategies to adapt priors across populations, lightweight variational approximations for faster inference, and integration with real‑time denoising pipelines.

In summary, BBM offers a pragmatic yet flexible Bayesian solution for individualized functional brain mapping. By embedding population variability directly into spatial and connectivity priors and performing inference at the single‑subject level, it reduces noise, respects overlapping network organization, and dramatically lowers computational demands, representing a significant advance for both basic neuroscience research and translational neuroimaging applications.