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

TL;DR

BBM tackles the challenge of estimating individualized brain network topography and connectivity from moderate fMRI data by recognizing substantial inter-individual variability. It introduces Bayesian brain mapping (BBM), a single-subject Bayesian framework that uses population-derived priors from templates to guide the estimation of spatial maps and FC, with the data modeled as ${y}_{tv} = \sum_{q=1}^{Q} a_{tq} s_{qv} + e_{tv}$. The approach provides two key contributions: flexible priors that permit network overlap and relaxed template constraints, and a novel permuted Cholesky prior for between-network FC that improves shrinkage and reliability. The authors supply the BayesBrainMap R package and HCP-derived priors, enabling practical deployment for biomarker discovery and clinical research.

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.

Figures (7)

• Figure 1: Overview of Bayesian Brain Mapping. The two main steps are (1) prior estimation and (2) model fitting, both implemented in the BayesBrainMap R package.
• Figure 2: HCP-derived spatial topography priors for Yeo17 template. Six exemplar networks are shown. While the parcels are constrained to have no overlap, the prior mean maps for each network show expansion beyond the parcel boundaries, indicating overlapping network engagement.
• Figure 3: HCP-derived spatial topography priors for a default mode network component from various templates. Templates include two parcellations (Yeo17 and MSC) and two types of network maps (ICA and PROFUMO) and are ordered from lowest to highest resolution (number of networks). The specific networks displayed are PROFUMO mode 8, GICA15 component 2, Yeo17 Deafult A, MSC Default, and GICA25 IC 2. Template maps are displayed on an arbitrary scale, while the scale of the priors is inversely proportional to the template resolution $Q$, given the additive nature of the BBM decomposition.
• Figure 4: Example of the functional connectivity (FC) prior in BBM, based on the Yeo17 template. The element-wise empirical mean and standard deviation (SD) within the training set used to establish the priors are show in the first column. The second and third columns show the element-wise mean and SD based on the Cholesky and the inverse Wishart choices of prior. The population mean is captured by both priors, but only the Cholesky prior captures the population variance patterns.
• Figure 5: Example individual BBM spatial engagement maps. A single HCP subject was analyzed using BBM with population-derived priors based on the Yeo17 template. The BBM posterior mean and standard deviation for a network corresponding to the Yeo17 DefaultA parcel are shown, along with areas of statistically significant engagement. Significance is based on the Bayesian equivalent of a hypothesis test with $\alpha = 0.05$ and Bonferroni correction. A range of effect size thresholds are specified based on $\sigma$, the standard deviation of the prior mean map. This is analogous to the common practice of thresholding group ICA maps at a certain number of standard deviations from the mean to isolate the main areas of engagement.