Bayesian brain mapping: A population-informed framework for personalized functional network topography and connectivity.
- 1Department of Statistics, Indiana University, Bloomington, IN, United States.
- 2Department of Psychology, Northwestern University, Evanston, IL, United States.
- 3Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, United States.
Abstract
The spatial topography of functional brain 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 functional brain connectivity. Yet, accurate estimation of personalized functional brain networks from functional magnetic resonance imaging (fMRI) without extensive scanning remains challenging due to high noise levels. Here, we describe Bayesian Brain Mapping (BBM), a technique for personalized functional topography and connectivity informed by population information. BBM relies on population-derived priors on both spatial topography of networks and between-network functional connectivity to guide subject-level estimation and combat noise. These priors are based on existing spatial templates, such as parcellations or continuous network maps, providing correspondence to those templates. Yet BBM is highly flexible, avoiding strong spatial or temporal constraints and allowing for overlap between networks and heterogeneous patterns of engagement. BBM is designed for single-subject analysis, making it 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 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.
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BibTeX
@article{derman2026bayesian,
title = {{Bayesian brain mapping: A population-informed framework for personalized functional network topography and connectivity.}},
author = {Diego Derman and Nohelia Da Silva Sanchez and Saige Rutherford and Damon D Pham and Ellyn R Butler and Mary Beth Nebel and Amanda F Mejia},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = oct,
doi = {10.1162/imag.a.1411},
url = {https://europepmc.org/article/MED/42953563},
note = {CC BY},
}
RIS
TY - JOUR
TI - Bayesian brain mapping: A population-informed framework for personalized functional network topography and connectivity.
AU - Diego Derman
AU - Nohelia Da Silva Sanchez
AU - Saige Rutherford
AU - Damon D Pham
AU - Ellyn R Butler
AU - Mary Beth Nebel
AU - Amanda F Mejia
PY - 2026
DA - 2026/10/27
JO - Imaging neuroscience (Cambridge, Mass.)
DO - 10.1162/imag.a.1411
UR - https://europepmc.org/article/MED/42953563
AB - The spatial topography of functional brain 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 functional brain connectivity. Yet, accurate estimation of personalized functional brain networks from functional magnetic resonance imaging (fMRI) without extensive scanning remains challenging due to high noise levels. Here, we describe Bayesian Brain Mapping (BBM), a technique for personalized functional topography and connectivity informed by population information. BBM relies on population-derived priors on both spatial topography of networks and between-network functional connectivity to guide subject-level estimation and combat noise. These priors are based on existing spatial templates, such as parcellations or continuous network maps, providing correspondence to those templates. Yet BBM is highly flexible, avoiding strong spatial or temporal constraints and allowing for overlap between networks and heterogeneous patterns of engagement. BBM is designed for single-subject analysis, making it 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 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.
SN - 2837-6056
ER -