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.

翻译：大脑功能组织的空间拓扑结构在认知与疾病中的作用日益受到重视。考虑功能拓扑的个体差异对于准确区分大脑组织的空间与时间维度也至关重要。然而，由于功能磁共振成像（fMRI）信噪比较低，在无需大量扫描的情况下精确估计个体功能脑网络仍具挑战性。本文提出贝叶斯脑图谱绘制（BBM）技术，该技术利用群体信息实现个体功能拓扑与连接分析。该方法基于现有空间模板（如脑区划分或连续网络图谱）构建群体衍生的空间拓扑与功能连接先验，以指导个体水平估计并抑制噪声干扰。BBM具有高度灵活性，避免强空间或时间约束，允许网络间存在重叠及异质性参与模式。与多主体分层模型不同，BBM专为单主体分析设计，具有极高的计算效率，可适用于临床场景。本文详细阐述BBM模型，并通过BayesBrainMap R软件包演示如何构建群体衍生先验、拟合模型及执行参与模式推断。相关演示代码已发布于配套Github仓库。我们同时公开基于人类连接组计划数据库衍生的先验模板，并提供支持从不同数据源构建先验的代码，以降低BBM在个体脑组织研究中的应用门槛。