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.

翻译：功能脑组织的空间拓扑结构日益被认为在认知和疾病中发挥重要作用。考虑功能拓扑的个体差异对于准确区分功能脑连接的空间和时间方面也至关重要。然而，由于高噪声水平，在没有广泛扫描的情况下，从功能磁共振成像数据中准确估计个性化功能脑网络仍然具有挑战性。本文提出贝叶斯脑图谱方法，这是一种受群体信息驱动的个性化功能拓扑与连接分析技术。BBM利用基于群体推导的网络空间拓扑先验知识和网络间功能连接先验知识，指导个体水平估计并抑制噪声影响。这些先验基于现有空间模板（如脑区分割或连续网络图谱），确保与模板的对应关系。但BBM具有高度灵活性，避免了强空间或时间约束，允许网络重叠和异质性参与模式。该方法专为单被试分析设计，计算高效且适用于临床场景。本文阐述了BBM模型框架，并演示了如何使用BayesBrainMap R包构建群体先验、拟合模型以及进行参与模式推断。配套Github仓库提供了演示案例。我们还共享了基于人类连接组项目推导的先验知识，并提供了支持从不同数据源构建先验的代码，从而降低BBM用于个体脑组织研究的应用门槛。