Surface-based cortical registration is an important topic in medical image analysis and facilitates many downstream applications. Current approaches for cortical registration are mainly driven by geometric features, such as sulcal depth and curvature, and often assume that registration of folding patterns leads to alignment of brain function. However, functional variability of anatomically corresponding areas across subjects has been widely reported, particularly in higher-order cognitive areas. In this work, we present JOSA, a novel cortical registration framework that jointly models the mismatch between geometry and function while simultaneously learning an unbiased population-specific atlas. Using a semi-supervised training strategy, JOSA achieves superior registration performance in both geometry and function to the state-of-the-art methods but without requiring functional data at inference. This learning framework can be extended to any auxiliary data to guide spherical registration that is available during training but is difficult or impossible to obtain during inference, such as parcellations, architectonic identity, transcriptomic information, and molecular profiles. By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of brain structure and function.

中文翻译：

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JOSA：基于表面的联合配准和大脑几何和功能的图谱构建


基于表面的皮质配准是医学图像分析中的一个重要主题，并促进了许多下游应用。当前的皮质配准方法主要由几何特征驱动，例如脑沟深度和曲率，并且通常假设折叠模式的配准导致大脑功能的对齐。然而，不同受试者的解剖学相应区域的功能变异已被广泛报道，特别是在高阶认知区域。在这项工作中，我们提出了 JOSA，一种新颖的皮质配准框架，它联合模拟几何和功能之间的不匹配，同时学习无偏见的特定人群图谱。使用半监督训练策略，JOSA 在几何和功能方面实现了优于最先进方法的配准性能，但在推理时不需要功能数据。该学习框架可以扩展到任何辅助数据，以指导在训练期间可用但在推理过程中很难或不可能获得的球形配准，例如分区、架构身份、转录组信息和分子概况。通过认识到几何形状和功能之间的不匹配，JOSA 为利用大脑结构和功能联合分析的配准方法的未来发展提供了新的见解。