A Multi-Atlas Labeling Approach for Identifying Subject-Specific Functional Regions of Interest

The functional region of interest (fROI) approach has increasingly become a favored methodology in functional magnetic resonance imaging (fMRI) because it can circumvent inter-subject anatomical and functional variability, and thus increase the sensitivity and functional resolution of fMRI analyses. The standard fROI method requires human experts to meticulously examine and identify subject-specific fROIs within activation clusters. This process is time-consuming and heavily dependent on experts’ knowledge. Several algorithmic approaches have been proposed for identifying subject-specific fROIs; however, these approaches cannot easily incorporate prior knowledge of inter-subject variability. In the present study, we improved the multi-atlas labeling approach for defining subject-specific fROIs. In particular, we used a classifier-based atlas-encoding scheme and an atlas selection procedure to account for the large spatial variability across subjects. Using a functional atlas database for face recognition, we showed that with these two features, our approach efficiently circumvented inter-subject anatomical and functional variability and thus improved labeling accuracy. Moreover, in comparison with a single-atlas approach, our multi-atlas labeling approach showed better performance in identifying subject-specific fROIs.

感兴趣功能区（functional region of interest, fROI）分析方法在功能磁共振成像（functional magnetic resonance imaging, fMRI）领域已日益成为主流研究方法，因其可规避受试者间解剖结构与功能活动的异质性，进而提升fMRI分析的灵敏度与功能分辨率。标准fROI方法需要领域专家细致检视激活簇并识别个体特异性fROI，该过程耗时冗长且高度依赖专家的专业知识。目前已有多种算法方法被提出用于识别个体特异性fROI，但此类方法难以整合跨受试者异质性的先验知识。本研究针对个体特异性fROI的定义方法，改进了多图谱标注策略：具体采用基于分类器的图谱编码方案与图谱选择流程，以应对受试者间显著的空间变异。我们使用人脸识别功能图谱数据库开展实验，结果表明，结合上述两项改进，本方法可有效规避受试者间解剖与功能异质性，进而提升标注准确率。此外，相较于单图谱标注方法，本研究提出的多图谱标注方法在识别个体特异性fROI方面展现出更优性能。