Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Neuroimage. 2018 Nov 1;181:16-29. doi: 10.1016/j.neuroimage.2018.06.019. Epub 2018 Jul 6.
This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.
这项工作提出了一种自动注释纤维簇（AAFC）方法，以实现从全脑追踪中识别具有解剖意义的白质结构。所提出的方法包括 1）使用成熟的基于数据的群组纤维聚类管道对整个大脑白质进行特定于研究的分割，将追踪分割成多个纤维簇，以及 2）一种新的簇注释方法，通过跨多个主体的皮质分割信息自动为每个纤维簇分配解剖学轨迹注释。AAFC 方法的新颖之处在于，它利用了关于纤维簇的群组信息，包括它们的纤维几何形状和皮质终止，以自动为每个簇计算轨迹解剖标签。我们在一项关于重度抑郁症（MDD）中情绪处理和感觉运动区域白质异常的研究中展示了所提出的 AAFC 方法。使用所提出的 AAFC 方法以及单独使用皮质分割的可比方法，自动识别了与情绪处理和感觉运动功能相关的七个感兴趣的轨迹。实验结果表明，我们提出的方法在跨主体和跨半球识别纤维方面更一致，就纤维数量而言。此外，我们使用 AAFC 方法对 31 名 MDD 患者和 62 名健康受试者的识别轨迹进行了组间统计分析。我们在纤维束内的局部区域发现了扩散测量的统计学差异（例如，在识别的左半球扣带束内的 4 个纤维簇（由 14 个簇组成）在两组之间存在显著差异），这表明我们的方法能够识别与白质结构细分相关的潜在异常。
