Cheng Hewei, Wu Hong, Fan Yong
Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
J Neurosci Methods. 2014 Nov 30;237:90-102. doi: 10.1016/j.jneumeth.2014.09.004. Epub 2014 Sep 16.
Parcellating brain structures into functionally homogeneous subregions based on resting state fMRI data could be achieved by grouping image voxels using clustering algorithms, such as normalized cut. The affinity between brain voxels adopted in the clustering algorithms is typically characterized by a combination of the similarity of their functional signals and their spatial distance with parameters empirically specified. However, improper parameter setting of the affinity measure may result in parcellation results biased to spatial smoothness.
To obtain a functionally homogeneous and spatially contiguous brain parcellation result, we propose to optimize the affinity measure of image voxels using a constrained bi-level programming optimization method. Particularly, we first identify the space of all possible parameters that are able to generate spatially contiguous brain parcellation results. Then, within the constrained parameter space we search those leading to the brain parcellation results with optimal functional homogeneity and spatial smoothness.
The method has successfully parcellated medial superior frontal cortex into supplementary motor area (SMA) and pre-SMA for 106 subjects based on their resting state fMRI data. These results have been validated through functional connectivity analysis and meta-analysis of existing functional imaging studies and compared with those obtained by state-of-the-art brain parcellation methods.
The validation results have demonstrated that our method could obtain brain parcellation results consistent with the existing functional anatomy knowledge, and the comparison results have further demonstrated that optimizing affinity measure could improve the brain parcellation's robustness and functional homogeneity.
基于静息态功能磁共振成像（fMRI）数据将脑结构划分为功能上均匀的子区域，可以通过使用聚类算法（如归一化割）对图像体素进行分组来实现。聚类算法中采用的脑体素之间的亲和力通常由其功能信号的相似性及其空间距离的组合来表征，参数通过经验指定。然而，亲和力度量的参数设置不当可能导致分割结果偏向于空间平滑性。
为了获得功能上均匀且空间上连续的脑分割结果，我们提出使用约束双层规划优化方法来优化图像体素的亲和力度量。具体而言，我们首先确定所有能够生成空间上连续的脑分割结果的可能参数的空间。然后，在约束参数空间内，我们搜索那些导致具有最佳功能均匀性和空间平滑性的脑分割结果。
该方法已基于106名受试者的静息态fMRI数据成功将内侧额上回分割为辅助运动区（SMA）和前辅助运动区（pre-SMA）。这些结果已通过功能连接性分析和现有功能成像研究的荟萃分析得到验证，并与通过最先进的脑分割方法获得的结果进行了比较。
验证结果表明我们的方法能够获得与现有功能解剖学知识一致的脑分割结果，比较结果进一步表明优化亲和力度量可以提高脑分割的鲁棒性和功能均匀性。
