Chen Hanbo, Zhang Tuo, Li Kaiming, Hu Xintao, Guo Lei, Liui Tianming
Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):318-26. doi: 10.1007/978-3-642-23629-7_39.
Cortical folding patterns are believed to be good predictors of brain cytoarchitecture and function. For instance, neuroscientists frequently apply their domain knowledge to identify brain Regions of Interests (ROIs) based on cortical folding patterns. However, quantitative mapping of cortical folding pattern and brain function has not been established yet in the literature. This paper presents our initial effort in quantification of the regularity and variability of cortical folding pattern features for working memory ROIs identified by task-based fMRI, which is widely accepted as a standard approach to localize functionally-specialized brain regions. Specifically, we used a set of shape attributes for each ROI base on multiple resolution decomposition of cortical surfaces, and described the meso-scale folding pattern via a polynomial-based approach. We also applied brain atlas label distribution as a global-scale description of ROI folding pattern. Our studies suggest that there is deep-rooted regularity of cortical folding patterns for certain working memory ROIs across subjects, and folding pattern attributes could be useful for the characterization, recognition and prediction of ROIs, if extracted and applied in a proper way.
皮质折叠模式被认为是脑神经元结构和功能的良好预测指标。例如，神经科学家经常运用他们的领域知识，基于皮质折叠模式来识别脑兴趣区（ROI）。然而，皮质折叠模式与脑功能的定量映射在文献中尚未确立。本文展示了我们在量化基于任务功能磁共振成像（fMRI）识别出的工作记忆ROI的皮质折叠模式特征的规律性和变异性方面的初步努力，任务功能磁共振成像被广泛认为是定位功能特化脑区的标准方法。具体而言，我们基于皮质表面的多分辨率分解为每个ROI使用了一组形状属性，并通过基于多项式的方法描述中尺度折叠模式。我们还应用脑图谱标签分布作为ROI折叠模式的全局尺度描述。我们的研究表明，跨个体的某些工作记忆ROI的皮质折叠模式存在根深蒂固的规律性，并且如果以适当的方式提取和应用，折叠模式属性可能有助于ROI的表征、识别和预测。
