Predicting Functional Cortical ROIs via DTI-Derived Fiber Shape Models
Predicting Functional Cortical ROIs via DTI-Derived Fiber Shape Models
复制标题
通过 DTI 衍生的纤维形状模型预测功能性皮质 ROI
DOI：
10.1093/cercor/bhr152
复制
发表时间：
2012-04-01
期刊：
影响因子：
3.7
通讯作者：
Liu, Tianming
中科院分区：
文献类型：
--
作者：
Zhang, Tuo;Guo, Lei;Liu, Tianming
近年来，对人类大脑皮层结构和功能连接的研究引起了人们极大的兴趣和努力。当试图测量特定皮层网络的结构和/或功能连接时，一个基本的和具有挑战性的问题出现了：如何识别和定位皮层上可能的最佳兴趣区域（roi） ？在我们看来，主要的挑战来自ROI边界定义的不确定性，个体之间显著的结构和功能可变性以及ROI内部和周围的高度非线性。在本文中，我们提出了一种新的ROI预测框架，该框架基于基于多模态任务的功能磁共振成像（fMRI）和扩散张量成像（DTI）数据的学习纤维形状模型来定位个体大脑中的ROI。在训练阶段，白质纤维的形状模型是从功能性roi发出的模型中学习的，功能性roi是基于任务的fMRI数据检测到的激活的大脑区域。在预测阶段，仅根据DTI数据预测个体大脑的功能roi。实验结果表明，与工作记忆和基于视觉任务的fMRI提供的基准数据相比，我们的平均ROI预测误差在3.94 mm左右。我们的工作表明，从DTI数据中得出的纤维束形状模型是功能性皮质roi的良好预测指标。
Studying structural and functional connectivities of human cerebral cortex has drawn significant interest and effort recently. A fundamental and challenging problem arises when attempting to measure the structural and/or functional connectivities of specific cortical networks: how to identify and localize the best possible regions of interests (ROIs) on the cortex? In our view, the major challenges come from uncertainties in ROI boundary definition, the remarkable structural and functional variability across individuals and high nonlinearities within and around ROIs. In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on their learned fiber shape models from multimodal task-based functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. In the training stage, shape models of white matter fibers are learnt from those emanating from the functional ROIs, which are activated brain regions detected from task-based fMRI data. In the prediction stage, functional ROIs are predicted in individual brains based only on DTI data. Our experiment results show that the average ROI prediction error is around 3.94 mm, in comparison with benchmark data provided by working memory and visual task-based fMRI. Our work demonstrated that fiber bundle shape models derived from DTI data are good predictors of functional cortical ROIs.