Yang Zhipeng, Li Xiaojie, Zhou Jiliu, Wu Xi, Ding Zhaohua
Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, PR China; College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, 610225, PR China.
Department of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, PR China.
J Neurosci Methods. 2020 Apr 1;335:108626. doi: 10.1016/j.jneumeth.2020.108626. Epub 2020 Feb 4.
Large numbers of fibers produced by fiber tractography are often grouped into bundles with anatomical interpretations. Traditional clustering methods usually generate bundles with spatial anatomic coherences only. To associate bundles with function, some studies incorporate functional connectivity of grey matter to guide clustering on the premise that fibers provide the basis of information transmission for cortex. However, functional properties along fiber tracts were ignored by these methods. Considering several recent studies showing that BOLD (Blood-Oxygen-Level Dependent) signals of white matter contain functional information of axonal fibers, this work is motivated to demonstrate that whole brain white matter fibers can be clustered with integration of functional and structural information they contain.
We proposed a novel algorithm based on Gaussian mixture model and expectation maximization to achieve optimal bundling with both structural and functional coherences. The functional coherence between two fibers is defined as the average correlation in BOLD signal between corresponding points. Whole brain fibers under resting state and sensory stimulation conditions were used to demonstrate the effectiveness of the proposed technique.
Our in vivo experiments show the robustness of proposed algorithm and influences of weights between structure and function, and repeatability of reconstructed major bundles across individuals.
In contrast to traditional methods, the proposed clustering method can achieve structurally more compact bundles, which are specifically related to evoking function.
The proposed concept and framework can be used to identify functional pathways and their structural features under specific function loading.
纤维束成像产生的大量纤维通常会根据解剖学解释分组为束。传统的聚类方法通常仅生成具有空间解剖连贯性的束。为了将束与功能联系起来，一些研究在纤维为皮层提供信息传递基础这一前提下，纳入灰质的功能连接来指导聚类。然而，这些方法忽略了沿纤维束的功能特性。考虑到最近的几项研究表明白质的血氧水平依赖（BOLD）信号包含轴突纤维的功能信息，这项工作旨在证明全脑白质纤维可以通过整合其包含的功能和结构信息进行聚类。
我们提出了一种基于高斯混合模型和期望最大化的新算法，以实现具有结构和功能连贯性的最优捆绑。两根纤维之间的功能连贯性定义为对应点之间BOLD信号的平均相关性。使用静息状态和感觉刺激条件下的全脑纤维来证明所提出技术的有效性。
我们的体内实验表明了所提出算法的稳健性以及结构与功能权重的影响，以及个体间重建主要束的可重复性。
与传统方法相比，所提出的聚类方法可以实现结构上更紧凑的束，这些束与诱发功能具有特定关系。
所提出的概念和框架可用于识别特定功能负荷下的功能通路及其结构特征。
