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Comparison analysis of diffusion-tensor-imaging tractography in native space and in normalized space: applied for brain network analysis based on

机译:自然空间和规范化空间中的弥散张量成像术的影像学比较分析:用于基于

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Brain network analysis has been widely used to study the development of the brain and various neural diseases. Brain networks are mainly constructed based on functional Magnetic Resonance Imaging (fMRI): resting state Blood-Oxygen-Level-Dependent (rs-BOLD) and Diffusion-Weighted-Imaging (DWI) or Diffusion-Tensor-Imaging (DTI) signals. The nodes of the network are normally defined by ROIs, typically brain atlases labelled in normalized space, whereas the edges are defined as the connectivity between each two nodes. If the connectivity is defined as the time-domain correlation between rs-BOLD data of two nodes, it is called functional connection network (FCN). If the connectivity is defined as the number of fibers which represent the spatial connections between two nodes, it is called diffusion tractography network or structural connection network (SCN). The SCN may vary depending on tractography space selected. We call each person's space, such as the original DTI volume space, the native space. Brain template space, such as the template ICBM152 in which the brain atlas is labelled, is called the normalized space. This study aims to evaluate which space is more suitable for constructing brain SCN, especially for clinic application. A comparison analysis experiment of tractography space is presented in this paper. The correlation between SCN and FCN is used as the comparison criteria. We use two brain atlases (Brodmann and AAL116), three connectivity networks (one FCN in normalized space, two SCNs with one in native space and the other in normalized space), and three datasets (healthy subjects, spinal cord injury patients and stroke patients) in this study. Both the correlation between FCN and SCN, and the t test between healthy subjects and patients' datasets are used for evaluating tractography space. The results show a positive correlation between FCN and SCN. The correlation coefficients between FCN and SCN in native space are significantly greater than that in normalized space. This inspires us that tractography in native space would result in higher correspondence with the functional organization. The independent t test of the connection strength between "Healthy" and "SCI patients", "Healthy" and "Stroke" shows that more significant difference of connection strength will be explored by the SCN in native than in normalized space. It is verified that the tractography in native space should be a better choice when studying brain connectivity in clinic application.
机译:脑网络分析已被广泛用于研究脑和各种神经疾病的发展。脑网络主要基于功能磁共振成像(fMRI)构建:静息状态血氧水平依赖性(rs-BOLD)和扩散加权成像(DWI)或扩散张量成像(DTI)信号。网络的节点通常由ROI定义,通常是在标准化空间中标记的脑图集,而边缘则定义为每两个节点之间的连通性。如果将连通性定义为两个节点的rs-BOLD数据之间的时域相关性,则称为功能连接网络(FCN)。如果将连通性定义为代表两个节点之间空间连接的光纤数量,则称为扩散束摄影网络或结构连接网络(SCN)。 SCN可能会根据所选的X光检查空间而有所不同。我们称呼每个人的空间,例如原始DTI体积空间,本机空间。大脑模板空间(例如标有脑图集的模板ICBM152)称为标准化空间。这项研究旨在评估哪个空间更适合构建大脑SCN,尤其是临床应用。本文提出了一种对比分析的方法。 SCN和FCN之间的相关性用作比较标准。我们使用两个大脑图谱(Brodmann和AAL116),三个连接网络(一个归一化空间中的FCN,两个SCN,一个归化空间中的SCN和另一个归一化空间中的SCN)和三个数据集(健康受试者,脊髓损伤患者和中风患者) ) 在这个研究中。 FCN和SCN之间的相关性,以及健康受试者与患者数据集之间的t检验均用于评估超声造影空间。结果显示FCN和SCN之间呈正相关。 FCN和SCN在原始空间中的相关系数显着大于归一化空间中的相关系数。这激发了我们,本机空间中的人体解剖学将导致与功能组织的更高对应性。对“健康”和“ SCI患者”,“健康”和“中风”之间的连接强度的独立t检验表明,与自然空间相比,SCN在天然空间中将探索更大的连接强度差异。事实证明,在临床应用中研究脑部连通性时,在自然空间中进行放射学检查应是更好的选择。

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