Brain without anatomy: construction and comparison of fully network-driven structural MRI connectomes.
Brain without anatomy: construction and comparison of fully network-driven structural MRI connectomes.
复制标题
DOI：
10.1371/journal.pone.0096196
复制
发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Xu D
中科院分区：
文献类型：
--
作者：
Tymofiyeva O;Ziv E;Barkovich AJ;Hess CP;Xu D
MRI 连接组学方法将大脑视为一个网络，并提供有关其组织、效率和破坏机制的新信息。定义网络节点最常用的方法是将大脑注册到基于布罗德曼区域的标准化解剖图谱。这种方法受到受试者间差异的限制，并且在大脑成熟或神经可塑性（脑损伤后的大脑重组）的背景下尤其可能出现问题。在这项研究中，我们结合了不同的图像处理和网络理论方法，创建了一种新颖的方法，可以实现基于扩散 MRI 的大脑网络的无图谱构建和连接方式比较。我们在三个年龄组中说明了所提出的方法：新生儿、6 个月大的婴儿和成人。首先，我们探索了一种数据驱动的方法，该方法基于以下假设来确定等面积节点的最佳数量：大脑的所有皮质区域都是连接的，因此大脑的任何部分在结构上都是孤立的。其次，为了实现连接方式比较，使用带有模拟退火的矩阵对齐算法在每个组内的网络域中执行与“参考大脑”的对齐。成对网络对齐后的相关系数范围为0.6102至0.6673。为了测试该方法的重现性，对 6 个月大组的一名受试者和成人组的一名受试者进行了两次扫描，相关系数分别为 0.7443 和 0.7037。虽然由于分割和噪声而小于 1，但从统计角度来看，这些值显着高于受试者间值。分区的旋转在很大程度上解释了这种变化。通过解剖学的抽象，开发的框架允许对结构 MRI 连接体进行完全网络驱动的分析，并且可以应用于任何发育阶段且皮质解剖学存在显着差异的受试者。
MRI connectomics methods treat the brain as a network and provide new information about its organization, efficiency, and mechanisms of disruption. The most commonly used method of defining network nodes is to register the brain to a standardized anatomical atlas based on the Brodmann areas. This approach is limited by inter-subject variability and can be especially problematic in the context of brain maturation or neuroplasticity (cerebral reorganization after brain damage). In this study, we combined different image processing and network theory methods and created a novel approach that enables atlas-free construction and connection-wise comparison of diffusion MRI-based brain networks. We illustrated the proposed approach in three age groups: neonates, 6-month-old infants, and adults. First, we explored a data-driven method of determining the optimal number of equal-area nodes based on the assumption that all cortical areas of the brain are connected and, thus, no part of the brain is structurally isolated. Second, to enable a connection-wise comparison, alignment to a “reference brain” was performed in the network domain within each group using a matrix alignment algorithm with simulated annealing. The correlation coefficients after pair-wise network alignment ranged from 0.6102 to 0.6673. To test the method’s reproducibility, one subject from the 6-month-old group and one from the adult group were scanned twice, resulting in correlation coefficients of 0.7443 and 0.7037, respectively. While being less than 1 due to parcellation and noise, statistically, these values were significantly higher than inter-subject values. Rotation of the parcellation largely explained the variability. Through the abstraction from anatomy, the developed framework allows for a fully network-driven analysis of structural MRI connectomes and can be applied to subjects at any stage of development and with substantial differences in cortical anatomy.
登录
查看更多内容
影响因子：
16.2
作者：
Mueller S;Wang D;Fox MD;Yeo BT;Sepulcre J;Sabuncu MR;Shafee R;Lu J;Liu H
通讯作者：
Liu H
影响因子：
3
作者：
Hagmann P;Grant PE;Fair DA
通讯作者：
Fair DA
影响因子：
5.7
作者：
Genovese, CR;Lazar, NA;Nichols, T
通讯作者：
Nichols, T
影响因子：
3.7
作者：
Raj A;Chen YH
通讯作者：
Chen YH
影响因子：
5.7
作者：
Dennis, Emily L.;Jahanshad, Neda;Thompson, Paul M.
通讯作者：
Thompson, Paul M.