The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods.
The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods.
复制标题
DOI：
10.1016/j.neuroimage.2016.04.006
复制
发表时间：
2016-07-01
期刊：
影响因子：
5.7
通讯作者：
Kou Z
中科院分区：
文献类型：
--
作者：
Iraji A;Calhoun VD;Wiseman NM;Davoodi-Bojd E;Avanaki MRN;Haacke EM;Kou Z
静息态功能磁共振成像（rsfMRI）的自发波动已被广泛用于了解人类大脑的宏连接体。然而，这些波动在受试者之间并不同步，这导致了限制，并使得利用一级基于模型的方法具有挑战性。考虑到rsfMRI数据在时域中的这种局限性，我们建议将rsfMRI数据的时空信息转移到另一个域，连接域，其中每个值表示跨受试者的相同效果。使用一组种子网络和一个连接性指数来计算每个种子网络的功能连接性，我们通过为每个主题生成连接性权重来将数据转换到连接性域。使用数据驱动方法的两个域的比较表明，在连接域中使用数据驱动方法分析数据比时域有几个优点。我们还证明了在连接域中应用基于模型的方法的可行性，这为在rsfMRI数据上使用第一级基于模型的方法提供了新的途径。此外，连接域展示了一个独特的机会来执行第一级基于特征的数据驱动和基于模型的分析。连接域可以从任何识别跨受试者相似的特征集的技术中构建，并且可以通过使我们能够对rsfMRI数据执行广泛的基于模型和数据驱动的方法来极大地帮助研究人员研究宏连接体脑功能，降低分析技术对与脑连接信息无关的参数的敏感性，从一个新的角度评价大脑的静态和动态功能连接。
Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into the connectivity domain by generating connectivity weights for each subject. Comparison of the two domains using a data-driven method suggests several advantages in analyzing data using data-driven methods in the connectivity domain over the time domain. We also demonstrate the feasibility of applying model-based methods in the connectivity domain, which offers a new pathway for the use of first-level model-based methods on rsfMRI data. The connectivity domain, furthermore, demonstrates a unique opportunity to perform first-level feature-based data-driven and model-based analyses. The connectivity domain can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling us to perform a wide range of model-based and data-driven approaches on rsfMRI data, decreasing susceptibility of analysis techniques to parameters that are not related to brain connectivity information, and evaluating both static and dynamic functional connectivity of the brain from a new perspective.