Unified framework for robust estimation of brain networks from FMRI using temporal and spatial correlation analyses.
Unified framework for robust estimation of brain networks from FMRI using temporal and spatial correlation analyses.
复制标题
使用时间和空间相关分析对fMRI的大脑网络进行稳健估算的统一框架。
DOI：
10.1109/tmi.2009.2014863
复制
发表时间：
2009-08
影响因子：
10.6
通讯作者：
Xia J
中科院分区：
文献类型：
--
作者：
Wang YM;Xia J
在神经成像领域中，使用功能性磁共振成像（fMRI）来探索大脑网络，即，大脑的各个区域是如何相互交流的本文提出了一个通用的和新的统计框架，强大的和更完整的估计功能磁共振成像的基础上的相关分析和假设检验的脑功能连接。除了在标准和现有方法中检查与每个单独种子的相关性的能力之外，所提出的框架可以通过经由多个相关系数同时检查多种子相关性来检测功能相互作用。空间结构噪声在功能磁共振成像也被考虑到在识别功能互连网络通过非中心F假设检验。还考虑了多重检验和有效自由度的相关问题。此外，部分多重相关性的引入和制定来衡量任何额外的任务引起的，但不是刺激锁定的关系，在大脑区域，使我们可以采取功能连接的分析更接近大脑的直接功能相互作用的表征。使用现实的合成数据和体内fMRI数据的准确性和优势，并在所提出的一般框架的新方法的比较进行评估。
There is a rapidly growing interest in the neuroimaging field to use functional magnetic resonance imaging (fMRI) to explore brain networks, i.e., how regions of the brain communicate with one another. This paper presents a general and novel statistical framework for robust and more complete estimation of brain functional connectivity from fMRI based on correlation analyses and hypothesis testing. In addition to the ability of examining the correlations with each individual seed as in the standard and existing methods, the proposed framework can detect functional interactions by simultaneously examining multiseed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through noncentral F hypothesis tests. The associated issues for the multiple testing and the effective degrees-of-freedom are considered as well. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages, and comparisons of the new approaches in the presented general framework are performed using both realistic synthetic data and in vivo fMRI data.