Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health Bethesda, MD, USA.
Front Syst Neurosci. 2012 Jan 18;5:104. doi: 10.3389/fnsys.2011.00104. eCollection 2011.
Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a "node" in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an "instantaneous" connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis.
功能磁共振成像（fMRI）数据中区域间定向特定或因果相互作用的分析已经大量涌现。在这里，我们确定了现有有效连通性方法需要解决的六个问题。这些问题是在 fMRI 的线性动力系统（LDSf）框架内讨论的。第一个问题涉及使用确定性模型来识别区域间的有效连通性。我们表明，确定性动力学无法识别通常作为连通性标记的试验到试验的可变性，而随机模型可以捕获这种可变性。第二个问题涉及大多数方法建模的简单（恒定）连通性。LDSf 模型的连通性参数可以与输入数据在同一时间尺度上变化。此外，将 LDSf 扩展到多个模型的混合物中，可以提供更稳健的连通性变化。第三个问题涉及网络本身的正确识别，包括网络节点的数量和解剖起源。LDSf 状态空间的扩充可以识别网络的附加节点。第四个问题涉及用作网络“节点”的信号的位置。一个新的扩展 LDSf 结合稀疏正则相关可以根据连通性从解剖定义的区域中选择最相关的体素。第五个问题涉及连接解释。个体参数差异受到了最多的关注。我们提出了替代的连通性变化网络描述符，这些描述符考虑了整个网络。第六个问题涉及 fMRI 数据相对于大脑中区域间相互作用的时间尺度的时间分辨率。LDSf 包括一个“瞬时”连接项，以捕获比数据分辨率更快的时间尺度上的连通性。LDS 框架还可以扩展到对 fMRI 和 EEG 数据进行统计组合。LDSf 框架是有效连通性分析的有前途的基础。
