Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD 20892-1407, USA.
Neuroimage. 2010 Sep;52(3):1027-40. doi: 10.1016/j.neuroimage.2009.11.081. Epub 2009 Dec 5.
Dynamic connectivity networks identify directed interregional interactions between modeled brain regions in neuroimaging. However, problems arise when the regions involved in a task and their interconnections are not fully known a priori. Objective measures of model adequacy are necessary to validate such models. We present a connectivity formalism, the Switching Linear Dynamic System (SLDS), that is capable of identifying both Granger-Geweke and instantaneous connectivity that vary according to experimental conditions. SLDS explicitly models the task condition as a Markov random variable. The series of task conditions can be estimated from new data given an identified model providing a means to validate connectivity patterns. We use SLDS to model functional magnetic resonance imaging data from five regions during a finger alternation task. Using interregional connectivity alone, the identified model predicted the task condition vector from a different subject with a different task ordering with high accuracy. In addition, important regions excluded from a model can be identified by augmenting the model state space. A motor task model excluding primary motor cortices was augmented with a new neural state constrained by its connectivity with the included regions. The augmented variable time series, convolved with a hemodynamic kernel, was compared to all brain voxels. The right primary motor cortex was identified as the best region to add to the model. Our results suggest that the SLDS model framework is an effective means to address several problems with modeling connectivity including measuring overall model adequacy and identifying important regions missing from models.
动态连接网络可识别神经影像学中建模脑区之间的有向区域间相互作用。然而，当任务涉及的区域及其相互连接在事先不完全了解时，就会出现问题。需要有适当的模型衡量标准来验证此类模型。我们提出了一种连接形式，即切换线性动态系统 (SLDS)，它能够识别 Granger-Geweke 和根据实验条件变化的即时连接。SLDS 明确将任务条件建模为马尔可夫随机变量。在给定已识别模型的情况下，可以从新数据中估计任务条件序列，从而提供验证连接模式的方法。我们使用 SLDS 对五个手指交替任务期间的功能磁共振成像数据进行建模。仅使用区域间连接，从具有不同任务顺序的不同受试者识别出的模型可以高精度地从不同受试者的任务条件向量进行预测。此外，通过增加模型状态空间，可以识别从模型中排除的重要区域。一个不包括初级运动皮质的运动任务模型通过增加一个受其与包含区域连接约束的新神经状态来扩充模型状态空间。扩充后的变量时间序列与血流动力学核卷积，并与所有大脑体素进行比较。右初级运动皮质被确定为添加到模型中的最佳区域。我们的研究结果表明，SLDS 模型框架是解决连接建模中几个问题的有效手段，包括衡量整体模型适当性和识别模型中缺少的重要区域。
