Identification and validation of effective connectivity networks in functional magnetic resonance imaging using switching linear dynamic systems.
Identification and validation of effective connectivity networks in functional magnetic resonance imaging using switching linear dynamic systems.
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DOI：
10.1016/j.neuroimage.2009.11.081
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发表时间：
2010-09
期刊：
影响因子：
5.7
通讯作者：
Horwitz B
中科院分区：
文献类型：
--
作者：
Smith JF;Pillai A;Chen K;Horwitz B
动态连接网络识别神经成像中建模的大脑区域之间的定向区域间相互作用。然而，当一项任务所涉及的区域及其相互联系事先并不完全清楚时，就会出现问题。模型充分性的客观措施是必要的，以验证这些模型。我们提出了一个连接形式主义，切换线性动态系统（SLDS），这是能够识别Granger-Geweke和瞬时连接，根据实验条件而变化。SLDS明确地将任务条件建模为马尔可夫随机变量。给定提供验证连接模式的手段的识别模型，可以从新数据估计一系列任务条件。我们使用SLDS模拟功能磁共振成像数据从五个地区在手指交替任务。单独使用区域间连接，所确定的模型预测的任务条件向量从不同的主题与不同的任务排序具有高精度。此外，可以通过扩充模型状态空间来识别从模型中排除的重要区域。一个运动任务模型，不包括初级运动皮层，增加了一个新的神经状态约束其连接所包括的地区。与血流动力学内核卷积的增强变量时间序列与所有脑体素进行比较。右侧初级运动皮层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模型框架是一种有效的手段，以解决建模连接，包括测量整体模型的充分性和识别重要的区域模型缺失的几个问题。
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.
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影响因子：
0.8
作者：
Cavanaugh, JE;Shumway, RH
通讯作者：
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使用格兰杰因果关系评估双手运动期间主导初级运动皮层的有效连接
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作者：
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通讯作者：
Ding, Mingzhou
影响因子：
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作者：
Aguirre, GK;Zarahn, E;D'Esposito, M
通讯作者：
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