Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity
Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity
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DOI：
10.1007/s10827-010-0271-2
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发表时间：
2011-02-01
影响因子：
1.2
通讯作者：
Prokopenko, Mikhail
中科院分区：
文献类型：
--
作者：
Lizier, Joseph T.;Heinzle, Jakob;Prokopenko, Mikhail
人脑通过其子区域之间的相互作用进行高度复杂的信息处理。我们提出了一种用于区域间连通性分析的新方法，使用互信息和转移熵的多元扩展。该方法使我们能够识别大脑区域之间潜在的定向信息结构，以及该结构如何根据行为条件而变化。该方法的特点是使用不对称、多元、信息理论分析，不仅捕获方向性和非线性关系，还捕获集体相互作用。重要的是，该方法能够仅用相对较少的数据来估计多元信息度量。我们演示了分析功能磁共振成像时间序列的方法，以建立参与视觉运动跟踪任务的大脑区域之间的定向信息结构。重要的是，这会产生分层结构，其中已知的运动规划区域驱动视觉和运动控制区域。此外，我们还检查了随着跟踪任务难度的增加该结构的变化。我们发现任务难度调节了参与运动规划的皮质网络区域之间以及参与运动控制微调的运动皮层和小脑之间的耦合强度。这些方法很可能会在识别其他认知任务和数据模式中的区域间结构（以及通过实验诱导该结构的变化）方面发挥作用。
The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.