Lizier Joseph T, Heinzle Jakob, Horstmann Annette, Haynes John-Dylan, Prokopenko Mikhail
School of Information Technologies, The University of Sydney, NSW 2006, Sydney, Australia.
J Comput Neurosci. 2011 Feb;30(1):85-107. doi: 10.1007/s10827-010-0271-2. Epub 2010 Aug 27.
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.
人类大脑通过其亚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，该方法使用互信息和转移熵的多变量扩展。该方法使我们能够识别脑区之间潜在的定向信息结构，以及该结构如何根据行为条件发生变化。该方法的独特之处在于使用不对称、多变量的信息理论分析，它不仅能捕捉定向和非线性关系，还能捕捉集体相互作用。重要的是，该方法仅用相对较少的数据就能估计多变量信息度量。我们展示了该方法用于分析功能磁共振成像时间序列，以建立参与视觉运动跟踪任务的脑区之间的定向信息结构。重要的是，这产生了一种分层结构，其中已知的运动规划区域驱动视觉和运动控制区域。此外，我们研究了随着跟踪任务难度增加，这种结构的变化。我们发现任务难度调节了参与运动规划的皮层网络区域之间以及运动皮层和参与运动控制微调的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（以及实验诱导的这种结构变化）方面发挥作用。
