Temporal lobe epilepsy tends to reshape the language system causing maladaptive reorganization that can be characterized by task-based functional MRI, and eventually can contribute to surgical decision making processes. However, the dynamic interacting nature of the brain as a complex system is often neglected, with many studies treating the language system as a static monolithic structure. Here, we demonstrate that as a specialized and integrated system, the language network is inherently dynamic, characterized by rich patterns of regional interactions, whose transient dynamics are disrupted in patients with temporal lobe epilepsy. Specifically, we applied tools from dynamic network neuroscience to functional MRI data collected from 50 temporal lobe epilepsy patients and 30 matched healthy controls during performance of a verbal fluency task, as well as during rest. By assigning 16 language-related regions into four subsystems (i.e. bilateral frontal and temporal), we observed regional specialization in both the probability of transient interactions and the frequency of such changes, in both healthy controls and patients during task performance but not rest. Furthermore, we found that both left and right temporal lobe epilepsy patients displayed reduced interactions within the left frontal ‘core’ subsystem compared to the healthy controls, while left temporal lobe epilepsy patients were unique in showing enhanced interactions between the left frontal ‘core’ and the right temporal subsystems. Also, both patient groups displayed reduced flexibility in the transient interactions of the left temporal and right frontal subsystems, which formed the ‘periphery’ of the language network. Importantly, such group differences were again evident only during task condition. Lastly, through random forest regression, we showed that dynamic reconfiguration of the language system tracks individual differences in verbal fluency with superior prediction accuracy compared to traditional activation-based static measures. Our results suggest dynamic network measures may be an effective biomarker for detecting the language dysfunction associated with neurological diseases such as temporal lobe epilepsy, specifying both the type of neuronal communications that are missing in these patients and those that are potentially added but maladaptive. Further advancements along these lines, transforming how we characterize and map language networks in the brain, have a high probability of altering clinical decision making in neurosurgical centres.

中文翻译：

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颞叶癫痫中语言系统的动态网络重构

颞叶癫痫症倾向于重塑语言系统，导致适应不良的重组，这可以通过基于任务的功能性MRI来表征，最终可以促进手术决策过程。然而，经常忽略大脑作为复杂系统的动态交互性质，许多研究将语言系统视为静态的整体结构。在这里，我们证明，作为一个专门的集成系统，语言网络具有内在的动态性，具有丰富的区域互动模式，在颞叶癫痫患者中其瞬态动态被破坏。具体来说，我们在执行口语流利任务期间，将动态网络神经科学的工具应用于从50例颞叶癫痫患者和30例匹配的健康对照中收集的功能性MRI数据，以及在休息期间。通过将16个与语言相关的区域分配到四个子系统（即双边额叶和颞叶）中，我们观察了健康控制对象和患者在执行任务过程中短暂互动的概率和此类变化的频率，但他们并未休息。此外，我们发现与健康对照组相比，左颞叶癫痫患者和右颞叶癫痫患者在左额叶“核心”子系统内的相互作用均降低，而左颞叶癫痫患者在显示左额叶“核心”与“正常”之间的相互作用增强。正确的时间子系统。同样，两个患者组在左颞和右额子系统的短暂交互中都显示出降低的灵活性，这形成了语言网络的“外围”。重要的是，这种群体差异仅在任务状态下才明显。最后，通过随机森林回归，我们发现与传统的基于激活的静态度量相比，语言系统的动态重配置可以以较高的预测精度跟踪口头流利性的个体差异。我们的研究结果表明，动态网络措施可能是检测与神经系统疾病（如颞叶癫痫）相关的语言功能障碍的有效生物标记，可以指定这些患者中缺失的神经元交流类型以及可能增加但适应不良的神经元交流。沿着这些思路的进一步发展，改变了我们在大脑中表征和映射语言网络的方式，很有可能改变神经外科中心的临床决策。这样的群体差异仅在任务状态下才明显。最后，通过随机森林回归，我们发现与传统的基于激活的静态度量相比，语言系统的动态重配置可以以较高的预测精度跟踪口头流利性的个体差异。我们的研究结果表明，动态网络措施可能是检测与神经系统疾病（如颞叶癫痫）相关的语言功能障碍的有效生物标记，可以指定这些患者中缺失的神经元交流类型以及可能增加但适应不良的神经元交流。沿着这些思路的进一步发展，改变了我们在大脑中表征和映射语言网络的方式，很有可能改变神经外科中心的临床决策。这样的群体差异仅在任务状态下才明显。最后，通过随机森林回归，我们发现与传统的基于激活的静态度量相比，语言系统的动态重配置可以以较高的预测精度跟踪口头流利性的个体差异。我们的研究结果表明，动态网络措施可能是检测与神经系统疾病（如颞叶癫痫）相关的语言功能障碍的有效生物标记，可以指定这些患者中缺失的神经元交流类型以及可能增加但适应不良的神经元交流。沿着这些思路的进一步发展，改变了我们在大脑中表征和映射语言网络的方式，很有可能改变神经外科中心的临床决策。