标题： 熵度量作为人脑连接路径的指标 显示英文标题

标题： Entropy measures as indicators of connectivity paths in the human brain

摘要： 在不同刺激下，信息如何在不同脑区之间流动？ 我们旨在通过从信息论的角度研究复杂的认知范式来回答这个问题。 为了从香农的角度评估创造力和模式的出现，我们应用了一系列工具，包括熵密度、有效测量复杂性以及Lempel-Ziv距离。 这些熵工具能够在不依赖于预设参数、模型或对数据的先验假设的情况下检测线性和非线性动态。 为了识别不同脑区之间的连接，我们分析受试者在运动、工作记忆、情绪识别和语言刺激期间的任务功能磁共振成像数据，以深入了解这些复杂的认知过程。 由于该方法不依赖于先验知识，因此特别适合探索性研究，有助于发现大脑中之前未被识别的连接或模式。 识别非线性动态的能力对于研究脑连接尤为重要，因为大脑在多个功能层面上表现出显著的非线性相互作用。 显示英文摘要

摘要： How does the information flow between different brain regions during various stimuli? This is the question we aim to address by studying complex cognitive paradigms in terms of Information Theory. To assess creativity and the emergence of patterns from a Shannon perspective, we applied a range of tools, including Entropy Density, Effective Measure Complexity, and the Lempel-Ziv distance. These entropic tools enable the detection of both linear and non-linear dynamics without relying on pre-established parameters, models, or prior assumptions about the data. To identify connections between different brain regions, we analyse task-based fMRI data from subjects during motor, working memory, emotion recognition, and language stimuli to gain insight into these complex cognitive processes. Since this method does not rely on prior knowledge, it is particularly well-suited for exploratory research, facilitating the discovery of previously unidentified connections or patterns in the brain. The capacity to identify non-linear dynamics is especially important for studying brain connectivity, as the brain exhibits significant non-linear interactions across multiple functional levels.