Abstract While the concept of connectivity has gained popularity in fields like hydrology and ecology, little agreement exists on its definition, which hinders its use in both scientific and legal contexts. In contrast, neuroscientists have developed not only strong conceptualizations of connectivity but also tools to quantify it: a clear distinction is made between structural connectivity, which is determined from brain anatomy; functional connectivity, which is estimated based on statistical dependencies between neuronal electric timeseries; and effective connectivity, which infers causal relations from the same timeseries based on the assumption that “true” interactions occur with a certain time delay. The motivation of this review arose from the hypothesis that connectivity-related statistical techniques, which are applied to timeseries of electrical currents measured by placing electrodes on the scalp of the human brain, could also apply to high-frequency hydrological timeseries acquired to characterize catchment response to precipitation. Here we bring together existing conceptualizations of structural, functional and effective connectivity in hydrology and ecology and compare them with those used in brain neuroscience. We then summarize the most important brain connectivity measures and their associated mathematical frameworks before evaluating the potential of those measures to help advance our understanding of hydrologic connectivity properties – in terms of the frequency, magnitude, timing, duration and rate of water movement linking two disparate locations. Lastly, we present a short case study where a selection of brain connectivity measures is applied to 35 groundwater and streamflow timeseries from a Swiss catchment to infer subsurface flow-driven hydrologic connectivity. Our literature review combined with our short case study suggest that an ensemble of functional and effective connectivity measures should be used and constrained not only by structural connectivity measures but also by interpretation thresholds in order to make results parsimonious. We highlight challenges associated with transferring brain connectivity measures to hydrology, especially those related to choosing the appropriate length and sampling frequency of input timeseries when assessing perennial versus ephemeral connectivity, appropriately detecting and differentiating noisy from indirect connections, and interpreting unbounded connectivity measures. We then offer recommendations for future research and propose that hydrologists use a common classification system encompassing all potential connectivity assessment approaches and measures in order to facilitate scientific communication.

中文翻译：

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使用脑神经科学方法评估结构、功能和有效的水文连通性：最新技术和研究方向

摘要 虽然连通性的概念在水文学和生态学等领域越来越受欢迎，但对其定义几乎没有共识，这阻碍了其在科学和法律环境中的使用。相比之下，神经科学家不仅开发了强大的连通性概念化，而且还开发了量化它的工具：结构连通性之间有明显的区别，这是由大脑解剖学决定的；功能连通性，这是基于神经元电时间序列之间的统计依赖性估计的；有效的连通性，它基于“真实”交互以一定的时间延迟发生的假设，从相同的时间序列推断因果关系。这篇评论的动机来自这样一个假设，即与连接相关的统计技术，应用于通过将电极放置在人脑头皮上测量的电流时间序列，也可以应用于获取的高频水文时间序列，以表征集水区对降水的响应。在这里，我们汇集了水文学和生态学中结构、功能和有效连接的现有概念，并将它们与脑神经科学中使用的概念进行比较。然后我们总结了最重要的大脑连通性措施及其相关的数学框架，然后评估这些措施的潜力，以帮助我们加深对水文连通性特性的理解——在频率、幅度、时间、持续时间和水流运动速度方面，将两个不同的地点。最后，我们提出了一个简短的案例研究，其中对来自瑞士集水区的 35 个地下水和溪流时间序列应用了一系列大脑连通性测量，以推断地下水流驱动的水文连通性。我们的文献综述结合我们的简短案例研究表明，不仅应通过结构连通性措施而且还应通过解释阈值来使用和约束一组功能性和有效的连通性措施，以使结果简洁。我们强调了与将大脑连通性措施转移到水文学相关的挑战，尤其是那些在评估常年与短暂连通性时选择合适的输入时间序列的长度和采样频率、适当地检测和区分噪声与间接连接相关的挑战，并解释无界连通性措施。然后，我们为未来的研究提供建议，并建议水文学家使用包含所有潜在连通性评估方法和措施的通用分类系统，以促进科学交流。