Objective. Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. Approach. We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. Main results. We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches. Significance. This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

客观。复杂的时空神经活动编码了与行为和认知相关的丰富信息。传统研究集中在使用许多不同测量方式中的一种获得的神经活动上，每种测量方式都提供了对神经代码的有用但不完整的评估。多模态技术可以克服单一模态的空间和时间分辨率的权衡，从而揭示对系统级神经机制的更深入和更全面的理解。揭示多尺度动力学对于从机制上理解大脑功能以及利用神经科学见解开发更有效的临床治疗至关重要。方法. 我们讨论了用于表征不同尺度的神经活动的传统方法，并回顾了这些方法如何结合的当代例子。然后，我们提出了整合多个尺度的活动的案例，以从每种方法的综合优势中受益，并阐明对神经过程的更全面的理解。主要结果。我们研究了不同尺度的神经活动的各种组合和可用于跨尺度整合或阐明信息的分析技术，以及实现此类令人兴奋的研究的技术。我们总结了未来多尺度研究面临的挑战，并讨论了这些方法的力量和潜力。意义. 该路线图将引导读者了解广泛的多尺度神经解码技术及其相对于单模态分析的优势。这篇评论文章强调了多尺度分析对于系统地探究认知和行为背后的复杂时空机制的重要性。