Jha Trisha, Sani Omid G, Pesaran Bijan, Shanechi Maryam M
Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.
Departments of Neurosurgery, Neuroscience, and Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America.
J Neural Eng. 2025 Aug 11;22(4):046040. doi: 10.1088/1741-2552/ade569.
. Improvements in recording technology for multi-region simultaneous recordings enable the study of interactions among distinct brain regions. However, a major computational challenge in studying cross-regional, or cross-population dynamics in general, is that the cross-population dynamics can be confounded or masked by within-population dynamics.. Here, we propose cross-population prioritized linear dynamical modeling (CroP-LDM) to tackle this challenge. CroP-LDM learns the cross-population dynamics in terms of a set of latent states using a prioritized learning approach, such that they are not confounded by within-population dynamics. Further, CroP-LDM can infer the latent states both causally in time using only past neural activity and non-causally in time, unlike some prior dynamic methods whose inference is non-causal.. First, through comparisons with various LDM methods, we show that the prioritized learning objective in CroP-LDM is key for accurate learning of cross-population dynamics. Second, using multi-regional bilateral motor and premotor cortical recordings during a naturalistic movement task, we demonstrate that CroP-LDM better learns cross-population dynamics compared to recent static and dynamic methods, even when using a low dimensionality. Finally, we demonstrate how CroP-LDM can quantify dominant interaction pathways across brain regions in an interpretable manner.. Overall, these results show that our approach can be a useful framework for addressing challenges associated with modeling dynamics across brain regions.
多区域同步记录技术的改进使得对不同脑区之间相互作用的研究成为可能。然而，在一般情况下，研究跨区域或跨群体动态时，一个主要的计算挑战是跨群体动态可能会被群体内部动态所混淆或掩盖。在此，我们提出跨群体优先线性动力学建模（CroP-LDM）来应对这一挑战。CroP-LDM使用优先学习方法，根据一组潜在状态来学习跨群体动态，从而使它们不会被群体内部动态所混淆。此外，与一些推理是非因果关系的先前动态方法不同，CroP-LDM既可以仅使用过去的神经活动在时间上进行因果推断潜在状态，也可以在时间上进行非因果推断。首先，通过与各种LDM方法的比较，我们表明CroP-LDM中的优先学习目标是准确学习跨群体动态的关键。其次，在自然运动任务期间使用多区域双侧运动和运动前皮层记录，我们证明与最近的静态和动态方法相比，CroP-LDM能更好地学习跨群体动态，即使使用低维度数据。最后，我们展示了CroP-LDM如何以可解释的方式量化跨脑区的主要相互作用途径。总体而言，这些结果表明我们的方法可以成为解决与跨脑区动态建模相关挑战的有用框架。
