Innovating beyond electrophysiology through multimodal neural interfaces

Mehrdad Ramezani, Yundong Ren, Ertugrul Cubukcu, Duygu Kuzum
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Abstract

Neural circuits distributed across different brain regions mediate how neural information is processed and integrated, resulting in complex cognitive capabilities and behaviour. To understand dynamics and interactions of neural circuits, it is crucial to capture the complete spectrum of neural activity, ranging from the fast action potentials of individual neurons to the population dynamics driven by slow brain-wide oscillations. In this Review, we discuss how advances in electrical and optical recording technologies, coupled with the emergence of machine learning methodologies, present a unique opportunity to unravel the complex dynamics of the brain. Although great progress has been made in both electrical and optical neural recording technologies, these alone fail to provide a comprehensive picture of the neuronal activity with high spatiotemporal resolution. To address this challenge, multimodal experiments integrating the complementary advantages of different techniques hold great promise. However, they are still hindered by the absence of multimodal data analysis methods capable of providing unified and interpretable explanations of the complex neural dynamics distinctly encoded in these modalities. Combining multimodal studies with advanced data analysis methods will offer novel perspectives to address unresolved questions in basic neuroscience and to develop treatments for various neurological disorders. Flexible and transparent neural probes have facilitated the integration of electrical and optical neural recording techniques in multimodal experiments. Combining these studies with state-of-the-art computational methods would deepen our understanding of neural dynamics, advancing neuroscience and improving brain–computer interface systems.

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通过多模态神经接口创新超越电生理学
分布在不同大脑区域的神经回路调节神经信息的处理和整合,从而产生复杂的认知能力和行为。为了理解神经回路的动力学和相互作用,捕捉神经活动的完整频谱至关重要,从单个神经元的快速动作电位到由慢脑振荡驱动的种群动态。在这篇综述中,我们讨论了电子和光学记录技术的进步,加上机器学习方法的出现,如何提供一个独特的机会来解开大脑的复杂动态。尽管电和光神经记录技术已经取得了很大的进步,但仅凭这些技术还不能提供高时空分辨率的神经元活动的全面图像。为了应对这一挑战,整合不同技术互补优势的多模式实验大有希望。然而,他们仍然受到缺乏多模态数据分析方法的阻碍,这些方法能够为这些模式中明显编码的复杂神经动力学提供统一和可解释的解释。将多模态研究与先进的数据分析方法相结合,将为解决基础神经科学中尚未解决的问题和开发各种神经系统疾病的治疗方法提供新的视角。柔性和透明的神经探针促进了多模态实验中电和光神经记录技术的集成。将这些研究与最先进的计算方法相结合,将加深我们对神经动力学的理解,推进神经科学和改进脑机接口系统。
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