Zhao Siyuan, Tang Xin, Tian Weiwen, Partarrieu Sebastian, Liu Ren, Shen Hao, Lee Jaeyong, Guo Shiqi, Lin Zuwan, Liu Jia
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
Nat Neurosci. 2023 Apr;26(4):696-710. doi: 10.1038/s41593-023-01267-x. Epub 2023 Feb 20.
Stably recording the electrical activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this timescale. Here, we introduce a method to precisely implant electronics with an open, unfolded mesh structure across multiple brain regions in the mouse. The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the year-long implantation. Rigorous statistical analysis, visual stimulus-dependent measurement and unbiased, machine-learning-based analysis demonstrated that single-unit action potentials have been recorded from the same neurons of behaving mice in a very long-term stable manner. Leveraging this stable structure, we demonstrated that the same neurons can be recorded over the entire adult life of the mouse, revealing the aging-associated evolution of single-neuron activities.
在动物的成年期稳定记录同一神经元的电活动对神经科学研究和生物医学应用至关重要。目前的可植入设备无法在这个时间尺度上提供稳定记录。在此,我们介绍一种方法,可将具有开放、展开网状结构的电子器件精确植入小鼠的多个脑区。这种开放的网状结构与神经网络形成稳定的交织结构,防止探针漂移,并且在长达一年的植入过程中未显示出免疫反应和神经元损失。严格的统计分析、视觉刺激依赖性测量以及基于无偏机器学习的分析表明,已以非常长期稳定的方式从行为小鼠的同一神经元记录到单单元动作电位。利用这种稳定结构,我们证明可以在小鼠的整个成年期记录同一神经元,揭示单神经元活动与衰老相关的演变。