NeuroCarto:为高电极计数神经探针构建自定义读出通道图的工具包。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2025-01-01 Epub Date: 2025-01-04 DOI:10.1007/s12021-024-09705-2
Ta-Shun Su, Fabian Kloosterman
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引用次数: 0

摘要

神经像素探针在一个或多个小腿上包含数千个电极,并且足够小,可以长期记录自由行为的小动物的神经活动。然而,电极数量的增加和探头封装的小型化导致了一种妥协,即电极组共享单个读出通道,并且在任何给定时间只能读出一小部分电极。然后,实验者面临的挑战是选择一个电极子集(即通道图),既覆盖感兴趣的大脑区域,又遵守底层硬件的限制。在这里,我们介绍了NeuroCarto,一个Python工具包和GUI,用于简化Neuropixels探针的自定义通道映射的构建。我们描述了一种通用的迭代方法来选择电极,并提供了一个特定的实现,允许实验者指定沿探针柄感兴趣的区域蓝图和所需的局部电极密度。NeuroCarto帮助从蓝图生成通道映射,并可视化潜在的读出通道冲突。我们展示了NeuroCarto在实验工作流程中的效用,在自由移动的小鼠中使用4柄Neuropixels 2.0探针同时记录背侧和腹侧海马。
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NeuroCarto: A Toolkit for Building Custom Read-out Channel Maps for High Electrode-count Neural Probes.

Neuropixels probes contain thousands of electrodes across one or more shanks and are sufficiently small to allow chronic recording of neural activity in freely behaving small animals. However, the joint increase in the number of electrodes and miniaturization of the probe package has led to a compromise in which groups of electrodes share a single read-out channel and only a fraction of the electrodes can be read out at any given time. Experimenters then face the challenge of selecting a subset of electrodes (i.e., channel map) that both covers the brain regions of interest and adheres to the restrictions of the underlying hardware. Here, we present NeuroCarto, a Python toolkit and GUI to simplify the construction of a custom channel map for Neuropixels probes. We describe a general iterative approach to select electrodes and provide a specific implementation that allows experimenters to specify a blueprint of regions of interest along the probe shanks and the desired local electrode density. NeuroCarto assists in generating a channel map from the blueprint and visualizes potential read-out channel conflicts. We showcase the utility of NeuroCarto in an experimental workflow to simultaneously record from the dorsal and ventral hippocampus with 4-shank Neuropixels 2.0 probes in freely moving mice.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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