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使用神经像素探针在猕猴初级视觉皮层中进行高分辨率层识别

High-Resolution Laminar Identification in Macaque Primary Visual Cortex Using Neuropixels Probes.

作者信息

Zhang Li A, Li Peichao, Callaway Edward M

机构信息

The Salk Institute for Biological Studies, La Jolla, CA 92037, USA.

Department of Neurology of the Second Affiliated Hospital and Interdisciplinary Institute of Neuroscience and Technology, School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou 310058, China.

出版信息

bioRxiv. 2024 Sep 22:2024.01.23.576944. doi: 10.1101/2024.01.23.576944.

Abstract

Laminar electrode arrays allow simultaneous recording of activity of many cortical neurons and assignment to layers using current source density (CSD) analyses. Electrode arrays with 100-micron contact spacing have been used to estimate borders between layer 4 versus superficial or deep layers, but in macaque primary visual cortex (V1) there are far more layers, such as 4A which is only 50-100 microns thick. Neuropixels electrode arrays have 20-micron spacing, and thus could potentially discern thinner layers and more precisely identify laminar borders. Here we show that laminar distributions of CSDs lack consistency and the spatial resolution required for thin layers and accurate layer boundaries. To take full advantage of high density Neuropixels arrays, we have developed approaches based on higher resolution electrical signals and analyses, including spike waveforms and spatial spread, unit density, high-frequency action potential (AP) power spectrum, temporal power change, and coherence spectrum, that afford far higher resolution of laminar distinctions, including the ability to precisely detect the borders of even the thinnest layers of V1.

摘要

层流电极阵列允许同时记录许多皮层神经元的活动,并通过电流源密度(CSD)分析确定其所属层。接触间距为100微米的电极阵列已被用于估计第4层与浅层或深层之间的边界,但在猕猴初级视觉皮层(V1)中存在更多的层,例如仅50-100微米厚的4A层。神经像素电极阵列的间距为20微米,因此有可能分辨出更薄的层并更精确地识别层边界。在这里,我们表明CSD的层流分布缺乏一致性,以及薄层层和精确层边界所需的空间分辨率。为了充分利用高密度神经像素阵列,我们开发了基于更高分辨率电信号和分析的方法,包括尖峰波形和空间扩散、单位密度、高频动作电位(AP)功率谱、时间功率变化和相干谱,这些方法能够提供更高分辨率的层区分,包括精确检测V1最薄层边界的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8fda/11422988/b116da22f857/nihpp-2024.01.23.576944v3-f0001.jpg

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