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新奇探索过程中啮齿动物大脑中的大规模和多尺度网络

Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration.

作者信息

Cohen Michael X, Englitz Bernhard, França Arthur S C

机构信息

Donders Centre for Medical Neuroscience, Radboud University Medical Center, 6525 GA, Nijmegen

Computational Neuroscience Lab, Department of Neurophysiology, Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 XZ, Nijmegen The Netherlands.

出版信息

eNeuro. 2021 May 12;8(3). doi: 10.1523/ENEURO.0494-20.2021. Print 2021 May-Jun.

DOI:10.1523/ENEURO.0494-20.2021
PMID:33757983
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8121262/
Abstract

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, because of limited data availability and to the difficulty of analyzing rich multivariate datasets. Here, we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential (LFP) activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2-200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (∼4-10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.

摘要

神经活动在多个空间和时间尺度上进行协调,这些协调模式与健康和受损的认知操作都有关联。然而,由于数据可用性有限以及分析丰富的多变量数据集存在困难,实证性跨尺度研究相对较少。在这里,我们应用频率分辨多变量源分离分析来表征一个大规模数据集,该数据集包含在自由活动小鼠的三个脑区(前额叶皮层、顶叶皮层、海马体)同时记录的尖峰活动和局部场电位(LFP)活动。我们在一系列频率(2 - 200赫兹)范围内识别出一组多维的、区域间网络。这些网络在不同动物个体内的不同记录时段是可重复的,但在不同动物之间存在差异,这表明网络架构存在个体变异性。θ波段(约4 - 10赫兹)网络具有几个突出特征,包括所有区域的贡献大致相等以及网络间的强同步性。总体而言,这些发现展示了在旷场探索期间,跨多个空间、频谱和时间尺度运行的皮层网络大规模功能激活的多维图景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/df3c73921f67/SN-ENUJ210090F006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/3efd1bfb031d/SN-ENUJ210090F001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/5729175f0e1a/SN-ENUJ210090F002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/41b8ab9cbd54/SN-ENUJ210090F003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/49b4fc60e656/SN-ENUJ210090F004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/f109cbbef735/SN-ENUJ210090F005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/df3c73921f67/SN-ENUJ210090F006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/3efd1bfb031d/SN-ENUJ210090F001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/5729175f0e1a/SN-ENUJ210090F002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/41b8ab9cbd54/SN-ENUJ210090F003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/49b4fc60e656/SN-ENUJ210090F004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/f109cbbef735/SN-ENUJ210090F005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8117/8121262/df3c73921f67/SN-ENUJ210090F006.jpg

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3
Low-cost and versatile electrodes for extracellular chronic recordings in rodents.
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Heliyon. 2020 Sep 14;6(9):e04867. doi: 10.1016/j.heliyon.2020.e04867. eCollection 2020 Sep.
4
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J Neurosci. 2020 Sep 30;40(40):7702-7713. doi: 10.1523/JNEUROSCI.0321-20.2020. Epub 2020 Sep 8.
5
Reflections on the past two decades of neuroscience.对过去二十年神经科学的反思。
Nat Rev Neurosci. 2020 Oct;21(10):524-534. doi: 10.1038/s41583-020-0363-6. Epub 2020 Sep 2.
6
Mesoscopic-scale functional networks in the primate amygdala.灵长类动物杏仁核的介观尺度功能网络。
Elife. 2020 Sep 2;9:e57341. doi: 10.7554/eLife.57341.
7
Different theta frameworks coexist in the rat hippocampus and are coordinated during memory-guided and novelty tasks.不同的theta 框架在大鼠海马中共存,并在记忆引导和新颖性任务中协调。
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