伏隔核和前额叶执行控制网络介导树鼩的反转学习过程

Ting-Ting Pan; Chao Liu; De-Min Li; Bin-Bin Nie; Tian-Hao Zhang; Wei Zhang; Shi-Lun Zhao; Qi-Xin Zhou; Hua Liu; Gao-Hong Zhu; Lin Xu; Bao-Ci Shan

doi:10.24272/j.issn.2095-8137.2022.063

伏隔核和前额叶执行控制网络介导树鼩的反转学习过程

doi: 10.24272/j.issn.2095-8137.2022.063

潘婷婷^{1, 2, 3, #},
刘超^{3, 6, #},
李德民^{1, #},
聂彬彬²,
张天昊²,
张维^{2, 5},
赵世伦^{2, 5},
周启心^{3, 6},
刘华^2, ,,
朱高红^4, ,,
徐林^{3, 6, 7, ,},
单保慈^{2, 5, ,}

1.
郑州大学物理学院, 河南郑州 450001, 中国
2.
中国科学院高能物理研究所核技术应用研究中心, 北京 100049, 中国
3.
中国科学院昆明动物研究所动物模型与人类疾病机理重点实验室, KIZ-SU疾病动物模型与新药研发联合实验室, 学习记忆实验室, 云南昆明 650223, 中国
4.
昆明医科大学第一附属医院核医学科, 云南昆明 650032, 中国
5.
中国科学院大学核科学与技术学院, 北京 100049, 中国
6.
中国科学院大学昆明生命科学学院, 云南昆明 650204, 中国
7.
中国科学院脑科学与智能技术卓越创新中心, 上海 200031, 中国

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出版历程
- 收稿日期: 2022-03-28
- 录用日期: 2022-05-09
- 网络出版日期: 2022-05-17
- 刊出日期: 2022-07-18

Nucleus accumbens-linked executive control networks mediating reversal learning in tree shrew brain

Ting-Ting Pan^{1, 2, 3, #},
Chao Liu^{3, 6, #},
De-Min Li^{1, #},
Bin-Bin Nie²,
Tian-Hao Zhang²,
Wei Zhang^{2, 5},
Shi-Lun Zhao^{2, 5},
Qi-Xin Zhou^{3, 6},
Hua Liu^{2
, ,},
Gao-Hong Zhu^{4
, ,},
Lin Xu^{3, 6, 7
, ,},
Bao-Ci Shan^{2, 5
, ,}

1.
School of Physics and Microelectronics, Zhengzhou University, Zhengzhou, Henan 450001, China
2.
Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
3.
CAS Key Laboratory of Animal Models and Human Disease Mechanisms, and KIZ-SU Joint Laboratory of Animal Model and Drug Development, and Laboratory of Learning and Memory, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
4.
Department of Nuclear Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China
5.
School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
6.
Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
7.
CAS Centre for Excellence in Brain Science and Intelligent Technology, Shanghai 200031, China

Funds: This work was supported by the National Natural Science Foundation of China (11975249, 81771923, 12175268, 32071029, 31861143037), China Postdoctoral Science Foundation (2021T140668), and Strategic Priority Research Program of the Chinese Academy of Sciences (XDB32020000)

More Information

Corresponding author: E-mail: liuhua@ihep.ac.cn; 1026909611@qq.com; lxu@vip.163.com; shanbc@ihep.ac.cn

#Authors contributed equally to this work

摘要

摘要: 认知灵活性对动物的生存至关重要。多种神经精神疾病的患者被发现具有认知灵活性功能障碍。以往的研究已经发现大脑的多个功能网络参与动物的认知灵活性，但这些功能网络之间的协作机制仍不清楚。动物反转学习模型是研究认知灵活性的神经机制常用的实验范式。该研究借助触摸屏实验平台，采用基于视觉辨别的反转学习范式训练19只雄性树鼩完成了学习-反转学习等一系列任务。同时结合¹⁸F-FDG正电子发射断层扫描成像，在baseline，learning expert (LE), reversal naive (RN) 和reversal expert (RE) 阶段采集了树鼩的脑代谢图像，以考察实验动物大脑网络的代谢活动在反转学习任务中的模式变化。基于体素的组间差异性分析显示，在RN时期，树鼩左侧伏隔核（Left NAc）的代谢活动显著增加，表明Left NAc是参与树鼩的反转学习过程的关键脑区之一。以Left NAc为种子区构建RN时期树鼩的反转学习网络，该网络主要包含以NAc为关键结构的行为监测系统功能网络和以前额叶皮质（PFC）为关键节点的执行控制系统功能网络。此外，我们还构建了LE和RE时期的代谢网络，用于研究在普通学习状态时大脑的协作模式。LE和RE时期的代谢网络的组成成员几乎是相同的，主要包含了以杏仁核和海马为主的记忆系统和以PFC为主的执行控制系统。因此，反转学习和普通学习过程是由与行为监控、执行控制和记忆系统相关的多个功能网络交互调节的，其中NAc和PFC功能网络可能是作为不同功能网络的连接和启动接口，灵活有效地处理突发和正常情况。

#Authors contributed equally to this work

注释:

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Figure 1. Variations in collaborative network patterns during visual discrimination learning and reversal learning (RL) in brains of tree shrews

A: Schematics of visual discrimination tasks and ¹⁸F-FDG PET/CT imaging. B: Voxel-wise comparisons between RN and LE stage. Significant hyper-metabolism region was found in left NAc in RN stage (P<0.05, FWE-corrected, cluster size>50). T value, value of two-sample t-test. C: Sub-region location of RL network in tree shrew brain. D: Venn diagram showing overlap of metabolic networks involved in LE, RN, and RE stages. E: Schematic of functional systems engaged in dynamic visual discrimination tasks. LN, learning naive; LE, learning expert; RN, reversal naive; RE, reversal expert. MFC, medial frontal cortex; DFC, dorsal frontal cortex; Cg, cingulate cortex; OFC, orbital frontal cortex; TC, temporal cortex; RSg, retrosplenial granular cortex; Pir, piriform cortex; PPC, posterior parietal cortex; SMC, sensorimotor cortex; VC, visual cortex; AuC, auditory cortex; Ent, entorhinal cortex; Ins, insular cortex; PRh, perirhinal cortex; PrS, presubiculum; Hip, hippocampus; Amy, amygdala; IC, inferior colliculus; SC, superior colliculus; NAc, accumbens nucleus; Cl, claustrum; Str, striatum; Cb, cerebellum. L, left hemisphere; R, right hemisphere.

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