留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

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

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. Nucleus accumbens-linked executive control networks mediating reversal learning in tree shrew brain. Zoological Research, 2022, 43(4): 528-531. doi: 10.24272/j.issn.2095-8137.2022.063
Citation: 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. Nucleus accumbens-linked executive control networks mediating reversal learning in tree shrew brain. Zoological Research, 2022, 43(4): 528-531. 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

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

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
  • 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
  • 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.

  • [1] Floresco SB. 2015. The nucleus accumbens: An interface between cognition, emotion, and action. Annual Review of Psychology, 66: 25−52. doi: 10.1146/annurev-psych-010213-115159
    [2] Güntürkün O, Ströckens F, Ocklenburg S. 2020. Brain lateralization: a comparative perspective. Physiological Reviews, 100(3): 1019−1063. doi: 10.1152/physrev.00006.2019
    [3] Hamson DK, Roes MM, Galea LAM. 2016. Sex hormones and cognition: Neuroendocrine influences on memory and learning. Comprehensive Physiology, 6(3): 1295−1337.
    [4] Izquierdo A, Brigman JL, Radke AK, Rudebeck PH, Holmes A. 2017. The neural basis of reversal learning: an updated perspective. Neuroscience, 345: 12−26. doi: 10.1016/j.neuroscience.2016.03.021
    [5] Mar AC, Horner AE, Nilsson SRO, Alsiö J, Kent BA, Kim CH, et al. 2013. The touchscreen operant platform for assessing executive function in rats and mice. Nature Protocols, 8(10): 1985−2005. doi: 10.1038/nprot.2013.123
    [6] Menon V, D'Esposito M. 2022. The role of PFC networks in cognitive control and executive function. Neuropsychopharmacology, 47(1): 90−103. doi: 10.1038/s41386-021-01152-w
    [7] Mustafar F, Harvey MA, Khani A, Arató J, Rainer G. 2018. Divergent solutions to visual problem solving across mammalian species. eNeuro, 5(4): ENEURO.0167−18.2018.
    [8] Schumacher JW, McCann M, Maximov KJ, Fitzpatrick D. 2021. Selective enhancement of neural coding in V1 underlies fine discrimination learning in tree shrew. bioRxiv,doi: 10.1101/2021.01.10.426145.
    [9] Uddin LQ. 2021. Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nature Reviews Neuroscience, 22(3): 167−179. doi: 10.1038/s41583-021-00428-w
    [10] Yakushev I, Drzezga A, Habeck C. 2017. Metabolic connectivity: methods and applications. Current Opinion in Neurology, 30(6): 677−685. doi: 10.1097/WCO.0000000000000494
    [11] Yao YG. 2017. Creating animal models, why not use the Chinese tree shrew (Tupaia belangeri chinensis)?. Zoological Research, 38(3): 118−126. doi: 10.24272/j.issn.2095-8137.2017.032
  • 加载中
图(1)
计量
  • 文章访问数:  237
  • HTML全文浏览量:  110
  • PDF下载量:  54
  • 被引次数: 0
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

目录

    /

    返回文章
    返回