A study on the effects of learning on the properties of rats hippocampal-prefrontal connections in a memory task_Journal of Biomedical Engineering

Authors：

 LI Shuangyan ^1,2,3,4 , ZHENG Weiran ^1,2,3,4 , A Lan ^1,2,3,4 , WANG Longlong ^1,3,4 , LIU Suhong ^1,2,3,4 , LIU Hui ^1,2,3,4

1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, P. R. China;
2. School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, P. R. China;
3. Hebei Key Laboratory of Electromagnetic Field and Electrical Reliability, Hebei University of Technology, Tianjin 300130, P. R. China;
4. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 300130, P. R. China;

Corresponding author：

LI Shuangyan, Email: lishuangyan@hebut.edu.cn

Keywords：

Learning and memory; Hippocampus; Prefrontal cortex; Dynamic causal model; Causal connections

DOI：

10.7507/1001-5515.202312042

Video：

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The transmission and interaction of neural information between the hippocampus and the prefrontal cortex play an important role in learning and memory. However, the specific effects of learning memory-related tasks on the connectivity characteristics between these two brain regions remain inadequately understood. This study employed in vivo microelectrode recording to obtain local field potentials (LFPs) from the ventral hippocampus (vHPC) and medial prefrontal cortex (mPFC) in eight rats during the performance of a T-maze task, assessed both before and after task learning. Additionally, dynamic causal modeling (DCM) was utilized to analyze alterations in causal connectivity between the vHPC and the mPFC during memory task execution pre- and post-learning. Results indicated the presence of forward connections from vHPC to mPFC and backward connections from mPFC to vHPC during the T-maze task. Moreover, the forward connection between these brain regions was slightly enhanced after task learning, whereas the backward connection was diminished. These changes in connectivity corresponded with the observed trends when the rats correctly performed the T-maze task. In conclusion, this study may facilitate future investigations into the underlying mechanisms of learning and memory from the perspective of connectivity characteristics between distinct brain regions.

Citation： LI Shuangyan, ZHENG Weiran, A Lan, WANG Longlong, LIU Suhong, LIU Hui. A study on the effects of learning on the properties of rats hippocampal-prefrontal connections in a memory task. Journal of Biomedical Engineering, 2024, 41(6): 1095-1102. doi: 10.7507/1001-5515.202312042 Copy

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2.	Rushworth M F, Noonan M P, Boorman E D, et al. Frontal cortex and reward-guided learning and decision-making. Neuron, 2011, 70(6): 1054-1069.
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6.	Vertes R P. Interactions among the medial prefrontal cortex, hippocampus and midline thalamus in emotional and cognitive processing in the rat. Neuroscience, 2006, 142(1): 1-20.
7.	Liu T, Bai W, Xia M, et al. Directional hippocampal-prefrontal interactions during working memory. Behavioural Brain Research, 2018, 338: 1-8.
8.	Morici J F, Weisstaub N V, Zold C L. Hippocampal-medial prefrontal cortex network dynamics predict performance during retrieval in a context-guided object memory task. Proc Natl Acad Sci U S A, 2022, 119(20): e2203024119.
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10.	Avigan P D, Cammack K, Shapiro M L. Flexible spatial learning requires both the dorsal and ventral hippocampus and their functional interactions with the prefrontal cortex. Hippocampus, 2020, 30(7): 733-744.
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12.	Pinotsis D A, Geerts J P, Pinto L, et al. Linking canonical microcircuits and neuronal activity: dynamic causal modelling of laminar recordings. Neuroimage, 2017, 146: 355-366.
13.	Cooray G K, Sengupta B, Douglas P, et al. Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling. Neuroimage, 2015, 118: 508-519.
14.	徐静, 李德民, 聂彬彬, 等. 运用动态因果模型探究精神分裂症异常有效连接. 中国临床心理学杂志, 2014, 22(6): 981-984.
15.	Symmonds M, Moran C H, Leite M I, et al. Ion channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis. Brain, 2018, 141(6): 1691-1702.
16.	Moran R J, Jones M W, Blockeel A J, et al. Losing control under ketamine: suppressed cortico-hippocampal drive following acute ketamine in rats. Neuropsychopharmacology, 2015, 40(2): 268-277.
17.	Friston K, Moran R J, Nagai Y, et al. World model learning and inference. Neural Networks, 2021, 144: 573-590.
18.	Friston K J, Preller K H, Mathys C, et al. Dynamic causal modelling revisited. Neuroimage, 2019, 199: 730-744.
19.	Kahan J, Foltynie T. Understanding DCM: ten simple rules for the clinician. Neuroimage, 2013, 83: 542-549.
20.	Zarghami T S, Friston K J. Dynamic effective connectivity. Neuroimage, 2020, 207: 116453.
21.	Li S, Bai W, Liu T, et al. Increases of theta-low gamma coupling in rat medial prefrontal cortex during working memory task. Brain Research Bulletin, 2012, 89(3-4): 115-123.
22.	张天恒, 郭苗苗, 徐桂芝, 等. 间歇性θ节律经颅磁刺激改善大鼠工作记忆的海马与前额叶跨脑区神经网络效应研究. 生物化学与生物物理进展, 2022, 49(8): 1573-1585.
23.	李双燕, 岳宣雅, 王龙龙, 等. 基于神经元集群模型的动态因果模型算法综述. 中国生物医学工程学报, 2023, 42(6): 740-749.
24.	张挺, 陆军, 郑旭媛. 基于动态因果模型的视觉工作记忆任务脑效应网络研究. 航天医学与医学工程, 2017, 30(2): 123-127.
25.	岳宣雅.基于动态因果模型的睡眠剥夺对大鼠工作记忆影响的研究. 天津: 河北工业大学, 2022.
26.	Fastenrath M, Friston K J, Kiebel S J. Dynamical causal modelling for M/EEG: spatial and temporal symmetry constraints. Neuroimage, 2009, 44(1): 154-163.
27.	David O, Harrison L, Friston K J. Modelling event-related responses in the brain. Neuroimage, 2005, 25(3): 756-770.
28.	Marreiros A C, Kiebel S J, Daunizeau J, et al. Population dynamics under the Laplace assumption. Neuroimage, 2009, 44(3): 701-714.
29.	Deco G, Tononi G, Boly M, et al. Rethinking segregation and integration: contributions of whole-brain modelling. Nature Reviews Neuroscience, 2015, 16(7): 430-439.
30.	Eichenbaum H. Prefrontal–hippocampal interactions in episodic memory. Nature Reviews Neuroscience, 2017, 18(9): 547-558.
31.	Stout J J, Hallock H L, George A E, et al. The ventral midline thalamus coordinates prefrontal-hippocampal neural synchrony during vicarious trial and error. Scientific Reports, 2022, 12(1): 10940.
32.	Dickson C R, Holmes G L, Barry J M. Dynamic θ frequency coordination within and between the prefrontal cortex-hippocampus circuit during learning of a spatial avoidance task. eNeuro, 2022, 9(2): 0414-21.
33.	Lisman J, Cooper K, Sehgal M, et al. Memory formation depends on both synapse-specific modifications of synaptic strength and cell-specific increases in excitability. Nature Neuroscience, 2018, 21(3): 309-314.
34.	Park A J, Harris A Z, Martyniuk K M, et al. Reset of hippocampal-prefrontal circuitry facilitates learning. Nature. 2021, 591(7851): 615-619.
35.	Rajasethupathy P, Sankaran S, Marshel J H, et al. Projections from neocortex mediate top-down control of memory retrieval. Nature, 2015, 526(7575): 653-659.
36.	Pereira I, Frässle S, Heinzle J, et al. Conductance-based dynamic causal modeling: a mathematical review of its application to cross-power spectral densities. Neuroimage. 2021, 245: 118662.

1. FeldmanHall O, Montez D F, Phelps E A, et al. Hippocampus guides adaptive learning during dynamic social interactions. Journal of Neuroscience, 2021, 41(6): 1340-1348.
2. Rushworth M F, Noonan M P, Boorman E D, et al. Frontal cortex and reward-guided learning and decision-making. Neuron, 2011, 70(6): 1054-1069.
3. Friston K J. Functional and effective connectivity: a review. Brain Connectivity, 2011, 1(1): 13-36.
4. Benchenane K, Peyrache A, Khamassi M, et al. Coherent theta oscillations and reorganization of spike timing in the hippocampal-prefrontal network upon learning. Neuron, 2010, 66(6): 921-936.
5. 王雪玲, 王伊萌, 杨佳佳, 等. 记忆水平依赖的海马-前额叶神经节律交互. 生物化学与生物物理进展, 2021, 48(8): 907-921.
6. Vertes R P. Interactions among the medial prefrontal cortex, hippocampus and midline thalamus in emotional and cognitive processing in the rat. Neuroscience, 2006, 142(1): 1-20.
7. Liu T, Bai W, Xia M, et al. Directional hippocampal-prefrontal interactions during working memory. Behavioural Brain Research, 2018, 338: 1-8.
8. Morici J F, Weisstaub N V, Zold C L. Hippocampal-medial prefrontal cortex network dynamics predict performance during retrieval in a context-guided object memory task. Proc Natl Acad Sci U S A, 2022, 119(20): e2203024119.
9. Alemany-González M, Gener T, Nebot P, et al. Prefrontal–hippocampal functional connectivity encodes recognition memory and is impaired in intellectual disability. Proc Natl Acad Sci U S A, 2020, 117(21): 11788-11798.
10. Avigan P D, Cammack K, Shapiro M L. Flexible spatial learning requires both the dorsal and ventral hippocampus and their functional interactions with the prefrontal cortex. Hippocampus, 2020, 30(7): 733-744.
11. Friston K J, Harrison L, Penny W. Dynamic causal modelling. Neuroimage, 2003, 19(4): 1273-1302.
12. Pinotsis D A, Geerts J P, Pinto L, et al. Linking canonical microcircuits and neuronal activity: dynamic causal modelling of laminar recordings. Neuroimage, 2017, 146: 355-366.
13. Cooray G K, Sengupta B, Douglas P, et al. Characterising seizures in anti-NMDA-receptor encephalitis with dynamic causal modelling. Neuroimage, 2015, 118: 508-519.
14. 徐静, 李德民, 聂彬彬, 等. 运用动态因果模型探究精神分裂症异常有效连接. 中国临床心理学杂志, 2014, 22(6): 981-984.
15. Symmonds M, Moran C H, Leite M I, et al. Ion channels in EEG: isolating channel dysfunction in NMDA receptor antibody encephalitis. Brain, 2018, 141(6): 1691-1702.
16. Moran R J, Jones M W, Blockeel A J, et al. Losing control under ketamine: suppressed cortico-hippocampal drive following acute ketamine in rats. Neuropsychopharmacology, 2015, 40(2): 268-277.
17. Friston K, Moran R J, Nagai Y, et al. World model learning and inference. Neural Networks, 2021, 144: 573-590.
18. Friston K J, Preller K H, Mathys C, et al. Dynamic causal modelling revisited. Neuroimage, 2019, 199: 730-744.
19. Kahan J, Foltynie T. Understanding DCM: ten simple rules for the clinician. Neuroimage, 2013, 83: 542-549.
20. Zarghami T S, Friston K J. Dynamic effective connectivity. Neuroimage, 2020, 207: 116453.
21. Li S, Bai W, Liu T, et al. Increases of theta-low gamma coupling in rat medial prefrontal cortex during working memory task. Brain Research Bulletin, 2012, 89(3-4): 115-123.
22. 张天恒, 郭苗苗, 徐桂芝, 等. 间歇性θ节律经颅磁刺激改善大鼠工作记忆的海马与前额叶跨脑区神经网络效应研究. 生物化学与生物物理进展, 2022, 49(8): 1573-1585.
23. 李双燕, 岳宣雅, 王龙龙, 等. 基于神经元集群模型的动态因果模型算法综述. 中国生物医学工程学报, 2023, 42(6): 740-749.
24. 张挺, 陆军, 郑旭媛. 基于动态因果模型的视觉工作记忆任务脑效应网络研究. 航天医学与医学工程, 2017, 30(2): 123-127.
25. 岳宣雅.基于动态因果模型的睡眠剥夺对大鼠工作记忆影响的研究. 天津: 河北工业大学, 2022.
26. Fastenrath M, Friston K J, Kiebel S J. Dynamical causal modelling for M/EEG: spatial and temporal symmetry constraints. Neuroimage, 2009, 44(1): 154-163.
27. David O, Harrison L, Friston K J. Modelling event-related responses in the brain. Neuroimage, 2005, 25(3): 756-770.
28. Marreiros A C, Kiebel S J, Daunizeau J, et al. Population dynamics under the Laplace assumption. Neuroimage, 2009, 44(3): 701-714.
29. Deco G, Tononi G, Boly M, et al. Rethinking segregation and integration: contributions of whole-brain modelling. Nature Reviews Neuroscience, 2015, 16(7): 430-439.
30. Eichenbaum H. Prefrontal–hippocampal interactions in episodic memory. Nature Reviews Neuroscience, 2017, 18(9): 547-558.
31. Stout J J, Hallock H L, George A E, et al. The ventral midline thalamus coordinates prefrontal-hippocampal neural synchrony during vicarious trial and error. Scientific Reports, 2022, 12(1): 10940.
32. Dickson C R, Holmes G L, Barry J M. Dynamic θ frequency coordination within and between the prefrontal cortex-hippocampus circuit during learning of a spatial avoidance task. eNeuro, 2022, 9(2): 0414-21.
33. Lisman J, Cooper K, Sehgal M, et al. Memory formation depends on both synapse-specific modifications of synaptic strength and cell-specific increases in excitability. Nature Neuroscience, 2018, 21(3): 309-314.
34. Park A J, Harris A Z, Martyniuk K M, et al. Reset of hippocampal-prefrontal circuitry facilitates learning. Nature. 2021, 591(7851): 615-619.
35. Rajasethupathy P, Sankaran S, Marshel J H, et al. Projections from neocortex mediate top-down control of memory retrieval. Nature, 2015, 526(7575): 653-659.
36. Pereira I, Frässle S, Heinzle J, et al. Conductance-based dynamic causal modeling: a mathematical review of its application to cross-power spectral densities. Neuroimage. 2021, 245: 118662.

Journal of Biomedical Engineering

A study on the effects of learning on the properties of rats hippocampal-prefrontal connections in a memory task

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