内侧前额皮层−伏隔核环路在决策冲动中的作用：基于动物模型的研究

doi:10.3724/SP.J.1041.2023.00556

摘要/Abstract

摘要：

注意缺陷多动障碍(attention deficit/hyperactivity disorder, ADHD)行为控制不足与决策冲动密切相关, 后者受内侧前额皮层(medial prefrontal cortex, mPFC)与伏隔核(nucleus accumbens, NAc)调节。为调查ADHD决策冲动与mPFC-NAc间功能耦合的关系, 研究采用ADHD模型SHR (spontaneously hypertensive rat, SHR)大鼠, 结合延迟折扣任务和在体电生理, 研究发现, 与对照Wistar (WIS)大鼠相比, SHR大鼠对延迟大奖赏的选择百分比降低; WIS大鼠mPFC-NAc的Theta频段相干值表现为延迟选择时显著大于立即选择时、首次选择时大于连续选择时、转换试次时大于连续试次时, 而SHR大鼠在上述条件均低于WIS大鼠。回归分析发现mPFC-NAc的相干差值与延迟大奖赏选择率显著正相关。结果表明mPFC-NAc间功能联系减弱是ADHD决策冲动缺陷的重要环路基础, 该缺陷与其深度信息加工以及策略转换能力受损有关, 扩展了ADHD决策冲动的认知和神经机制的认识。

关键词: 注意缺陷多动障碍, 决策冲动, 内侧前额皮层, 伏隔核, 神经振荡

Abstract:

Insufficient behavior control in patients with attention deficit / hyperactivity disorder (ADHD) is closely related to decision impulsivity, which is regulated by medial prefrontal cortex (mPFC) and nucleus accumbens (NAc). In ADHD patients, the mPFC and NAc show abnormality both structurally and functionally, indicating that these two brain regions are involved in regulation of decision-making, especially impulsivity. Although extensive anatomical connections between mPFC and NAc have been found, the role of mPFC-NAc circuit in decision impulsivity remains to be investigated.

Wistar (WIS) rats and ADHD rats (SHR, spontaneously hypertensive rat) were used as subjects of this study. We recorded the local field potential (LFP) of mPFC and NAc using multi-channel electrophysiology during a delay discounting task (DDT). We further analyzed the coherence difference of Theta (4~12 Hz) oscillation in expectation period (0~3s) and compared this measure between the two groups.

Results: (1) SHR rats had higher decision impulsivity level than WIS group. Power spectral density between 6~12 Hz of LFP in mPFC and NAc increased in both groups. (2) When choosing large/delayed rewards, coherence of mPFC-NAc activity increased compared to small/immediate rewards in WIS group. This indicates that the mPFC-NAc circuit mediates decision impulsivity. (3) When choosing large/delayed rewards, mPFC-NAc activity in SHR group showed lower coherence than WIS group, indicating SHR rats have weaker mPFC-NAc functional connections. (4) Coherence of mPFC-NAc activity is higher during initial choice behavioral than continuous choice behavior. It indicated that stronger mPFC-NAc functional connections are required during controlled information processing (dominant in initial choice behavioral), rather than automatic information processing (dominant in continuous choice behavior). Coherence of mPFC-NAc activity is higher in WIS group than SHR group when choosing large/immediate rewards. It indicated that the decision impulsivity deficits in SHR rats results from mPFC-NAc weak functional connections. (5) Coherence of mPFC-NAc activity is higher in shift trials than continuous trials. Plus, WIS group showed an overall higher coherence than SHR group. This indicated that mPFC-NAc circuit heavily involves in controlled information processing, and SHR group has deficiency of this process. (6) Regression analysis showed that coherence difference of mPFC-NAc activity in prediction period has positive correlation with delayed large reward selection rate in WIS group, that is, the more coherence of mPFC-NAc Theta activity increased during prediction period, the less decision impulsivity WIS rats behaved during choice period. However, the coherence difference cannot predict decision impulsivity in SHR group.

Conclusion: mPFC-NAc heavily involves in decision impulsivity. Increase of coherence of mPFC-NAc theta oscillation in prediction period can predict impulsivity level. The study revealed that weak mPFC-NAc functional connections mediate decision impulsivity in ADHD and brought new perspectives in the treatment of ADHD.

Key words: attention deficit/hyperactivity disorder, decision impulsivity, mPFC, NAc, neural network oscillation

中图分类号:

B845

卓利楠, 曾祥玉, 吴冰, 牛荣荣, 于萍, 王玮文. (2023). 内侧前额皮层−伏隔核环路在决策冲动中的作用：基于动物模型的研究. 心理学报, 55(4), 556-571.

ZHUO Linan, ZENG Xiangyu, WU Bing, NIU Rongrong, YU Ping, WANG Weiwen. (2023). The function of mPFC-NAc circuit in decision impulsivity: A study based on an animal model. Acta Psychologica Sinica, 55(4), 556-571.

图/表 4

图1 实验程序 A. 实验流程图。B. DDT任务流程图。C和D. 分别为mPFC和NAc电极植入位点示意图和尼氏染色图。

图2 WIS与SHR大鼠的行为学和电生理活动。A. SHR组自发活动量显著高于WIS组。B. (i)在DDT任务中, 随着延迟时间的增加两组大鼠对延迟大奖赏选择百分比逐渐降低, 延迟时间为0 s时, SHR大鼠对延迟大奖赏的选择率与WIS组大鼠相比无差异, 延迟时间为10 s、20 s时, SHR大鼠选择率均低于WIS组大鼠。(ii) 比较两组大鼠在延迟10 s条件下的直线下降斜率, 发现SHR大鼠下降幅度明显高于对照组WIS大鼠。C. 决策过程中两组大鼠的mPFC和NAc在6~12 Hz频段的功率谱变化。横坐标是频率, 纵坐标是功率谱大小, 单位是log of PSD (dB)。D. 两组大鼠的mPFC和NAc在基线期与预期期6~12 Hz频段功率谱比较。E. 两组大鼠的mPFC和NAc的时−频分析功率谱(WIS, n=8; SHR, n=8)。F. mPFC-NAc的Theta频段相干值随着时间的变化情况。横坐标以线索呈现时刻点为0点, 灰色方框表示预期期(0~3 s)。G. 预期期mPFC-NAc的Theta频段相干值统计直方图。H. mPFC-NAc的Theta频段相干值的时−频分析图(WIS, n=8; SHR, n=8)。M ± SE, *表示p < 0.05, **表示p < 0.01, ***表示p < 0.001。

图3 首次和连续选择时mPFC-NAc局部场电位的Theta频段相干值。A. 在选择立即小奖赏时, 两组大鼠首次选择和连续选择时的mPFC-NAc局部场电位Theta频段相干值随着时间的变化情况。横坐标以线索呈现时刻点为0点, 灰色方框表示预期期(0~3 s)。B. 两因素重复测量方差分析显示, WIS组和SHR组首次选择时的mPFC-NAc Theta频段相干值均显著高于连续选择时。首次选择时两组大鼠之间均没有显著差异, 而在连续选择时, SHR组的mPFC-NAc Theta频段相干值显著低于WIS组。C. mPFC-NAc两个脑区Theta频段相干值的时−频分析图(WIS, n=8; SHR, n=8)。D. 在选择延迟大奖赏时, 两组大鼠首次选择和连续选择时的mPFC-NAc局部场电位Theta频段相干值随着时间的变化情况。E. 两因素重复测量方差分析显示, WIS组和SHR组首次选择时的mPFC-NAc的Theta频段相干值均显著高于连续选择时, 无论是首次选择还是连续选择, SHR组的mPFC-NAc的Theta频段相干值均显著低于WIS组。F. mPFC-NAc的Theta频段相干值的时−频分析图(WIS, n=8; SHR, n=8)。M ± SE, *表示p < 0.05, ***表示p < 0.001。

图4 试次转换时mPFC-NAc局部场电位的Theta频段相干值。A. 试次转换时两组大鼠mPFC-NAc局部场电位Theta频段相干值随着时间的变化情况。横坐标以线索呈现时刻点为0点, 灰色方框表示预期期(0~3 s)。B. 两因素重复测量方差分析显示, WIS组和SHR大鼠在转换试次时的相干值均显著高于连续试次; 在转换试次和连续试次中, WIS组的相干值均显著高于SHR组。C. mPFC-NAc的Theta频段相干值的时−频分析图(WIS, n=8; SHR, n=8)。M ± SE, ***表示p < 0.001。D. WIS组大鼠mPFC与NAc局部场电位Theta频段的相干差值与延迟大奖赏选择率呈显著正相关。E. SHR大鼠mPFC与NAc局部场电位的相干差值与延迟大奖赏选择率之间不存在显著相关。

参考文献 49

[1]	American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association.
[2]	Aparicio, C. F., Hennigan, P. J., Mulligan, L. J., & Alonso- Alvarez, B. (2019). Spontaneously hypertensive (SHR) rats choose more impulsively than Wistar-Kyoto (WKY) rats on a delay discounting task. Behavioural Brain Research, 364, 480-493. doi: S0166-4328(17)31084-7 pmid: 28963043
[3]	Asher, A., & Lodge, D. J. (2012). Distinct prefrontal cortical regions negatively regulate evoked activity in nucleus accumbens subregions. International Journal of Neuropsychopharmacology, 15(9), 1287-1294. doi: 10.1017/S146114571100143X URL
[4]	Balleine, B. W., & O’Doherty, J. P. (2010). Human and rodent homologies in action control:Corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 35(1), 48-69.
[5]	Bartolo, R., & Averbeck, B. B. (2020). Prefrontal cortex predicts state switches during reversal learning. Neuron, 106(6), 1044-1054. doi: S0896-6273(20)30231-2 pmid: 32315603
[6]	Basar, K., Sesia, T., Groenewegen, H., Steinbusch, H. W., Visser-Vandewalle, V., & Temel, Y. (2010). Nucleus accumbens and impulsivity. Progress in Neurobiology, 92(4), 533-557. doi: 10.1016/j.pneurobio.2010.08.007 pmid: 20831892
[7]	Bossert, J. M., Stern, A. L., Theberge, F., Marchant, N. J., Wang, H. L., Morales, M., & Shaham, Y. (2012). Role of projections from ventral medial prefrontal cortex to nucleus accumbens shell in context-induced reinstatement of heroin seeking. Journal of Neuroscience, 32(14), 4982-4991. doi: 10.1523/JNEUROSCI.0005-12.2012 pmid: 22492053
[8]	Carpenter, K. M., Bedi, G., & Vadhan, N. P. (2015). Understanding and shifting drug-related decisions: contributions of automatic decision-making processes. Current Psychiatry Reports, 17(8), 607. doi: 10.1007/s11920-015-0607-8 pmid: 26084667
[9]	Charan, J., & Kantharia, N. D. (2013). How to calculate sample size in animal studies? Journal of Pharmacology and Pharmacotherapeutics, 4(4), 303-306. doi: 10.4103/0976-500X.119726 pmid: 24250214
[10]	Cohen, M. X., Axmacher, N., Lenartz, D., Elger, C. E., Sturm, V., & Schlaepfer, T. E. (2009). Nuclei accumbens phase synchrony predicts decision-making reversals following negative feedback. The Journal of Neuroscience, 29(23), 7591-7598. doi: 10.1523/JNEUROSCI.5335-08.2009 URL
[11]	Christiansen, R. E., Roald, A. B., Tenstad, O., & Iversen, B. M. (2002). Renal hemodynamics during development of hypertension in young spontaneously hypertensive rats. Kidney Blood Pressure Research, 25(5), 322-328. doi: 10.1159/000066792 URL
[12]	Donnelly, N. A., Holtzman, T., Rich, P. D., Nevado-Holgado, A. J., Fernando, A. B., van Dijck, G., … Dalley, J. W. (2014). Oscillatory activity in the medial prefrontal cortex and nucleus accumbens correlates with impulsivity and reward outcome. PLoS ONE, 9(10), e111300.
[13]	Erdeniz, B., & Done, J. (2020). Towards automaticity in reinforcement learning: A model-based functional magnetic resonance imaging study. Archives of Neuropsychiatry, 57(2), 98-107.
[14]	Faraone, S. V., Asherson, P., Banaschewski, T., Biederman, J., Buitelaar, J. K., Ramos-Quiroga, J. A., … Franke, B. (2015). Attention-deficit/hyperactivity disorder. Nature Reviews Disease Primers, 1, 15020. doi: 10.1038/nrdp.2015.20 pmid: 27189265
[15]	Floresco, S. B. (2015). The nucleus accumbens: An interface between cognition, emotion, and action. Annual Review Psychology, 66, 25-52. doi: 10.1146/psych.2015.66.issue-1 URL
[16]	Fox, A. T., Hand, D. J., & Reilly, M. P. (2008). Impulsive choice in a rodent model of attention-deficit/hyperactivity disorder. Behavioural Brain Research, 187(1), 146-152. pmid: 17950930
[17]	Friston, K. J., Bastos, A. M., Pinotsis, D., & Litvak, V. (2015). LFP and oscillations-what do they tell us? Current Opinion in Neurobiology, 31, 1-6. doi: 10.1016/j.conb.2014.05.004 pmid: 25079053
[18]	Gauthier, J. M., Tassin, D. H., Dwoskin, L. P., & Kantak, K. M. (2014). Effects of dopamine D1 receptor blockade in the prelimbic prefrontal cortex or lateral dorsal striatum on frontostriatal function in wistar and spontaneously hypertensive rats. Behavioural Brain Research, 268, 229-238. doi: 10.1016/j.bbr.2014.04.018 pmid: 24755309
[19]	Gui, D. Y., Yu, T., Hu, Z., Yan, J., & Li, X. (2018). Dissociable functional activities of cortical theta and beta oscillations in the lateral prefrontal cortex during intertemporal choice. Scientific Reports, 8(1), 11233. doi: 10.1038/s41598-018-21150-1
[20]	Hauser, T. U., Iannaccone, R., Ball, J., Mathys, C., Brandeis, D., Walitza, S., & Brem, S. (2014). Role of the medial prefrontal cortex in impaired decision making in juvenile attention-deficit/hyperactivity disorder. JAMA Psychiatry, 71(10), 1165-1173. doi: 10.1001/jamapsychiatry.2014.1093 pmid: 25142296
[21]	Jackson, J. N. S., & MacKillop, J. (2016). Attention-deficit/ hyperactivity disorder and monetary delay discounting: A meta-analysis of case-control studies. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1(4), 316-325. doi: 10.1016/j.bpsc.2016.01.007 URL
[22]	Jenni, N. L., Larkin, J. D., & Floresco, S. B. (2017). Prefrontal dopamine D1 and D2 receptors regulate dissociable aspects of decision making via distinct ventral striatal and amygdalar circuits. The Journal of Neuroscience, 37(26), 6200-6213. doi: 10.1523/JNEUROSCI.0030-17.2017 URL
[23]	Kable, J. W., & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nature Neuroscience, 10(12), 1625-1633. doi: 10.1038/nn2007 pmid: 17982449
[24]	Khader, P. H., Pachur, T., Weber, L. A., & Jost, K. (2016). Neural signatures of controlled and automatic retrieval processes in memory-based decision-making. Journal of Cognitive Neuroscience, 28(1), 69-83. doi: 10.1162/jocn_a_00882 pmid: 26401812
[25]	Kim, S., & Lee, D. (2011). Prefrontal cortex and impulsive decision making. Biological Psychiatry, 69(12), 1140-1146. doi: 10.1016/j.biopsych.2010.07.005 pmid: 20728878
[26]	Li, Y., Wang, X., Li, N., Qu, L., Wang, P., Ge, S. N., & Wang, X. L. (2020). The NAc lesions disrupted the hippocampus- mPFC theta coherence during intravenous cocaine administration in rats. Neuroscience Letters, 729, 134986. doi: 10.1016/j.neulet.2020.134986 URL
[27]	Lv, C., Wang, Q., Chen, C., Qiu, J., Xue, G., & He, Q. (2019). The regional homogeneity patterns of the dorsal medial prefrontal cortex predict individual differences in decision impulsivity. NeuroImage, 200, 556-561. doi: S1053-8119(19)30583-X pmid: 31295568
[28]	Marx, I., Hacker, T., Yu, X., Cortese, S., & Sonuga-Barke, E. (2018). ADHD and the choice of small immediate over larger delayed rewards: A comparative meta-analysis of performance on simple choice-delay and temporal discounting paradigms. Journal Attention Disorders, 25(2), 171-187. doi: 10.1177/1087054718772138 URL
[29]	Miller, E. M., Pomerleau, F., Huettl, P., Gerhardt, G. A., & Glaser, P. E. (2014). Aberrant glutamate signaling in the prefrontal cortex and striatum of the spontaneously hypertensive rat model of attention-deficit/hyperactivity disorder. Psychopharmacology, 231(15), 3019-3029. doi: 10.1007/s00213-014-3479-4 pmid: 24682500
[30]	Miyazaki, K., Miyazaki, K. W., & Matsumoto, G. (2004). Different representation of forthcoming reward in nucleus accumbens and medial prefrontal cortex. Neuroreport, 15(4), 721-726. pmid: 15094484
[31]	Moorman, D. E., & Aston-Jones, G. (2015). Prefrontal neurons encode context-based response execution and inhibition in reward seeking and extinction. Proceedings of the National Academy of Sciences, 112(30), 9472-9477. doi: 10.1073/pnas.1507611112 URL
[32]	Narayanan, N. S., Cavanagh, J. F., Frank, M. J., & Laubach, M. (2013). Common medial frontal mechanisms of adaptive control in humans and rodents. Nature Neuroscience, 16(12), 1888-1895. doi: 10.1038/nn.3549 pmid: 24141310
[33]	Orduña, V., & Mercado, E. (2017). Impulsivity in spontaneously hypertensive rats: Within-subjects comparison of sensitivity to delay and to amount of reinforcement. Behavioural Brain Research, 328, 178-185. doi: S0166-4328(17)30172-9 pmid: 28435126
[34]	Paxinos, G, A, .,& Watson.,, C. (2004). The rat brain atlas in stereotaxic coordinates. San Diego: Academic.
[35]	Pérez-Díaz, F., Díaz, E., Sánchez, N., Vargas, J. P., Pearce, J. M., & López, J. (2017). Different involvement of medial prefrontal cortex and dorso-lateral striatum in automatic and controlled processing of a future conditioned stimulus. PLoS ONE, 12(12), e0189630.
[36]	Piray, P., Toni, I., & Cools, R. (2016). Human choice strategy varies with anatomical projections from ventromedial prefrontal cortex to medial striatum. Journal of Neuroscience, 36(10), 2857-2867. doi: 10.1523/JNEUROSCI.2033-15.2016 pmid: 26961942
[37]	Robbins, T. W., & Dalley, J. W. (2017). Impulsivity, risky choice, and impulse control disorders:Animal models. In J-C. Dreher & L. Tremblay (Eds.), Decision neuroscience (pp. 81-93). San Diego, CA: Academic Press.
[38]	Sackett, D. A., Moschak, T. M., & Carelli, R. M. (2019). Prelimbic cortical neurons track preferred reward value and reflect impulsive choice during delay discounting behavior. The Journal of Neuroscience, 39(16), 3108-3118. doi: 10.1523/JNEUROSCI.2532-18.2019 URL
[39]	Salavert, J., Ramos-Quiroga, J. A., Moreno-Alcázar, A., Caseras, X., Palomar, G., Radua, J., … Pomarol-Clotet, E. (2018). Functional imaging changes in the medial prefrontal cortex in adult ADHD. Journal of Attention Disorders, 22(7), 679-693. doi: 10.1177/1087054715611492 pmid: 26515892
[40]	Scheres, A., Milham, M. P., Knutson, B., & Castellanos, F. X. (2007). Ventral striatal hyporesponsiveness during reward anticipation in attention-deficit/hyperactivity disorder. Biological Psychiatry, 61(5), 720-724. doi: 10.1016/j.biopsych.2006.04.042 pmid: 16950228
[41]	Somkuwar, S. S., Kantak, K. M., Bardo, M. T., & Dwoskin, L. P. (2016). Adolescent methylphenidate treatment differentially alters adult impulsivity and hyperactivity in the spontaneously hypertensive rat model of ADHD. Pharmacology Biochemistry and Behavior, 141, 66-77. doi: 10.1016/j.pbb.2015.12.002 URL
[42]	Sonuga-Barke, E., & Fairchild, G. (2012). Neuroeconomics of attention-deficit/hyperactivity disorder: Differential influences of medial, dorsal, and ventral prefrontal brain networks on suboptimal decision making? Biological Psychiatry, 72(2), 126-133. doi: 10.1016/j.biopsych.2012.04.004 pmid: 22560046
[43]	Starkweather, C. K., Gershman, S. J., & Uchida, N. (2018). The medial prefrontal cortex shapes dopamine reward prediction errors under state uncertainty. Neuron, 98(3), 616-629. doi: S0896-6273(18)30242-3 pmid: 29656872
[44]	Steele, C. C., Peterson, J. R., Marshall, A. T., Stuebing, S. L., & Kirkpatrick, K. (2018). Nucleus accumbens core lesions induce sub-optimal choice and reduce sensitivity to magnitude and delay in impulsive choice tasks. Behavioural Brain Research, 339, 28-38. doi: S0166-4328(17)31135-X pmid: 29146281
[45]	Vanderveldt, A., Oliveira, L., & Green, L. (2016). Delay discounting: Pigeon, rat, human--does it matter? Journal Experiment Psychology Animal Learn Cognitive, 42(2), 141-162.
[46]	Wang, Q., Lv, C., He, Q., & Xue, G. (2020). Dissociable fronto-striatal functional networks predict choice impulsivity. Brain Structure Function, 225(8), 2377-2386. doi: 10.1007/s00429-020-02128-0
[47]	Wang, Z., Yue, L., Cui, C., Liu, S., Wang, X., Li, Y., & Ma, L. (2019). Top-down control of the medial orbitofrontal cortex to nucleus accumbens core pathway in decisional impulsivity. Brain Structure Function, 224(7), 2437-2452. doi: 10.1007/s00429-019-01913-w
[48]	Womelsdorf, T., Schoffelen, J. M., Oostenveld, R., Singer, W., Desimone, R., & Engel, A. K. (2007). Modulation of neuronal interactions through neuronal synchronization. Science, 316(5831), 1609-1612. doi: 10.1126/science.1139597 pmid: 17569862
[49]	Zhao, W. J., Diederich, A., Trueblood, J. S., & Bhatia, S. (2019). Automatic biases in intertemporal choice. Psychonmic Bulletin Review, 26(2), 661-668.