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Modeling nucleus accumbens

A Computational Model from Single Cell to Circuit Level

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Abstract

Nucleus accumbens is part of the neural structures required for reward based learning and cognitive processing of motivation. Understanding its cellular dynamics and its role in basal ganglia circuits is important not only in diagnosing behavioral disorders and psychiatric problems as addiction and depression but also for developing therapeutic treatments for them. Building a computational model would expand our comprehension of nucleus accumbens. In this work, we are focusing on establishing a model of nucleus accumbens which has not been considered as much as dorsal striatum in computational neuroscience. We will begin by modeling the behavior of single cells and then build a holistic model of nucleus accumbens considering the effect of synaptic currents. We will verify the validity of the model by showing the consistency of simulation results with the empirical data. Furthermore, the simulation results reveal the joint effect of cortical stimulation and dopaminergic modulation on the activity of medium spiny neurons. This effect differentiates with the type of dopamine receptors.

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  1. https://github.com/rahmielibol/ModellingNAcc

References

  • Yelnik, J. (2002). Functional anatomy of the basal ganglia. Movement disorders, 17(S3), 15–S21.

    Google Scholar 

  • Alexander, G.E., & Crutcher, M.D. (1990). Functional architecture of basal ganglia circuits: neural substrates of parallel processing. Trends in neurosciences, 13(7), 266–271.

    CAS  PubMed  Google Scholar 

  • Haber, S.N., Fudge, J.L., & McFarland, N.R. (2000). Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum. Journal of Neuroscience, 20(6), 2369–2382.

    CAS  PubMed  Google Scholar 

  • DeLong, M.R., & Wichmann, T. (2007). Circuits and circuit disorders of the basal ganglia. Archives of neurology, 64(1), 20–24.

    PubMed  Google Scholar 

  • Halje, P., Brys, I., Mariman, J.J., da Cunha, C., Fuentes, R., & Petersson, P. (2019). Oscillations in cortico-basal ganglia circuits: implications for Parkinson’s disease and other neurologic and psychiatric conditions. Journal of Neurophysiology, 122(1), 203–231.

    PubMed  Google Scholar 

  • Neto, L.L., Oliveira, E., Correia, F., & Ferreira, A.G. (2008). The human nucleus accumbens: Where is it? A stereotactic, anatomical and magnetic resonance imaging study. Neuromodulation: Technology at the Neural Interface, 11(1), 13–22.

    Google Scholar 

  • Salgado, S., & Kaplitt, M.G. (2015). The nucleus accumbens: a comprehensive review. Stereotact Funct Neurosurg, 93, 75–93.

    PubMed  Google Scholar 

  • Prensa, L., Richard, S., & Parent, A. (2003). Chemical anatomy of the human ventral striatum and adjacent basal forebrain structures. The Journal of Comparative Neurology, 460(3), 345–367.

    PubMed  Google Scholar 

  • Mavridis, I., Boviatsis, E., & Anagnostopoulou, S. (2011). Anatomy of the human nucleus accumbens: a combined morphometric study. Surgical and Radiologic Anatomy, 33(5), 405–414.

    PubMed  Google Scholar 

  • Lucas-Neto, L., Neto, D., Oliveira, E., Martins, H., Mourato, B., Correia, F., Rainha-Campos, A., & Gonçalves-Ferreira, A. (2013). Three dimensional anatomy of the human nucleus accumbens. Acta Neurochirurgica., 155(12), 2389–2398.

    CAS  PubMed  Google Scholar 

  • Pijnenburg, A.J., & Van Rossum, J.M. (1973). Stimulation of locomotor activity following injection of dopamine into the nucleus accumbens. Journal of Pharmacy and Pharmacology, 25, 1003–1005.

    CAS  PubMed  Google Scholar 

  • Salamone, J.D. (1994). The involvement of nucleus accumbens dopamine in appetitive and aversive motivation. Behavioural brain research, 61(2), 117–133.

    CAS  PubMed  Google Scholar 

  • Yohn, S.E., Galbraith, J., Calipari, E.S., Conn, P.J., & Behavioral, Shared. (2019). Neurocircuitry disruptions in drug addiction obesity, and binge eating disorder: focus on group i mglurs in the mesolimbic dopamine pathway. ACS chemical neuroscience, 10(5), 2125–2143.

    CAS  PubMed  Google Scholar 

  • Taylor, J.G., & Taylor, N.R. (2000). Analysis of recurrent cortico-basal ganglia-thalamic loops for working memory. Biological Cybernetics, 82(5), 415–432.

    CAS  PubMed  Google Scholar 

  • Gurney, K., Prescott, T.J., & Redgrave, P. (2001). A computational model of action selection in the basal ganglia. i. a new functional anatomy. Biological cybernetics, 84(6), 401–410.

    CAS  PubMed  Google Scholar 

  • Gurney, K., Prescott, T.J., & Redgrave, P. (2001). A computational model of action selection in the basal ganglia. II. Analysis and simulation of behaviour. Biological cybernetics., 84(6), 411–423.

    CAS  PubMed  Google Scholar 

  • Sengor, N.S., & Karabacak, O. (2015). A computational model revealing the effect of dopamine on action selection. arXiv preprint arXiv:1512.05340.

  • Guthrie, M., Myers, C.E., & Gluck, M.A. (2009). A neurocomputational model of tonic and phasic dopamine in action selection: a comparison with cognitive deficits in Parkinson’s disease. Behavioural brain research, 200(1), 48–59.

    CAS  PubMed  PubMed Central  Google Scholar 

  • O’Reilly, R.C., & Frank, M.J. (2006). Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia. Neural computation, 18(2), 283–328.

    PubMed  Google Scholar 

  • Celikok, U., & Navarro-López, E M. (2016). SengorNS, A computational model describing the interplay of basal ganglia and subcortical background oscillations during working memory processes. arXiv preprint arXiv:1601.07740.

  • Berns, G., & Sejnowski, T. (1994). A model of basal ganglia function unifying reinforcement learning and action selection. Joint Symposium on Neural Computation, pp 129–148.

  • Schultz, W., Dayan, P., & Montague, P.R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599.

    CAS  PubMed  Google Scholar 

  • Suri, R.E., & Schultz, W. (1998). Learning of sequential movements by neural network model with dopamine-like reinforcement signal. Experimental Brain Research, 121(3), 350–354.

    CAS  PubMed  Google Scholar 

  • Dayan, P., & Balleine, B.W. (2002). Reward, motivation, and reinforcement learning. Neuron, 36(2), 285–298.

    CAS  PubMed  Google Scholar 

  • Joel, D., Niv, Y., & Ruppin, E. (2002). Actor-critic models of the basal ganglia: New anatomical and computational perspectives. Neural networks, 15(4), 535–547.

    PubMed  Google Scholar 

  • Haruno, M., & Kawato, M. (2006). Heterarchical reinforcement-learning model for integration of multiple cortico-striatal loops: fMRI examination in stimulus-action-reward association learning. Neural Networks, 19(8), 1242–1254.

    PubMed  Google Scholar 

  • Chartove, J.A., McCarthy, M.M., Pittman-Polletta, B.R., & Kopell, N.J. (2020). A biophysical model of striatal microcircuits suggests gamma and beta oscillations interleaved at delta/theta frequencies mediate periodicity in motor control. PLoS computational biology, 16(2), p.e1007300.

    Google Scholar 

  • Hjorth, J.J., Kozlov, A., Carannante, I., Nylén, J F, Lindroos, R., Johansson, Y., Tokarska, A., Dorst, M.C., Suryanarayana, S.M., Silberberg, G., & Kotaleski, J.H. (2020). The microcircuits of striatum in silico. Proceedings of the National Academy of Sciences, 117(17), 9554–9565.

    Google Scholar 

  • Girard, B., Lienard, J., Gutierrez, C.E., Delord, B., & Doya, K. (2020). A biologically constrained spiking neural network model of the primate basal ganglia with overlapping pathways exhibits action selection. Eur J Neurosci., 00: 1–24. https://doi.org/10.1111/ejn.14869.

  • Erçelik, E., & Şengör, N.S. (2015). A neurocomputational model implemented on humanoid robot for learning action selection. International Joint Conference on Neural Networks (IJCNN), pp 1–6.

  • Bahuguna, J., Weidel, P., & Morrison, A. (2019). Exploring the role of striatal d1 and d2 medium spiny neurons in action selection using a virtual robotic framework. European Journal of Neuroscience, 49(6), 737–753.

    Google Scholar 

  • Azimirad, V., & Sani, M.F. (2020). Experimental study of reinforcement learning in mobile robots through spiking architecture of Thalamo-Cortico-Thalamic circuitry of mammalian brain. Robotica, 38(9), 1558–1575.

    Google Scholar 

  • Thompson, A.M., Porr, B., & Worgotter, F. (2010). Learning and reversal learning in the subcortical limbic system: a computational model. Adaptive Behavior, 18(3-4), 211–236. https://doi.org/10.1177/1059712309353612.

    Article  Google Scholar 

  • Piray, P., Keramati, M.M., Dezfouli, A., Lucas, C., & Mokri, A. (2010). Individual differences in nucleus accumbens dopamine receptors predict development of addiction-like behavior: a computational approach. Neural computation, 22(9), 2334–2368.

    PubMed  Google Scholar 

  • Wolf, J.A., Schroeder, L.F., & Finkel, L.H. (2001). Computational modeling of medium spiny projection neurons in nucleus accumbens: toward the cellular mechanisms of afferent stream integration. Proceedings of the IEEE, 89(7), 1083–1092.

    Google Scholar 

  • Wolf, J.A., Moyer, J.T., Lazarewicz, M.T., Contreras, D., Benoit-Marand, M., O’Donnell, P., & Finkel, L.H. (2005). NMDA/AMPA Ratio impacts state transitions and entrainment to oscillations in a computational model of the nucleus accumbens medium spiny projection neuron. Journal of Neuroscience., 25(40), 9080–9095.

    CAS  PubMed  Google Scholar 

  • Wolf, J.A., Moyer, J.T., & Finkel, L.H. (2005). The role of NMDA currents in state transitions of the nucleus accumbens medium spiny neuron. Neurocomputing, 65, 565–570.

    Google Scholar 

  • Moyer, J.T., Wolf, J.A., & Finkel, L.H. (2007). Effects of dopaminergic modulation on the integrative properties of the ventral striatal medium spiny neuron. Journal of Neurophysiology, 98(6), 3731–3748.

    CAS  PubMed  Google Scholar 

  • Metin, S., & Sengor, N.S. (2012). Ventral striatal pathway determines actions employed: A computational model. In Bernstein Conference.

  • Metin, S., & Sengor, N.S. (2013). Merging dorsal and ventral striatal pathway outputs of basal ganglia circuit in decision making process. BMC Neuroscience, 14(Suppl 1), P352. https://doi.org/10.1186/1471-2202-14-S1-P352.

    Article  PubMed Central  Google Scholar 

  • Steephen, J.E., & Manchanda, R. (2009). Differences in biophysical properties of nucleus accumbens medium spiny neurons emerging from inactivation of inward rectifying potassium currents. J Comput Neurosci., 27, 453.

    PubMed  Google Scholar 

  • Spiga, S., Lintas, A., Migliore, M., & Diana, M. (2010). Altered architecture and functional consequences of the mesolimbic dopamine system in cannabis dependence. Addiction Biology, 15, 266–276.

    CAS  PubMed  Google Scholar 

  • John, J., & Manchanda, R. (2011). Modulation of synaptic potentials and cell excitability by dendritic KIR and KAS channels in nucleus accumbens medium spiny neurons: a computational study. Journal of Biosciences., 36(2), 309–328.

    PubMed  Google Scholar 

  • Wolf, J.A., & Finkel, L.H. (2003). A computational model of the Nucleus accumbens: network properties and their functional implications. In First International IEEE EMBS Conference on Neural Engineering, Conference Proceedings, pp 214–217.

  • Humphries, M.D., & Prescott, T.J. (2010). The ventral basal ganglia, a selection mechanism at the crossroads of space, strategy, and reward. Progress in Neurobiology., 90(4), 385–417.

    PubMed  Google Scholar 

  • Gray, J.A., Joseph, M.H., Hemsley, D.R., Young, A.M.J., Warburton, E.C., Boulenguez, P., Grigoryan, G.A., & et al. (1995). The role of mesolimbic dopaminergic and retrohippocampal afferents to the nucleus accumbens in latent inhibition: implications for schizophrenia, Behavioural brain research 71, no 1-2: 19-IN3.

  • Di Chiara, G., Tanda, G., Bassareo, V., Pontieri, F., Acquas, E., Fenu, S., Cadoni, C., & Carboni, E. (1999). Drug addiction as a disorder of associative learning: role of nucleus accumbens shell/extended amygdala dopamine. Annals of the New York Academy of Sciences, 877(1), 461–485.

    PubMed  Google Scholar 

  • Solinas, M., Belujon, P., Fernagut, P.O., Jaber, M., & Thiriet, N. (2019). Dopamine and addiction: what have we learned from 40 years of research. Journal of Neural Transmission, 126(4), 481–516.

    CAS  PubMed  Google Scholar 

  • Deco, G., Jirsa, V.K., Robinson, P.A., Breakspear, M., & Friston, K. (2008). The dynamic brain: from spiking neurons to neural masses and cortical fields. PLos computational biology, 4(8), e1000092.

    PubMed  PubMed Central  Google Scholar 

  • Moustafa, A.A. (2017). Ed, Computational models of brain and behavior John Wiley & Sons.

  • Xue, F., & Markram, H. (2019). A brief history of simulation neuroscience. Frontiers in neuroinformatics, 13, 32.

    Google Scholar 

  • Stimberg, M., Brette, R., & Goodman, D.F. (2019). Brian 2, an intuitive and efficient neural simulator. eLife, 2019(8), e47314. https://doi.org/10.7554/eLife.47314.

    Article  Google Scholar 

  • Kandel, E.R., Schwartz, J.H., Jessell, T.M., & Mack, S. (2013). Principles of neural science, McGraw-Hill Medical. New York, Chicago, San Francisco isbn:978-0-07-139011-8.

  • Squire, L.R., Berg, D., Bloom, F.E., du Lac, S., Ghosh, A., & Spitzer, N.C. (2008). Fundamental Neuroscience, Elsevier isbn:9780123740199.

  • Gertler, T.S., Chan, C.S., & Surmeier, D.J. (2008). Dichotomous anatomical properties of adult striatal medium spiny neurons. Journal of Neuroscience, 28(43), 10814–10824. https://doi.org/10.1523/JNEUROSCI.2660-08.2008.

    Article  CAS  PubMed  Google Scholar 

  • Wilson, C.J., & Groves, P.M. (1981). Spontaneous firing patterns of identified spiny neurons in the rat neostriatum. Brain Research, 220(1), 67–80.

    CAS  PubMed  Google Scholar 

  • Wilson, C.J. (1993). Chapter 18 the generation of natural firing patterns in neostriatal neurons. In Arbuthnott g, emson p, editors. Chemical signalling in the basal ganglia, progress in brain research, elsevier, 99, 277–297.

  • Gabel, L. A., & Nisenbaum, E.S. (1998). Biophysical characterization and functional consequences of a slowly inactivating potassium current in neostriatal neurons. Journal of Neurophysiology., 79(4), 1989–2002.

    CAS  PubMed  Google Scholar 

  • Goto, Y., & O’donnell, P. (2001). Network synchrony in the nucleus accumbens In Vivo. Journal of Neuroscience, 21(12), 4498–4504.

    CAS  PubMed  Google Scholar 

  • O’Donnell, P., & Grace, A. (1995). Synaptic interactions among excitatory afferents to nucleus accumbens neurons: hippocampal gating of prefrontal cortical input. Journal of Neuroscience., 15(5), 3622–3639.

    PubMed  Google Scholar 

  • Heimer, L., Alheid, G.F., de Olmos, J.S., Groenewegen, H.J., Haber, S.N., Harlan, R.E., & Zahm, D.S. (1997). The accumbens: beyond the core-shell dichotomy. The Journal of Neuropsychiatry and Clinical Neurosciences, 9(3), 354–381.

    CAS  PubMed  Google Scholar 

  • Dreyer, J.K., Weele, V., Caitlin, M., Vedran, L., & Aragona, B.J. (2016). Functionally Distinct Dopamine Signals in Nucleus Accumbens Core and Shell in the Freely Moving Rat. Journal of Neuroscience, 36(1), 98–112. https://doi.org/10.1523/JNEUROSCI.2326-15.2016.

    Article  CAS  PubMed  Google Scholar 

  • Hodgkin, A.L., & Huxley, A.F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology, 117(4), 500–544. https://doi.org/10.1113/jphysiol.1952.sp004764.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Terman, D., Rubin, J.E., Yew, A.C., & Wilson, C.J. (2002). Activity patterns in a model for the subthalamopallidal network of the basal ganglia. The Journal of neuroscience, 22(7), 2963–2976.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yucelgen, C., Denizdurduran, B., Metin, S., Elibol, R., & Sengor, N.S. (2012). A biophysical network model displaying the role of basal ganglia pathways in action selection. In International Conference on Artificial Neural Networks (pp. 177–184). Springer, Berlin, Heidelberg.

  • Naud, R., Marcille, N., Clopath, C., & Gerstner, W. (2008). Firing patterns in the adaptive exponential integrate-and-fire model. Biological cybernetics, 99(4-5), 335.

    PubMed  PubMed Central  Google Scholar 

  • Izhikevich, E.M. (2004). Which model to use for cortical spiking neurons?. IEEE Transactions on Neural Networks, 15(5), 1063–1070. https://doi.org/10.1109/TNN.2004.832719.

    Article  PubMed  Google Scholar 

  • Izhikevich, E.M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569–1572. https://doi.org/10.1109/TNN.2003.820440.

    Article  CAS  PubMed  Google Scholar 

  • Izhikevich, E.M. (2007). Dynamical systems in neuroscience the MIT press.

  • Surmeier, D.J., Ding, J., Day, M., Wang, Z., & Shen, W. (2007). D1 and D2 dopamine-receptor modulation of striatal glutamatergic signaling in striatal medium spiny neurons. Trends Neurosci., 30 (5), 228–235. https://doi.org/10.1016/j.tins.2007.03.008.

    Article  CAS  PubMed  Google Scholar 

  • Buzsaki, G., Anastassiou, C.A., & Koch, C. (2012). The origin of extracellular fields and currents - EEG, ECoG, LFP and spikes. Nature Reviews Neuroscience., 13, 407–420.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bedard, C., & Destexhe, A. (2012). Local field potentials. In Brette r, destexhe a, editors. Handbook of neural activity measurement, place: cambridge university press; 136-191.

  • Linden, H., Hagen, E., Leski, S., & et al. (2014). LFPY: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons. Frontiers in Neuroinformatics., 7, 41.

    PubMed  PubMed Central  Google Scholar 

  • Parasuram, H., Nair, B., & D’Angelo, E. (2016). At al, Computational Modeling of Single Neuron Extracellular Electric Potentials and Network Local Field Potentials using LFPsim. Frontiers in Computational Neuroscience., 10, 65.

    PubMed  PubMed Central  Google Scholar 

  • Hagen, E., Dahmen, D., Stavrinou, M.L., & et al. (2016). Hybrid scheme for modeling local field potentials from Point-Neuron networks. Cereb Cortex., 26(12), 4461–4496.

    PubMed  PubMed Central  Google Scholar 

  • Mazzoni, A., Linden, H., Cuntz, H., & et al. (2015). Computing the local field potential (LFP) from integrate-and-fire network models. PLOS Computational Biology., 11(12), e1004584.

    PubMed  PubMed Central  Google Scholar 

  • Zaehle, T., Bauch, E.M., Hinrichs, H., Schmitt, F.C., Voges, J., Heinze, H.J., & Bunzeck, N. (2013). Nucleus accumbens activity dissociates different forms of salience: evidence from human intracranial recordings. Journal of Neuroscience, 33(20), 8764–8771.

    CAS  PubMed  Google Scholar 

  • McCracken, C.B., & Grace, A.A. (2009). Nucleus accumbens deep brain stimulation produces region-specific alterations in local field potential oscillations and evoked responses in vivo. Journal of Neuroscience, 29 (16), 5354–5363.

    CAS  PubMed  Google Scholar 

  • Gleeson, P., Cantarelli, M., Marin, B., Quintana, A., & Et al. (2019). Open Source Brain: A Collaborative Resource for Visualizing, Analyzing, Simulating, and Developing Standardized Models of Neurons and Circuits. Neuron. 7, 103(3), 395–411.e5.

    CAS  Google Scholar 

  • Helmstaedter, M., Briggman, K.L., Turaga, S.C., Jain, V., Seung, H.S., & Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), 168– 74.

    CAS  PubMed  Google Scholar 

  • Sinz, F.H., Pitkow, X., Reimer, J., Bethge, M., & Tolias, A.S. (2019). Engineering a less artificial intelligence. Neuron, 103(6), 967– 979.

    CAS  PubMed  Google Scholar 

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Elibol, R., Şengör, N.S. Modeling nucleus accumbens. J Comput Neurosci 49, 21–35 (2021). https://doi.org/10.1007/s10827-020-00769-y

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