Dopamine receptor antagonists effects on low-dimensional attractors of local field potentials in optogenetic mice


Autoři: Sorinel A. Oprisan aff001;  Xandre Clementsmith aff002;  Tamas Tompa aff003;  Antonieta Lavin aff003
Působiště autorů: Department of Physics and Astronomy, College of Charleston, Charleston, SC, United States of America aff001;  Department of Computer Science, College of Charleston, Charleston, SC, United States of America aff002;  Department of Neuroscience, Medical University of South Carolina, Charleston, SC, United States of America aff003;  Faculty of Healthcare, Department of Preventive Medicine, University of Miskolc, Miskolc, Hungary aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223469

Souhrn

The goal of this study was to investigate the effects of acute cocaine injection or dopamine (DA) receptor antagonists on the medial prefrontal cortex (mPFC) gamma oscillations and their relationship to short term neuroadaptation that may mediate addiction. For this purpose, optogenetically evoked local field potentials (LFPs) in response to a brief 10 ms laser light pulse were recorded from 17 mice. D1-like receptor antagonist SCH 23390 or D2-like receptor antagonist sulpiride, or both, were administered either before or after cocaine. A Euclidian distance-based dendrogram classifier separated the 100 trials for each animal in disjoint clusters. When baseline and DA receptor antagonists trials were combined in a single trial, a minimum of 20% overlap occurred in some dendrogram clusters, which suggests a possible common, invariant, dynamic mechanism shared by both baseline and DA receptor antagonists data. The delay-embedding method of neural activity reconstruction was performed using the correlation time and mutual information to determine the lag/correlation time of LFPs and false nearest neighbors to determine the embedding dimension. We found that DA receptor antagonists applied before cocaine cancels out the effect of cocaine and leaves the lag time distributions at baseline values. On the other hand, cocaine applied after DA receptor antagonists shifts the lag time distributions to longer durations, i.e. increase the correlation time of LFPs. Fourier analysis showed that a reasonable accurate decomposition of the LFP data can be obtained with a relatively small (less than ten) Fourier coefficients.

Klíčová slova:

Cocaine – Lasers – Neural networks – Prefrontal cortex – Pyramidal cells – Fourier analysis – Interneurons – Optogenetics


Zdroje

1. Ashby JC, Hitzemann R. Pharmacology of cocaine. In: Volkow ND, Swann AC, editors. Cocaine in the Brain. New Brunswick, NJ: Rutgers University Press; 1990. p. 117–134.

2. Weiss RD, Mirin SM. Subtypes of cocaine abusers. Psychiatric Clinics of North America. 1986;9(3):491–501. doi: 10.1016/S0193-953X(18)30608-7 3774602

3. Jentsch JD, Olausson P, II RDLG, Taylor JR. Impairments of Reversal Learning and Response Perseveration after Repeated, Intermittent Cocaine Administrations to Monkeys. Neuropsychopharmacologyvolume. 2002;26:183–190. doi: 10.1016/S0893-133X(01)00355-4

4. Thompson DM, Moerschbaecher JM. An experimental analysis of the effects of d-amphetamine and cocaine on the acquisition and performance of response chains in monkeys. Journal of the Experimental Analysis of Behavior. 1979;32(3):433–444. doi: 10.1901/jeab.1979.32-433 117072

5. Evans E, Wenger G. Effects of drugs of abuse on acquisition of behavioral chains in squirrel monkeys. Psychopharmacology (Berl). 1992;107(1):55–60. doi: 10.1007/BF02244965

6. Howell LL, Votaw JR, Goodman MM, Lindsey KP. Cortical activation during cocaine use and extinction in rhesus monkeys. Psychopharmacology. 2009;208(2):191. doi: 10.1007/s00213-009-1720-3 19924404

7. Fillmore MT, Rush CR, Hays L. Acute effects of oral cocaine on inhibitory control of behavior in humans. Drug and Alcohol Dependence. 2002;67(2):157–167. doi: 10.1016/s0376-8716(02)00062-5 12095665

8. Garavan H, Kaufman JN, Hester R. Acute effects of cocaine on the neurobiology of cognitive control. Philos Trans R Soc Lond B Biol Sci. 2008;363:3267–3276. doi: 10.1098/rstb.2008.0106 18640911

9. Mendelson JH, Mello NK. Management of Cocaine Abuse and Dependence. New England Journal of Medicine. 1996;334(15):965–972. doi: 10.1056/NEJM199604113341507 8596599

10. Kumar DS, Benedict E, Wu O, Rubin E, Gluck MA, Foltin RW, et al. Learning functions in short-term cocaine users. Addictive Behaviors Reports. 2019;9:100169. doi: 10.1016/j.abrep.2019.100169 31193767

11. Engel AK, Fries P, Kanig P, Brecht M, Singer W. Temporal Binding, Binocular Rivalry, and Consciousness. Consciousness and Cognition. 1999;8(2):128–151. doi: 10.1006/ccog.1999.0389 10447995

12. Buzsaki G, Draguhn A. Neuronal Oscillations in Cortical Networks. Science. 2004;304(5679):1926–1929. doi: 10.1126/science.1099745 15218136

13. Jutras MJ, Fries P, Buffalo EA. Gamma-Band Synchronization in the Macaque Hippocampus and Memory Formation. Journal of Neuroscience. 2009;29(40):12521–12531. doi: 10.1523/JNEUROSCI.0640-09.2009 19812327

14. Traub R, Jefferys JR, Whittington M. Simulation of Gamma Rhythms in Networks of Interneurons and Pyramidal Cells. Journal of Computational Neuroscience. 1997;4(2):141–150. doi: 10.1023/A:1008839312043 9154520

15. Feldman ML. Morphology of the neocortical neuron. In: Peters A, Jones EG, editors. The Cerebral Cortex. New York: Plenum Press; 1984. p. 123–200.

16. Bannister AP. Inter- and intra-laminar connections of pyramidal cells in the neocortex. Neuroscience Research. 2005;53(2):95–103. doi: 10.1016/j.neures.2005.06.019 16054257

17. Sanchez-Vives M, McCormick D. Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nature Neurosci. 2000;3:1027–1034. doi: 10.1038/79848 11017176

18. Compte A, Reig R, Descalzo VF, Harvey MA, Puccini GD, Sanchez-Vives MV. Spontaneous High-Frequency (10–80 Hz) Oscillations during Up States in the Cerebral Cortex In Vitro. Journal of Neuroscience. 2008;28(51):13828–13844. doi: 10.1523/JNEUROSCI.2684-08.2008

19. Luczak A, Bartho P, Marguet SL, Buzsaki G, Harris KD. Sequential structure of neocortical spontaneous activity in vivo. Proceedings of the National Academy of Sciences. 2007;104(1):347–352. doi: 10.1073/pnas.0605643104

20. Kana RK, Libero LE, Moore MS. Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders. Physics of Life Reviews. 2011;8(4):410–437. doi: 10.1016/j.plrev.2011.10.001 22018722

21. Takahata K, Kato M. Neural mechanism underlying autistic savant and acquired savant syndrome. Brain Nerve. 2008;60(7):861–9. 18646626

22. Casanova M, Trippe J. Radial cytoarchitecture and patterns of cortical connectivity in autism. Philosophical Transactions of the Royal Society B: Biological Sciences. 2009;364:1433–1436. doi: 10.1098/rstb.2008.0331

23. Galarreta M, Hestrin S. Spike Transmission and Synchrony Detection in Networks of GABAergic Interneurons. Science. 2001;292(5525):2295–2299. doi: 10.1126/science.1061395 11423653

24. Sultan K, Brown K, Shi SH. Production and organization of neocortical interneurons. Frontiers in Cellular Neuroscience. 2013;7:221. doi: 10.3389/fncel.2013.00221 24312011

25. Michael A K, Zoe M C. Gamma and beta neural activity evoked during a sensory gating paradigm: Effects of auditory, somatosensory and cross-modal stimulation. Clinical Neuropsychology. 2006;117:2549–2563.

26. Cheng CH, Chan PYS, Niddam DM, Tsai SY, Hsu SC, Liu CY. Sensory gating, inhibition control and gamma oscillations in the human somatosensory cortex. Scientific Reports. 2016;6:20437–47. doi: 10.1038/srep20437 26843358

27. Hong L, Summerfelt A, Mitchell BD, McMahon RP, Wonodi I, Buchanan RW, et al. Sensory gating endophenotype based on its neural oscillatory pattern and heritability estimate. Archives of General Psychiatry. 2008;65(9):1008–1016. 18762587

28. Cardin JA, Carlen M, Meletis K, Knoblich U, Zhang F, Deisseroth K, et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature. 2009;459:663–7. doi: 10.1038/nature08002 19396156

29. Sohal VS, Zhang F, Yizhar O, Deisseroth K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature. 2009;459 7247:698–702. doi: 10.1038/nature07991 19396159

30. Sohal VS, Zhang F, Yizhar O, Deisseroth K. Insights into Cortical Oscillations Arising from Optogenetic Studies. Biological Psychiatry. 2016;71:1039–1045. doi: 10.1016/j.biopsych.2012.01.024

31. Guidotti A, Auta J, Davis JM, Dong E, Grayson DR, Veldic M, et al. GABAergic dysfunction in schizophrenia: new treatment strategies on the horizon. Psychopharmacology. 2005;180(2):191–205. doi: 10.1007/s00213-005-2212-8 15864560

32. Schmidt M, Mirnics K. Neurodevelopment, GABA System Dysfunction, and Schizophrenia. Neuropsychopharmacology. 2015;40:190–206. doi: 10.1038/npp.2014.95 24759129

33. Fuchs EC, Zivkovic AR, Cunningham MO, Middleton S, LeBeau FEN, Bannerman D, et al. Recruitment of Parvalbumin-Positive Interneurons Determines Hippocampal Function and Associated Behavior. Neuron. 2007;53(4):591–604. doi: 10.1016/j.neuron.2007.01.031 17296559

34. Halasy K, Buhl E, Lorinczi Z, Tamas G, Somogyi P. Synaptic target selectivity and input of GABAergic basket and bistratified interneurons in the CA1 area of the rat hippocampus. Hippocampus. 1996;6:306–29. doi: 10.1002/(SICI)1098-1063(1996)6:3<306::AID-HIPO8>3.0.CO;2-K 8841829

35. Booker SA, Gross A, Althof D, Shigemoto R, Bettler B, Frotscher M, et al. Differential GABAB-Receptor-Mediated Effects in Perisomatic- and Dendrite-Targeting Parvalbumin Interneurons. Journal of Neuroscience. 2013;33(18):7961–7974. doi: 10.1523/JNEUROSCI.1186-12.2013 23637187

36. Henry TR Markram amd Maria, Yun W, Anirudh G, Gilad S, Caizhi W. Interneurons of the neocortical inhibitory system. Nature Reviews Neuroscience. 2004;5:793–807. doi: 10.1038/nrn1519

37. Micheva KD, Wolman D, Mensh BD, Pax E, Buchanan J, Smith SJ, et al. A large fraction of neocortical myelin ensheathes axons of local inhibitory neurons. eLife. 2016;5:e15784. doi: 10.7554/eLife.15784 27383052

38. Melchitzky DS, Lewis DA. Pyramidal Neuron Local Axon Terminals in Monkey Prefrontal Cortex: Differential Targeting of Subclasses of GABA Neurons. Cerebral Cortex. 2003;13(5):452–460. doi: 10.1093/cercor/13.5.452 12679292

39. Schnitzler A, Gross J. Normal and pathological oscillatory communication in the brain. Nat Rev Neurosci. 2005;6:285–296. doi: 10.1038/nrn1650 15803160

40. Peter J U, Wolf S. Abnormal neural oscillations and synchrony in schizophrenia. Nature Reviews Neuroscience. 2010;11:100–113. doi: 10.1038/nrn2774

41. Levy F. Theories of Autism. Australian & New Zealand Journal of Psychiatry. 2007;41(11):859–868. doi: 10.1080/00048670701634937

42. Orekhova EV, Stroganova TA, Nygren G, Tsetlin MM, Posikera IN, Gillberg C, et al. Excess of High Frequency Electroencephalogram Oscillations in Boys with Autism. Biological Psychiatry. 2007;62(9):1022–1029. doi: 10.1016/j.biopsych.2006.12.029 17543897

43. Liddle EB, Price D, Palaniyappan L, Brookes MJ, Robson SE, Hall EL, et al. Abnormal salience signaling in schizophrenia: The role of integrative beta oscillations. Human Brain Mapping. 2016;37(4):1361–1374. doi: 10.1002/hbm.23107 26853904

44. Lewis DA, Hashimoto T, Volk DW. Cortical inhibitory neurons and schizophrenia. Nat Rev Neurosci. 2005;6(4):312–324. doi: 10.1038/nrn1648 15803162

45. Lewis DA, Hashimoto T. Deciphering the Disease Process of Schizophrenia: The Contribution of Cortical Gaba Neurons. International Review of Neurobiology. 2007;78:109–131. doi: 10.1016/S0074-7742(06)78004-7 17349859

46. Ethridge LE, White SP, Mosconi MW, Wang J, Pedapati EV, Erickson CA, et al. Neural synchronization deficits linked to cortical hyper-excitability and auditory hypersensitivity in fragile X syndrome. Molecular Autism. 2017;8(1):22. doi: 10.1186/s13229-017-0140-1 28596820

47. Rotschafer S, Razak K. Auditory Processing in Fragile X Syndrome. Frontiers in Cellular Neuroscience. 2014;8:19. doi: 10.3389/fncel.2014.00019 24550778

48. Gibson JR, Bartley AF, Hays SA, Huber KM. Imbalance of Neocortical Excitation and Inhibition and Altered UP States Reflect Network Hyperexcitability in the Mouse Model of Fragile X Syndrome. Journal of Neurophysiology. 2008;100(5):2615–2626. doi: 10.1152/jn.90752.2008 18784272

49. Contractor A, Klyachko V, Portera-Cailliau C. Altered Neuronal and Circuit Excitability in Fragile X Syndrome. Neuron. 2017;87(4):699–715. doi: 10.1016/j.neuron.2015.06.017

50. Gradinaru V, Thompson KR, Zhang F, Mogri M, Kay K, Schneider MB, et al. Targeting and Readout Strategies for Fast Optical Neural Control In Vitro and In Vivo. Journal of Neuroscience. 2007;27(52):14231–14238. doi: 10.1523/JNEUROSCI.3578-07.2007 18160630

51. Eleftheriou C, Cesca F, Maragliano L, Benfenati F, Maya-Vetencourt J. Optogenetic Modulation of Intracellular Signalling and Transcription: Focus on Neuronal Plasticity. Journal of Experimental Neuroscience. 2017;11:1179069517703354. doi: 10.1177/1179069517703354 28579827

52. Kim K, Kim JH, Song YH, Lee SH. Functional dissection of inhibitory microcircuits in the visual cortex. Neuroscience Research. 2017;116:70–76. doi: 10.1016/j.neures.2016.09.003 27633836

53. Iurilli G, Ghezzi D, Olcese U, Lassi G, Nazzaro C, Tonini R, et al. Sound-Driven Synaptic Inhibition in Primary Visual Cortex. Neuron. 2012;73(4):814–828. doi: 10.1016/j.neuron.2011.12.026 22365553

54. Wilson NR, Runyan CA, Wang FL, Sur M. Division and subtraction by distinct cortical inhibitory networks in vivo. Nature. 2012;488:343–348. doi: 10.1038/nature11347 22878717

55. Liu X, Ramirez S, Pang PT, Puryear CB, Govindarajan A, Deisseroth K, et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature. 2012;484:381–385. doi: 10.1038/nature11028 22441246

56. Ramirez S, Tonegawa S, Liu X. Identification and optogenetic manipulation of memory engrams in the hippocampus. Frontiers in Behavioral Neuroscience. 2014;7:226. doi: 10.3389/fnbeh.2013.00226 24478647

57. Chen Y, Knight ZA. Making sense of the sensory regulation of hunger neurons. BioEssays. 2016;38(4):316–324. doi: 10.1002/bies.201500167 26898524

58. Aponte Y, Atasoy D, Sternson SM. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nature Neuroscience. 2011;14:351–355. doi: 10.1038/nn.2739 21209617

59. Atasoy D, Betley JN, Su HH, Sternson SM. Deconstruction of a neural circuit for hunger. Nature. 2012;488:172–177. doi: 10.1038/nature11270 22801496

60. Jennings JH, Rizzi G, Stamatakis AM, Ung RL, Stuber GD. The Inhibitory Circuit Architecture of the Lateral Hypothalamus Orchestrates Feeding. Science. 2013;341(6153):1517–1521. doi: 10.1126/science.1241812 24072922

61. Do Monte F, Quirk G, Li B, Penzo M. Retrieving fear memories, as time goes by? Molecular psychiatry. 2016;21:1027–1036.

62. Haubensak W, Kunwar PS, Cai H, Ciocchi S, Wall NR, Ponnusamy R, et al. Genetic dissection of an amygdala microcircuit that gates conditioned fear. Nature. 2010;468:270–276. doi: 10.1038/nature09553 21068836

63. Lin D, Boyle MP, Dollar P, Lee H, Lein ES, Perona P, et al. Functional identification of an aggression locus in the mouse hypothalamus. Nature. 2011;470(7333):221–226. doi: 10.1038/nature09736 21307935

64. Allsop SA, Vander Weele CM, Wichmann R, Tye KM. Optogenetic insights on the relationship between anxiety-related behaviors and social deficits. Frontiers in Behavioral Neuroscience. 2014;8:241. doi: 10.3389/fnbeh.2014.00241 25076878

65. Tye KM, Prakash R, Kim SY, Fenno LE, Grosenick L, Zarabi H, et al. Amygdala circuitry mediating reversible and bidirectional control of anxiety. Nature. 2011;471(7338):358–362. doi: 10.1038/nature09820 21389985

66. Tye KM, Mirzabekov JJ, Warden MR, Ferenczi EA, Tsai HC, Finkelstein J, et al. Dopamine neurons modulate neural encoding and expression of depression-related behaviour. Nature. 2013;493(7433):537–541. doi: 10.1038/nature11740 23235822

67. Gradinaru V, Mogri M, Thompson KR, Henderson JM, Deisseroth K. Optical Deconstruction of Parkinsonian Neural Circuitry. Science. 2009;324(5925):354–359. doi: 10.1126/science.1167093 19299587

68. Kravitz AV, Freeze BS, Parker PRL, Kay K, Thwin MT, Deisseroth K, et al. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature. 2010;466:622–626. doi: 10.1038/nature09159 20613723

69. Wykes RC, Kullmann DM, Pavlov I, Magloire V. Optogenetic approaches to treat epilepsy. Journal of Neuroscience Methods. 2016;260:215–220. doi: 10.1016/j.jneumeth.2015.06.004 26072246

70. Peng Z, Zhang N, Wei W, Huang CS, Cetina Y, Otis TS, et al. A Reorganized GABAergic Circuit in a Model of Epilepsy: Evidence from Optogenetic Labeling and Stimulation of Somatostatin Interneurons. Journal of Neuroscience. 2013;33(36):14392–14405. doi: 10.1523/JNEUROSCI.2045-13.2013 24005292

71. Kokaia M, Andersson M, Ledri M. An optogenetic approach in epilepsy. Neuropharmacology. 2013;69:89–95. doi: 10.1016/j.neuropharm.2012.05.049 22698957

72. Paz JT, Davidson TJ, Frechette ES, Delord B, Parada I, Peng K, et al. Closed-loop optogenetic control of thalamus as a tool for interrupting seizures after cortical injury. Nat Neurosci. 2013;16(1):64–70. doi: 10.1038/nn.3269 23143518

73. Gelder RNV. Photochemical approaches to vision restoration. Vision Research. 2015;111:134–141. doi: 10.1016/j.visres.2015.02.001 25680758

74. Busskamp V, Duebel J, Balya D, Fradot M, Viney TJ, Siegert S, et al. Genetic Reactivation of Cone Photoreceptors Restores Visual Responses in Retinitis Pigmentosa. Science. 2010;329(5990):413–417. doi: 10.1126/science.1190897 20576849

75. Lagali PS, Balya D, Awatramani GB, Munch TA, Kim DS, Busskamp V, et al. Light-activated channels targeted to ON bipolar cells restore visual function in retinal degeneration. Nat Neurosci. 2008;11:667–675. doi: 10.1038/nn.2117 18432197

76. Rivnay J, Wang H, Fenno L, Deisseroth K, Malliaras G. Next-generation probes, particles, and proteins for neural interfacing. Science Advances. 2017;3:e1601649. doi: 10.1126/sciadv.1601649 28630894

77. Ritz M, Lamb R, Goldberg S, Kuhar M. Cocaine receptors on dopamine transporters are related to self-administration of cocaine. Science. 1987;237(4819):1219–1223.

78. Weed MR, Woolverton WL. The reinforcing effects of dopamine D1 receptor agonists in rhesus monkeys. Journal of Pharmacology and Experimental Therapeutics. 1995;275(3):1367–1374. 8531104

79. Iravani MM, Asari D, Patel JC, Wieczorek WJ, Kruk ZL. Direct effects of 3,4-methylenedioxymethamphetamine (MDMA) on serotonin or dopamine release and uptake in the caudate putamen, nucleus accumbens, substantia nigra pars reticulata, and the dorsal raphé nucleus slices. Synapse. 2000;364:275–85. doi: 10.1002/(SICI)1098-2396(20000615)36:4%3C275::AID-SYN4%3E3.0.CO;2-%23

80. Dunlap LE, Andrews AM, Olson DE. Dark Classics in Chemical Neuroscience: 3,4-Methylenedioxymethamphetamine. ACS chemical neuroscience. 2018;9:2408–2427. doi: 10.1021/acschemneuro.8b00155 30001118

81. Koob GF. Drugs of abuse: anatomy, pharmacology and function of reward pathways. Trends in Pharmacological Sciences. 1992;13:177–184. doi: 10.1016/0165-6147(92)90060-j 1604710

82. Chiara GD. Drug addiction as dopamine-dependent associative learning disorder. European Journal of Pharmacology. 1999;375(1):13–30. doi: 10.1016/s0014-2999(99)00372-6 10443561

83. Bergman J, Kamien JB, Spealman RD. Antagonism of cocaine self-administration by selective dopamine D1 and D2 antagonists. Behavioural pharmacology. 1990;1:355–363. doi: 10.1097/00008877-199000140-00009 11175420

84. Watkins SS, Epping-Jordan MP, Koob GF, Markou A. Blockade of Nicotine Self-Administration with Nicotinic Antagonists in Rats. Pharmacology Biochemistry and Behavior. 1999;62(4):743–751. doi: 10.1016/S0091-3057(98)00226-3

85. Daniela E, Brennan K, Gittings D, Hely L, Schenk S. Effect of SCH 23390 on (±)-3,4-methylenedioxymethamphetamine hyperactivity and self-administration in rats. Pharmacology Biochemistry and Behavior. 2004;77(4):745–750. doi: 10.1016/j.pbb.2004.01.008

86. Wise R, Murray A, Bozarth M. Bromocriptine self-administration and bromocriptine-reinstatement of cocaine-trained and heroin-trained lever pressing in rats. Psychopharmacology (Berl). 1990;100(3):355–360. doi: 10.1007/BF02244606

87. Spealman RD, Barrett-Larimore RL, Rowlett JK, Platt DM, Khroyan TV. Pharmacological and Environmental Determinants of Relapse to Cocaine-Seeking Behavior. Pharmacology Biochemistry and Behavior. 1999;64(2):327–336. doi: 10.1016/S0091-3057(99)00049-0

88. Reiner DJ, Fredriksson I, Lofaro OM, Bossert JM, Shaham Y. Relapse to opioid seeking in rat models: behavior, pharmacology and circuits. Neuropsychopharmacology. 2019;44(3):465–477. doi: 10.1038/s41386-018-0234-2 30293087

89. Farrell MR, Schoch H, Mahler SV. Modeling cocaine relapse in rodents: Behavioral considerations and circuit mechanisms. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2018;87:33–47. doi: 10.1016/j.pnpbp.2018.01.002 29305936

90. Self DW, Barnhart WJ, Lehman DA, Nestler EJ. Opposite Modulation of Cocaine-Seeking Behavior by D1- and D2-Like Dopamine Receptor Agonists. Science. 1996;271(5255):1586–1589. doi: 10.1126/science.271.5255.1586 8599115

91. Mello N, Negus S. Preclinical Evaluation of Pharmacotherapies for Treatment of Cocaine and Opioid Abuse Using Drug Self-Administration Procedures. Neuropsychopharmacology. 1996;14:375–424. doi: 10.1016/0893-133X(95)00274-H 8726752

92. Haney M, Ward AS, Foltin RW, Fischman MW. Effects of ecopipam, a selective dopamine D1 antagonist, on smoked cocaine self-administration by humans. Psychopharmacology. 2001;155(4):330–337. doi: 10.1007/s002130100725 11441422

93. Romach MK, Glue P, Kampman K, Kaplan HL, Somer GR, Poole S, et al. Attenuation of the Euphoric Effects of Cocaine by the Dopamine D1/D5 Antagonist Ecopipam (SCH 39166). Archives of General Psychiatry. 1999;56(12):1101–1106. 10591286

94. Jackson DM, Westlind-Danielsson A. Dopamine receptors: Molecular biology, biochemistry and behavioural aspects. Pharmacology & Therapeutics. 1994;64(2):291–370. doi: 10.1016/0163-7258(94)90041-8

95. Rangel-Barajas C, Coronel I, Floran B. Dopamine Receptors and Neurodegeneration. Aging and disease. 2015;6(5):349–368. doi: 10.14336/AD.2015.0330 26425390

96. Kebabian JW, Calne DB. Multiple receptors for dopamine. Naure. 1979;277:93–96.

97. Mishra A, Singh S, Shukla S. Physiological and Functional Basis of Dopamine Receptors and Their Role in Neurogenesis: Possible Implication for Parkinson’s disease. Journal of experimental neuroscience. 2018;12.

98. Self DW, Stein L. The D1 agonists SKF 82958 and SKF 77434 are self-administered by rats. Brain Research. 1992;582(2):349–352. doi: 10.1016/0006-8993(92)90155-3 1356585

99. Caine SB, Koob GF. Effects of dopamine D-1 and D-2 antagonists on cocaine self-administration under different schedules of reinforcement in the rat. Journal of Pharmacology and Experimental Therapeutics. 1994;270(1):209–218. 8035317

100. Khroyan TV, Barrett-Larimore RL, Rowlett JK, Spealman RD. Dopamine D1- and D2-Like Receptor Mechanisms in Relapse to Cocaine-Seeking Behavior: Effects of Selective Antagonists and Agonists. Journal of Pharmacology and Experimental Therapeutics. 2000;294(2):680–687. 10900248

101. Caine SB, Negus SS, Mello NK. Effects of dopamine D1-like and D2-like agonists on cocaine self-administration in rhesus monkeys: rapid assessment of cocaine dose-effect functions. Psychopharmacology. 2000;148(1):41–51. doi: 10.1007/s002130050023

102. Oprisan SA, Lynn PE, Tompa T, Lavin A. Low-dimensional attractor for neural activity from local field potentials in optogenetic mice. Frontiers in Computational Neuroscience. 2015;9:125. doi: 10.3389/fncom.2015.00125 26483665

103. Oprisan SA, Imperatore J, Helms J, Tompa T, Lavin A. Cocaine-Induced Changes in Low-Dimensional Attractors of Local Field Potentials in Optogenetic Mice. Frontiers in Computational Neuroscience. 2018;12:2. doi: 10.3389/fncom.2018.00002 29445337

104. Oprisan SA. All Phase Resetting Curves Are Bimodal, but Some Are More Bimodal Than Others. ISRN Computational Biology. 2013;2013(Article ID 230571):1–11. doi: 10.1155/2013/230571

105. Oprisan SA. A Consistent Definition of Phase Resetting Using Hilbert Transform. International Scholarly Research Notices Computational Biology. 2017;2017(Article ID 5865101):10.

106. Oprisan SA, Canavier CC. The influence of limit cycle topology on the phase resetting curve. Neural Computation. 2002;14:1027–2002. doi: 10.1162/089976602753633376 11972906

107. Oprisan SA, Thirumalai V, Canavier CC. Dynamics from a time series: Can we extract the phase resetting curve from a time series? Biophysical Journal. 2003;84:2919–2928. doi: 10.1016/S0006-3495(03)70019-8 12719224

108. Dilgen J, Tompa T, Saggu S, Naselaris T, Lavin A. Optogenetically evoked gamma oscillations are disturbed by cocaine administration. Frontiers in Cellular Neuroscience. 2013;7:213. doi: 10.3389/fncel.2013.00213 24376397

109. Saraçli S, Doğan N, Doğan İ. Comparison of hierarchical cluster analysis methods by cophenetic correlation. Journal of Inequalities and Applications. 2013;2013(1):203. doi: 10.1186/1029-242X-2013-203

110. Hill T, Lewicki P, editors. Statistics: Methods and Applications. Tulksa, OK: StatSoft, Inc; 2005.

111. Xu P. Differential phase space reconstructed for chaotic time series. Applied Mathematical Modelling. 2009;33(2):999–1013. doi: 10.1016/j.apm.2007.12.021

112. Takens F. Detecting strange attractors in turbulence. In: Rand D, Young LS, editors. Dynamical Systems and Turbulence, Warwick 1980. vol. 898 of Lecture Notes in Mathematics. Springer Berlin Heidelberg; 1981. p. 366–381.

113. Diks C, van Houwelingen JC, Takens F, DeGoede J. Reversibility as a criterion for discriminating time series. Phys Lett A. 1995;201:221–228. doi: 10.1016/0375-9601(95)00239-Y

114. Packard NH, Crutchfield JP, Farmer JD, Shaw RS. Geometry from a Time Series. Phys Rev Lett. 1980;45:712–716. doi: 10.1103/PhysRevLett.45.712

115. Whitney H. Differentiable Manifolds. Annals of Mathematics. 1936;37(3):645–680. doi: 10.2307/1968482

116. Mañé R. On the dimension of the compact invariant sets of certain non-linear maps. In: Rand D, Young LS, editors. Dynamical Systems and Turbulence, Warwick 1980. Berlin, Heidelberg: Springer Berlin Heidelberg; 1981. p. 230–242.

117. Casdagli M, Eubank S, Farmer JD, Gibson J. State Space Reconstruction in the Presence of Noise. Phys D. 1991;51(1-3):52–98. doi: 10.1016/0167-2789(91)90222-U

118. Zeng X, Eykholt R, Pielke RA. Estimating the Lyapunov-exponent spectrum from short time series of low precision. Phys Rev Lett. 1991;66:3229–3232. doi: 10.1103/PhysRevLett.66.3229 10043734

119. Schiff SJ, Chang T. Differentiation of linearly correlated noise from chaos in a biologic system using surrogate data. Biological Cybernetics. 1992;67(5):387–393. doi: 10.1007/bf00200982 1391112

120. Schuster HG, Just W, editors. Deterministic Chaos: An Introduction, 4th, Revised and Enlarged Edition. Weinheim: WILEY-VCH Verlag GmbH and Co. KGaA; 2005.

121. King GP, Jones R, Broomhead DS. Phase portraits from a time series: A singular system approach. Nuclear Physics B—Proceedings Supplements. 1987;2:379–390. doi: 10.1016/0920-5632(87)90029-6

122. Holzfuss J, Mayer-Kress G. An Approach to Error-Estimation in the Application of Dimension Algorithms. In: Mayer-Kress G, editor. Dimensions and Entropies in Chaotic Systems. vol. 32 of Springer Series in Synergetics; 1986. p. 114–122. doi: 10.1007/978-3-642-71001-8_15

123. Fraser AM, Swinney HL. Independent coordinates for strange attractors from mutual information. Phys Rev A. 1986;33:1134–1140. doi: 10.1103/PhysRevA.33.1134

124. Hegger R, Kantz H, Schreiber T. Practical implementation of nonlinear time series methods: The TISEAN package. Chaos. 1999;9:413–435. doi: 10.1063/1.166424 12779839

125. Kantz H, Schreiber T, editors. Non-linear Time Series Analysis. Cambridge: Cambridge University Press; 1997.

126. Abarbanel HDI, editor. Analysis of Observed Chaotic Data. New York: Springer; 1996.

127. Kennel MB, Brown R, Abarbanel HDI. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A. 1992;45:3403–3411. doi: 10.1103/physreva.45.3403 9907388

128. Sen AK, Litak G, Syta A. Cutting process dynamics by nonlinear time series and wavelet analysis. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2007;17(2). doi: 10.1063/1.2749329

129. Kugiumtzis D. Surrogate Data Test on Time Series. In: Soofi A, Cao L, editors. Modelling and Forecasting Financial Data. vol. 2 of Studies in Computational Finance. Springer US; 2002. p. 267–282.

130. Grassberger P. Evidence for climatic attractors. Nature. 1987;362:524. doi: 10.1038/326524a0

131. Theiler J. Estimating fractal dimension. J Opt Soc Am A. 1990;7(6):1055–1073. doi: 10.1364/JOSAA.7.001055

132. Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer JD. Testing for nonlinearity in time series: the method of surrogate data. Physica D. 1992;58(58):77–94. doi: 10.1016/0167-2789(92)90102-S

133. Frechet M. Sur quelques points du calcul fonctionnel. Rendiconti del Circolo Mathematico di Palermo. 1906;22:1–74. doi: 10.1007/BF03018603

134. Eiter T, Mannila H. Computing discrete Frechet distance. Technical University of Vienna and University of Helsinki; 1994.

135. Alt H, Godau M. Computiong the Frechet distance between towo polgonal curves. International Journal of Computational Geometry & Applications. 1995;05(01n02):75–91. doi: 10.1142/S0218195995000064

136. Danziger A. Discrete Frechet Distance; 2013. https://www.mathworks.com/matlabcentral/fileexchange/31922-discrete-frechet-distance?focused=3785717&tab=function&requestedDomain=www.mathworks.com.

137. Howell KB. Principles of Fourier Analysis. Textbooks in Mathematics. CRC Press; 2001. Available from: https://books.google.com/books?id=Q5HMBQAAQBAJ.

138. Stein EM, Shakarchi R. Fourier Analysis: An Introduction. Princeton Lectures in Analysis Series. Princeton University Press; 2003.

139. Osorio I, Frei MG. Seizure abatement with single DC pulses: ias phase resetting at play? International Journal of Neural Systems. 2009;19(03):149–156.

140. Parastarfeizabadi M, Kouzani AZ. Advances in closed-loop deep brain stimulation devices. Journal of NeuroEngineering and Rehabilitation. 2017;14(1):79. doi: 10.1186/s12984-017-0295-1 28800738

141. Tass PA. Stochastic Phase Resetting: A Theory for Deep Brain Stimulation. Progress of Theoretical Physics Supplement. 2000;139:301–313. doi: 10.1143/PTPS.139.301

142. Tass PA. A model of desynchronizing deep brain stimulation with a demand-controlled coordinated reset of neural subpopulations. Biological Cybernetics. 2003;89(2):81–88. doi: 10.1007/s00422-003-0425-7 12905037

143. Oprisan SA, Prinz A, Canavier CC. Phase resetting and phase locking in hybrid circuits of one model and one biological neuron. Biophysical Journal. 2004;87:2283–2298. doi: 10.1529/biophysj.104.046193 15454430

144. Oprisan SA, Austin DI. A Generalized Phase Resetting Method for Phase-Locked Modes Prediction. PLoS ONE. 2017;12(3):e0174304. doi: 10.1371/journal.pone.0174304 28323894

145. Chakravarti IM, Laha RG, Roy J. Handbook of methods of applied statistics. No. v. 1 in Wiley series in probability and mathematical statistics. Wiley; 1967.

146. Steinskog DJ, Tjostheim DB, Kvamsto NG. A Cautionary Note on the Use of the Kolmogorov—Smirnov Test for Normality. Monthly Weather Review. 2007;135(3):1151–1157. doi: 10.1175/MWR3326.1


Článek vyšel v časopise

PLOS One


2019 Číslo 10