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Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification

Neuropsychopharmacology volume 48, pages 1377–1385 (2023)Cite this article

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Abstract

The ability to appropriately update the value of a given action is a critical component of flexible decision making. Several psychiatric disorders, including schizophrenia, are associated with impairments in flexible decision making that can be evaluated using the probabilistic reversal learning (PRL) task. The PRL task has been reverse-translated for use in rodents. Disrupting glutamate neurotransmission during early postnatal neurodevelopment in rodents has induced behavioral, cognitive, and neuropathophysiological abnormalities relevant to schizophrenia. Here, we tested the hypothesis that using the NMDA receptor antagonist phencyclidine (PCP) to disrupt postnatal glutamatergic transmission in rats would lead to impaired decision making in the PRL. Consistent with this hypothesis, compared to controls the postnatal PCP-treated rats completed fewer reversals and exhibited disruptions in reward and punishment sensitivity (i.e., win-stay and lose-shift responding, respectively). Moreover, computational analysis of behavior revealed that postnatal PCP-treatment resulted in a pronounced impairment in the learning rate throughout PRL testing. Finally, a deep neural network (DNN) trained on the rodent behavior could accurately predict the treatment group of subjects. These data demonstrate that disrupting early postnatal glutamatergic neurotransmission impairs flexible decision making and provides evidence that DNNs can be trained on behavioral datasets to accurately predict the treatment group of new subjects, highlighting the potential for DNNs to aid in the diagnosis of schizophrenia.

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Fig. 1: Postnatal PCP-treatment impairs PRL performance.
Fig. 2: Validation of Q-learning model.
Fig. 3: Postnatal PCP-treatment disrupts Q-learning.
Fig. 4: Deep neural network (DNN) predicts treatment group.

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References

  1. Cohen JD, McClure SM, Yu AJ. Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philos Trans R Soc Lond Ser B Biol Sci. 2007;362:933–42.

    Google Scholar 

  2. Daw ND, O’Doherty JP, Dayan P, Seymour B, Dolan RJ. Cortical substrates for exploratory decisions in humans. Nature. 2006;441:876–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Ebitz RB, Albarran E, Moore T. Exploration disrupts choice-predictive signals and alters dynamics in prefrontal cortex. Neuron. 2018;97:450–61.e9

    CAS  PubMed  Google Scholar 

  4. Murray EA, Rudebeck PH. Specializations for reward-guided decision-making in the primate ventral prefrontal cortex. Nat Rev Neurosci. 2018;19:404–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Culbreth AJ, Westbrook A, Xu Z, Barch DM, Waltz JA. Intact ventral striatal prediction error signaling in medicated schizophrenia patients. Biol Psychiatry Cogn Neurosci Neuroimaging. 2016;1:474–83.

    PubMed  PubMed Central  Google Scholar 

  6. Hernaus D, Xu Z, Brown EC, Ruiz R, Frank MJ, Gold JM, et al. Motivational deficits in schizophrenia relate to abnormalities in cortical learning rate signals. Cogn Affect Behav Neurosci. 2018;18:1338–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Ermakova AO, Knolle F, Justicia A, Bullmore ET, Jones PB, Robbins TW, et al. Abnormal reward prediction-error signalling in antipsychotic naive individuals with first-episode psychosis or clinical risk for psychosis. Neuropsychopharmacology. 2018;43:1691–99.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Yaple ZA, Tolomeo S, Yu R. Abnormal prediction error processing in schizophrenia and depression. Hum Brain Mapp. 2021;42:3547–60.

    PubMed  PubMed Central  Google Scholar 

  9. Nasser HM, Calu DJ, Schoenbaum G, Sharpe MJ. The dopamine prediction error: contributions to associative models of reward learning. Front Psychol. 2017;8:244.

  10. Schultz W. Dopamine reward prediction-error signalling: a two-component response. Nat Rev Neurosci. 2016;17:183–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Schlagenhauf F, Huys QJM, Deserno L, Rapp MA, Beck A, Heinze H-J, et al. Striatal dysfunction during reversal learning in unmedicated schizophrenia patients. NeuroImage. 2014;89:171–80.

    PubMed  Google Scholar 

  12. Katthagen T, Kaminski J, Heinz A, Buchert R, Schlagenhauf F. Striatal dopamine and reward prediction error signaling in unmedicated schizophrenia patients. Schizophr Bull. 2020;46:1535–46.

    PubMed  PubMed Central  Google Scholar 

  13. Reddy LF, Waltz JA, Green MF, Wynn JK, Horan WP. Probabilistic reversal learning in schizophrenia: stability of deficits and potential causal mechanisms. Schizophr Bull. 2016;42:942–51.

    PubMed  PubMed Central  Google Scholar 

  14. Strauss GP, Datta R, Armstrong W, Raugh IM, Kraguljac NV, Lahti AC. Reinforcement learning abnormalities in the attenuated psychosis syndrome and first episode psychosis. Eur Neuropsychopharmacol. 2021;47:11–19.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Chang WC, Waltz JA, Gold JM, Chan TCW, Chen EYH. Mild reinforcement learning deficits in patients with first-episode psychosis. Schizophr Bull. 2016;42:1476–85.

    PubMed  PubMed Central  Google Scholar 

  16. McGorry PD, Killackey E, Yung A. Early intervention in psychosis: concepts, evidence and future directions. World Psychiatry. 2008;7:148–56.

    PubMed  PubMed Central  Google Scholar 

  17. Norman RM, Malla AK, Verdi MB, Hassall LD, Fazekas C. Understanding delay in treatment for first-episode psychosis. Psychological Med. 2004;34:255–66.

    CAS  Google Scholar 

  18. Tzur Bitan D, Grossman Giron A, Alon G, Mendlovic S, Bloch Y, Segev A. Attitudes of mental health clinicians toward perceived inaccuracy of a schizophrenia diagnosis in routine clinical practice. BMC Psychiatry. 2018;18:317.

    PubMed  PubMed Central  Google Scholar 

  19. Frances A. The past, present and future of psychiatric diagnosis. World Psychiatry. 2013;12:111.

    PubMed  PubMed Central  Google Scholar 

  20. Quaak M, van de Mortel L, Thomas RM, van Wingen G. Deep learning applications for the classification of psychiatric disorders using neuroimaging data: systematic review and meta-analysis. NeuroImage: Clin. 2021;30:102584.

    PubMed  Google Scholar 

  21. Chen L, Xia C, Sun H. Recent advances of deep learning in psychiatric disorders. Precis Clin Med. 2020;3:202–13.

    PubMed  PubMed Central  Google Scholar 

  22. Kalmady SV, Greiner R, Agrawal R, Shivakumar V, Narayanaswamy JC, Brown MRG, et al. Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. npj Schizophr. 2019;5:2.

    PubMed  PubMed Central  Google Scholar 

  23. Chou P-H, Yao Y-H, Zheng R-X, Liou Y-L, Liu T-T, Lane H-Y, et al. Deep neural network to differentiate brain activity between patients with first-episode schizophrenia and healthy individuals: a multi-channel near infrared spectroscopy study. Front Psychiatry. 2021;12:655292.

  24. Fatemi SH, Folsom TD. The neurodevelopmental hypothesis of schizophrenia, revisited. Schizophr Bull. 2009;35:528–48.

    PubMed  PubMed Central  Google Scholar 

  25. Grayson B, Barnes SA, Markou A, Piercy C, Podda G, Neill JC. Postnatal phencyclidine (PCP) as a neurodevelopmental animal model of schizophrenia pathophysiology and symptomatology: a review. Curr Top Behav Neurosci. 2015;29:403–28.

    Google Scholar 

  26. Lewis DA, Curley AA, Glausier JR, Volk DW. Cortical parvalbumin interneurons and cognitive dysfunction in schizophrenia. Trends Neurosci. 2012;35:57–67.

    CAS  PubMed  Google Scholar 

  27. Kaalund SS, Riise J, Broberg BV, Fabricius K, Karlsen AS, Secher T, et al. Differential expression of parvalbumin in neonatal phencyclidine-treated rats and socially isolated rats. J Neurochem. 2013;124:548–57.

    CAS  PubMed  Google Scholar 

  28. Kjaerby C, Broberg BV, Kristiansen U, Dalby NO. Impaired GABAergic inhibition in the prefrontal cortex of early postnatal phencyclidine (PCP)-treated rats. Cereb Cortex. 2014;24:2522–32.

  29. Wang C, McInnis J, Ross-Sanchez M, Shinnick-Gallagher P, Wiley JL, Johnson KM. Long-term behavioral and neurodegenerative effects of perinatal phencyclidine administration: implications for schizophrenia. Neuroscience. 2001;107:535–50.

    CAS  PubMed  Google Scholar 

  30. Terranova JP, Chabot C, Barnouin MC, Perrault G, Depoortere R, Griebel G, et al. SSR181507, a dopamine D(2) receptor antagonist and 5-HT(1A) receptor agonist, alleviates disturbances of novelty discrimination in a social context in rats, a putative model of selective attention deficit. Psychopharmacology (Berl). 2005;181:134–44.

    CAS  PubMed  Google Scholar 

  31. Broberg BV, Dias R, Glenthøj BY, Olsen CK. Evaluation of a neurodevelopmental model of schizophrenia—early postnatal PCP treatment in attentional set-shifting. Behav Brain Res. 2008;190:160–63.

    CAS  PubMed  Google Scholar 

  32. Broberg BV, Glenthoj BY, Dias R, Larsen DB, Olsen CK. Reversal of cognitive deficits by an ampakine (CX516) and sertindole in two animal models of schizophrenia—sub-chronic and early postnatal PCP treatment in attentional set-shifting. Psychopharmacology (Berl). 2009;206:631–40.

    CAS  PubMed  Google Scholar 

  33. Amitai N, Markou A. Disruption of performance in the five-choice serial reaction time task induced by administration of N-Methyl-D-aspartate receptor antagonists: relevance to cognitive dysfunction in schizophrenia. Biol Psychiatry. 2010;68:5–16.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Bari A, Theobald DE, Caprioli D, Mar AC, Aidoo-Micah A, Dalley JW, et al. Serotonin modulates sensitivity to reward and negative feedback in a probabilistic reversal learning task in rats. Neuropsychopharmacology. 2010;35:1290–301.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Amitai N, Young JW, Higa K, Sharp RF, Geyer MA, Powell SB. Isolation rearing effects on probabilistic learning and cognitive flexibility in rats. Cogn Affect Behav Neurosci. 2014;14:388–406.

    PubMed  PubMed Central  Google Scholar 

  36. Dalton GL, Wang NY, Phillips AG, Floresco SB. Multifaceted contributions by different regions of the orbitofrontal and medial prefrontal cortex to probabilistic reversal learning. J Neurosci. 2016;36:1996–2006.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Sutton RS, Barto AG. Reinforcement learning: an introduction. MIT Press; 2018.

  38. Correa CMC, Noorman S, Jiang J, Palminteri S, Cohen MX, Lebreton M, et al. How the level of reward awareness changes the computational and electrophysiological signatures of reinforcement learning. J Neurosci. 2018;38:10338–48.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Frank MJ, Moustafa AA, Haughey HM, Curran T, Hutchison KE. Genetic triple dissociation reveals multiple roles for dopamine in reinforcement learning. Proc Natl Acad Sci USA. 2007;104:16311–16.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Parker NF, Baidya A, Cox J, Haetzel LM, Zhukovskaya A, Murugan M, et al. Choice-selective sequences dominate in cortical relative to thalamic inputs to NAc to support reinforcement learning. Cell Rep. 2022;39:110756.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Parker NF, Cameron CM, Taliaferro JP, Lee J, Choi JY, Davidson TJ, et al. Reward and choice encoding in terminals of midbrain dopamine neurons depends on striatal target. Nat Neurosci. 2016;19:845–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Hattori R, Danskin B, Babic Z, Mlynaryk N, Komiyama T. Area-specificity and plasticity of history-dependent value coding during learning. Cell. 2019;177:1858–72.e15

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Huys QJ, Pizzagalli DA, Bogdan R, Dayan P. Mapping anhedonia onto reinforcement learning: a behavioural meta-analysis. Biol Mood Anxiety Disord. 2013;3:1–16.

    Google Scholar 

  44. Katahira K. The relation between reinforcement learning parameters and the influence of reinforcement history on choice behavior. J Math Psychol. 2015;66:59–69.

    Google Scholar 

  45. Burnham KP, Anderson DR, Huyvaert KP. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav Ecol Sociobiol. 2011;65:23–35.

    Google Scholar 

  46. Waltz JA, Gold JM. Probabilistic reversal learning impairments in schizophrenia: further evidence of orbitofrontal dysfunction. Schizophr Res. 2007;93:296–303.

    PubMed  PubMed Central  Google Scholar 

  47. Waltz JA, Kasanova Z, Ross TJ, Salmeron BJ, McMahon RP, Gold JM, et al. The roles of reward, default, and executive control networks in set-shifting impairments in schizophrenia. PLoS One. 2013;8:e57257.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Neill JC, Barnes S, Cook S, Grayson B, Idris NF, McLean SL, et al. Animal models of cognitive dysfunction and negative symptoms of schizophrenia: focus on NMDA receptor antagonism. Pharm Ther. 2010;128:419–32.

    CAS  Google Scholar 

  49. Abdul-Monim Z, Neill JC, Reynolds GP. Sub-chronic psychotomimetic phencyclidine induces deficits in reversal learning and alterations in parvalbumin-immunoreactive expression in the rat. J Psychopharmacol (Oxf, Engl). 2007;21:198–205.

    CAS  Google Scholar 

  50. Brigman JL, Ihne J, Saksida LM, Bussey TJ, Holmes A. Effects of Subchronic Phencyclidine (PCP) Treatment on Social Behaviors, and Operant Discrimination and Reversal Learning in C57BL/6J Mice. Front Behav Neurosci. 2009;3:2

  51. Amodeo DA, Lai CY, Hassan O, Mukamel EA, Behrens MM, Powell SB. Maternal immune activation impairs cognitive flexibility and alters transcription in frontal cortex. Neurobiol Dis. 2019;125:211–18.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Bari BA, Moerke MJ, Jedema HP, Effinger DP, Cohen JY, Bradberry CW. Reinforcement learning modeling reveals a reward-history-dependent strategy underlying reversal learning in squirrel monkeys. Behav Neurosci. 2022;136:46–60

  53. Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science. 1997;275:1593–99.

    CAS  PubMed  Google Scholar 

  54. Corlett PR, Honey GD, Fletcher PC. Prediction error, ketamine and psychosis: an updated model. J Psychopharmacol. 2016;30:1145–55.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Corlett PR, Honey GD, Aitken MRF, Dickinson A, Shanks DR, Absalom AR, et al. Frontal responses during learning predict vulnerability to the psychotogenic effects of ketamine: linking cognition, brain activity, and psychosis. Arch Gen Psychiatry. 2006;63:611–21.

    PubMed  Google Scholar 

  56. Reinhart RMG, Zhu J, Park S, Woodman GF. Medial–frontal stimulation enhances learning in schizophrenia by restoring prediction error signaling. J Neurosci. 2015;35:12232–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Jang AI, Nassar MR, Dillon DG, Frank MJ. Positive reward prediction errors during decision-making strengthen memory encoding. Nat Hum Behav. 2019;3:719–32.

    PubMed  PubMed Central  Google Scholar 

  58. Bai Y, Katahira K, Ohira H. Dual learning processes underlying human decision-making in reversal learning tasks: functional significance and evidence from the model fit to human behavior. Front Psychol. 2014;5:871.

  59. Cinotti F, Fresno V, Aklil N, Coutureau E, Girard B, Marchand AR, et al. Dopamine blockade impairs the exploration-exploitation trade-off in rats. Sci Rep. 2019;9:6770.

    PubMed  PubMed Central  Google Scholar 

  60. Strauss GP, Thaler NS, Matveeva TM, Vogel SJ, Sutton GP, Lee BG, et al. Predicting psychosis across diagnostic boundaries: behavioral and computational modeling evidence for impaired reinforcement learning in schizophrenia and bipolar disorder with a history of psychosis. J Abnorm Psychol. 2015;124:697–708.

    PubMed  Google Scholar 

  61. Oyama K, Hernádi I, Iijima T, Tsutsui K-I. Reward prediction error coding in dorsal striatal neurons. J Neurosci. 2010;30:11447–57.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Cooper JC, Dunne S, Furey T, O’Doherty JP. Human dorsal striatum encodes prediction errors during observational learning of instrumental actions. J Cogn Neurosci. 2012;24:106–18.

    PubMed  Google Scholar 

  63. D’Ardenne K, McClure SM, Nystrom LE, Cohen JD. BOLD responses reflecting dopaminergic signals in the human ventral tegmental area. Science. 2008;319:1264–67.

    PubMed  Google Scholar 

  64. Semba J, Tanaka N, Wakuta M, Suhara T. Neonatal phencyclidine treatment selectively attenuates mesolimbic dopamine function in adult rats as revealed by methamphetamine-induced behavior and c-fos mRNA expression in the brain. Synapse. 2001;40:11–8.

    CAS  PubMed  Google Scholar 

  65. Nakao K, Jeevakumar V, Jiang SZ, Fujita Y, Diaz NB, Pretell Annan CA, et al. Schizophrenia-like dopamine release abnormalities in a mouse model of NMDA receptor hypofunction. Schizophr Bull. 2019;45:138–47.

  66. Uehara T, Sumiyoshi T, Seo T, Matsuoka T, Itoh H, Suzuki M, et al. Neonatal exposure to MK-801, an N-methyl-d-aspartate receptor antagonist, enhances methamphetamine-induced locomotion and disrupts sensorimotor gating in pre- and postpubertal rats. Brain Res. 2010;1352:223–30.

    CAS  PubMed  Google Scholar 

  67. Cavanagh JF, Gregg D, Light GA, Olguin SL, Sharp RF, Bismark AW, et al. Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms. Transl Psychiatry. 2021;11:482.

    PubMed  PubMed Central  Google Scholar 

  68. Cavanagh JF, Olguin SL, Talledo JA, Kotz JE, Roberts BZ, Nungaray JA, et al. Amphetamine alters an EEG marker of reward processing in humans and mice. Psychopharmacology (Berl). 2022;239:923–33.

    CAS  PubMed  Google Scholar 

  69. Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology. 2021;46:176–90.

    PubMed  Google Scholar 

  70. Lin E, Kuo PH, Liu YL, Yu YW, Yang AC, Tsai SJ. A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers. Front Psychiatry. 2018;9:290.

    PubMed  PubMed Central  Google Scholar 

  71. Jaworska N, de la Salle S, Ibrahim MH, Blier P, Knott V. Leveraging machine learning approaches for predicting antidepressant treatment response using electroencephalography (EEG) and clinical data. Front Psychiatry. 2018;9:768.

    PubMed  Google Scholar 

  72. Oh J, Oh B-L, Lee K-U, Chae J-H, Yun K. Identifying schizophrenia using structural MRI with a deep learning algorithm. Fron Psychiatry. 2020;11:16.

  73. Sun J, Cao R, Zhou M, Hussain W, Wang B, Xue J, et al. A hybrid deep neural network for classification of schizophrenia using EEG Data. Sci Rep. 2021;11:4706.

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Cortes-Briones JA, Tapia-Rivas NI, D’Souza DC, Estevez PA. Going deep into schizophrenia with artificial intelligence. Schizophr Res. 2022;245:122–40.

  75. Hill CE, Reynolds EL, Burke JF, Banerjee M, Kerber KA, Magliocco B, et al. Increasing out-of-pocket costs for neurologic care for privately insured patients. Neurology. 2021;96:e322–32.

    PubMed  PubMed Central  Google Scholar 

  76. van Beek EJR, Kuhl C, Anzai Y, Desmond P, Ehman RL, Gong Q, et al. Value of MRI in medicine: More than just another test? J Magn Reson Imaging. 2019;49:e14–25.

    PubMed  Google Scholar 

  77. Yan W, Calhoun V, Song M, Cui Y, Yan H, Liu S, et al. Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data. EBioMedicine. 2019;47:543–52.

    PubMed  PubMed Central  Google Scholar 

  78. Salvador R, Canales-Rodríguez E, Guerrero-Pedraza A, Sarró S, Tordesillas-Gutiérrez D, Maristany T, et al. Multimodal integration of brain images for MRI-based diagnosis in schizophrenia. Front Neurosci. 2019;13:1203.

  79. Kim J, Calhoun VD, Shim E, Lee JH. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage. 2016;124:127–46. Pt A

    PubMed  Google Scholar 

  80. Niu YW, Lin QH, Qiu Y, Kuang LD, Calhoun VD. Sample Augmentation for Classification of Schizophrenia Patients and Healthy Controls Using ICA of fMRI Data and Convolutional Neural Networks. Tenth International Conference on Intelligent Control and Information Processing. 2019:297–302. https://doi.org/10.1109/ICICIP47338.2019.9012169

  81. Zhao J, Huang J, Zhi D, Yan W, Ma X, Yang X, et al. Functional network connectivity (FNC)-based generative adversarial network (GAN) and its applications in classification of mental disorders. J Neurosci Methods. 2020;341:108756.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Metha JA, Brian ML, Oberrauch S, Barnes SA, Featherby TJ, Bossaerts P, et al. Separating probability and reversal learning in a novel probabilistic reversal learning task for mice. Front Behav Neurosci. 2020;13:270–70.

    PubMed  PubMed Central  Google Scholar 

  83. Ito M, Doya K. Validation of decision-making models and analysis of decision variables in the rat basal ganglia. J Neurosci. 2009;29:9861–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Pearce JM, Hall G. A model for Pavlovian learning: variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychological Rev. 1980;87:532–52.

    CAS  Google Scholar 

  85. Bartolo R, Averbeck BB. Prefrontal cortex predicts state switches during reversal learning. Neuron. 2020;106:1044–54.e4

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Gold JM, Waltz JA, Matveeva TM, Kasanova Z, Strauss GP, Herbener ES, et al. Negative symptoms and the failure to represent the expected reward value of actions: behavioral and computational modeling evidence. Arch Gen Psychiatry. 2012;69:129–38.

    PubMed  PubMed Central  Google Scholar 

  87. Taylor Tavares JV, Clark L, Furey ML, Williams GB, Sahakian BJ, Drevets WC. Neural basis of abnormal response to negative feedback in unmedicated mood disorders. NeuroImage. 2008;42:1118–26.

    PubMed  Google Scholar 

  88. Smith P, Benzina N, Vorspan F, Mallet L, N’Diaye K. Compulsivity and probabilistic reversal learning in OCD and cocaine addiction. Eur Psychiatry. 2015;30(Supplement):S110–11.

    Google Scholar 

  89. Ersche KD, Roiser JP, Robbins TW, Sahakian BJ. Chronic cocaine but not chronic amphetamine use is associated with perseverative responding in humans. Psychopharmacology (Berl). 2008;197:421–31.

    CAS  PubMed  Google Scholar 

  90. Barnes SA, Der-Avakian A, Young JW. Preclinical models to investigate mechanisms of negative symptoms in schizophrenia. Schizophr Bull. 2017;43:706–11.

    PubMed  PubMed Central  Google Scholar 

  91. Kovács I, Richman MJ, Janka Z, Maraz A, Andó B. Decision making measured by the Iowa Gambling Task in alcohol use disorder and gambling disorder: a systematic review and meta-analysis. Drug Alcohol Depend. 2017;181:152–61.

    PubMed  Google Scholar 

  92. Modirrousta M, Meek BP, Sareen J, Enns MW. Impaired trial-by-trial adjustment of cognitive control in obsessive compulsive disorder improves after deep repetitive transcranial magnetic stimulation. BMC Neurosci. 2015;16:63.

    PubMed  PubMed Central  Google Scholar 

  93. Dillon DG, Wiecki T, Pechtel P, Webb C, Goer F, Murray L, et al. A computational analysis of flanker interference in depression. Psychological Med. 2015;45:2333–44.

    CAS  Google Scholar 

  94. Beard C, Donahue RJ, Dillon DG, Van’t Veer A, Webber C, Lee J, et al. Abnormal error processing in depressive states: a translational examination in humans and rats. Transl Psychiatry. 2015;5:e564.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Strauss GP, Whearty KM, Morra LF, Sullivan SK, Ossenfort KL, Frost KH. Avolition in schizophrenia is associated with reduced willingness to expend effort for reward on a Progressive Ratio task. Schizophr Res. 2016;170:198–204.

    PubMed  Google Scholar 

  96. Han H, Li Y, Zhu X. Convolutional neural network learning for generic data classification. Inf Sci. 2019;477:448–65.

    Google Scholar 

  97. Livieris IE, Pintelas E, Pintelas P. A CNN–LSTM model for gold price time-series forecasting. Neural Comput Appl. 2020;32:17351–60.

    Google Scholar 

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Funding

This work was funded by NIMH grants R01MH108653 (SAB) and R01MH111676 (DGD).

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  1. These authors contributed equally: Michael M. Tranter, Samarth Aggarwal.

Authors and Affiliations

  1. Department of Psychiatry, University of California San Diego, La Jolla, CA, 92093, USA

    Michael M. Tranter, Samarth Aggarwal, Jared W. Young & Samuel A. Barnes

  2. Department of Mental Health, VA San Diego Healthcare System, La Jolla, CA, 92093, USA

    Michael M. Tranter, Jared W. Young & Samuel A. Barnes

  3. Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, 02478, USA

    Daniel G. Dillon

  4. Harvard Medical School, Boston, MA, 02115, USA

    Daniel G. Dillon

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SAB designed and ran the experiments, analyzed the data, and primarily wrote the manuscript. MMT assisted in writing the manuscript. SA assisted with implementing the deep neural network and with writing the manuscript. JWY assisted with writing the manuscript. DGD assisted with implementing the Q-learning analysis and writing the manuscript.

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Correspondence to Samuel A. Barnes.

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Tranter, M.M., Aggarwal, S., Young, J.W. et al. Reinforcement learning deficits exhibited by postnatal PCP-treated rats enable deep neural network classification. Neuropsychopharmacol. 48, 1377–1385 (2023). https://doi.org/10.1038/s41386-022-01514-y

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