본문 바로가기
KERD
논문KCI 등재

Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches

2

조회

0

다운로드

3

인용

0

참고문헌

키워드

초록

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

Abstract

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

참고문헌 (82)

  1. Maia TV, "The role of serotonin in orbitofrontal function and obsessive-compulsive disorder", Clin Psychol Sci, 3, pp.460~482 (2015)
  2. Stephan KE, "Charting the landscape of priority problems in psychiatry, part 1 : classification and diagnosis", Lancet Psychiatry, 3, pp.77~83 (2016)
  3. Talepasand S, "Psychiatric disorders in children and adolescents : prevalence and sociodemographic correlates in Semnan Province in Iran", Asian J Psychiatr, 40, pp.9~14 (2019)
  4. Khaleghi A, "Epidemiology of psychiatric disorders in children and adolescents; in Tehran, 2017", Asian J Psychiatr, 37, pp.146~153 (2018)
  5. Mohammadi MR, "Prevalence and correlates of psychiatric disorders in a national survey of Iranian children and adolescents", Iran J Psychiatry, 14, pp.1~15 (2019)
  6. Mohammadi MR, "Prevalence of autism and its comorbidities and the relationship with maternal psychopathology : a national population-based study", Arch Iran Med, 22, pp.546~553 (2019)
  7. Zarafshan H, "Electroencephalogram complexity analysis in children with attention-deficit", hyperactivity disorder during a visual cognitive task, 2016, pp.38, 361~369
  8. Khaleghi A, "Visual and auditory steady-state responses in attention-deficit", hyperactivity disorder, 2019, pp.269, 645~655
  9. Mostafavi SA, "Noninvasive brain stimulation in alcohol craving : a systematic review and meta-analysis", Prog Neuropsychopharmacol Biol Psychiatry, 101, pp.109938~ (2020)
  10. Khaleghi A, "Effects of transcranial direct current stimulation of prefrontal cortex on risk-taking behavior", Psychiatry Clin Neurosci, 74, pp.455~465 (2020)
  11. Ali Khaleghi, "Effects of Non-invasive Neurostimulation on Autism Spectrum Disorder: A Systematic Review", Clinical Psychopharmacology and Neuroscience, 18(4), pp.527~552 (2020), 대한정신약물학회[DOI]
  12. Wang XJ, "Computational psychiatry", Neuron, 84, pp.638~654 (2014)
  13. Friston KJ, "Computational psychiatry : the brain as a phantastic organ", Lancet Psychiatry, 1, pp.148~158 (2014)
  14. Huys QJ, "Computational psychiatry as a bridge from neuroscience to clinical applications", Nat Neurosci, 19, pp.404~413 (2016)
  15. Jaeger D, "Encyclopedia of computational neuroscience" (2015), Springer
  16. Dayan P, "Book review : Theoretical neuroscience : computational and mathematical modeling of neural systems", J Cognit Neurosci, 15, pp.154~155 (2003)
  17. Kriegeskorte N, "Cognitive computational neuroscience", Nat Neurosci, 21, pp.1148~1160 (2018)
  18. Maia TV, "From reinforcement learning models to psychiatric and neurological disorders", Nat Neurosci, 14, pp.154~162 (2011)
  19. Deserno L, "Computational approaches to schizophrenia : a perspective on negative symptoms", Schizophr Res, 186, pp.46~54 (2017)
  20. Valton V, "Comprehensive review : computational modelling of schizophrenia", Neurosci Biobehav Rev, 83, pp.631~646 (2017)
  21. Yahata N, "Computational neuroscience approach to biomarkers and treatments for mental disorders", Psychiatry Clin Neurosci, 71, pp.215~237 (2017)
  22. Gold A, "Simulation of mental disorders: II. Computer models, purposes and future directions", Isr J Psychiatry Relat Sci, 53, pp.73~81 (2016)
  23. Karbalaei Akbari M, "A bioinspired optoelectronically engineered artificial neurorobotics device with sensorimotor functionalities", Nat Commun, 10, pp.3873~ (2019)
  24. Falotico E, "Connecting artificial brains to robots in a comprehensive simulation framework : the neurorobotics platform", Front Neurorobot, 11, pp.2~ (2017)
  25. Eliasmith C, "A large-scale model of the functioning brain", Science, 338, pp.1202~1205 (2012)
  26. Zuckerman O, "KIP3: robotic companion as an external cue to students with ADHD", TEI '16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction, pp.621~626 (2016)
  27. Rakhymbayeva N, "A long-term study of robot-assisted therapy for children with severe autism and ADHD", HRI '20:Companion of the 2020 ACM, 2020, 401~402
  28. O’Reilly RC, "Biologically based computational models of high-level cognition", Science, 314, pp.91~94 (2006)
  29. Markram H, "The human brain project", Sci Am, 306, pp.50~55 (2012)
  30. Tsoutsouras V, "Simulation of healthy and epileptiform brain activity using cellular automata", Int J Bifurc Chaos, 22, pp.1250229~ (2012)
  31. Javanbakht A, "Editorial : Neurobiological models of psychotherapy", Front Behav Neurosci, 13, pp.144~ (2019)
  32. Zilcha-Mano S, "Not just nonspecific factors : the roles of alliance and expectancy in treatment, and their neurobiological underpinnings", Front Behav Neurosci, 12, pp.293~ (2019)
  33. Iglesias S, "Models of neuromodulation for computational psychiatry", Wiley Interdiscip Rev Cogn Sci, 8, pp.e1420~ (2017)
  34. Murray JD, "Linking microcircuit dysfunction to cognitive impairment : effects of disinhibition associated with schizophrenia in a cortical working memory model", Cereb Cortex, 24, pp.859~872 (2014)
  35. Lisman JE, "Circuit-based framework for understanding neurotransmitter and risk gene interactions in schizophrenia", Trends Neurosci, 31, pp.234~242 (2008)
  36. Krystal JH, "Psychotomimetic, perceptual, cognitive, and neuroendocrine responses", Arch Gen Psychiatry, 51, pp.199~214 (1994)
  37. Anticevic A, "NMDA receptor function in large-scale anticorrelated neural systems with implications for cognition and schizophrenia", Proc Natl Acad Sci U S A, 109, pp.16720~16725 (2012)
  38. Frank MJ, "Dynamic dopamine modulation in the basal ganglia : a neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism", J Cogn Neurosci, 17, pp.51~72 (2005)
  39. Gurney KN, "A new framework for cortico-striatal plasticity : behavioural theory meets in vitro data at the reinforcement-action interface", PLoS Biol, 13, pp.e1002034~ (2015)
  40. Frank MJ, "An introduction to model-based cognitive neuroscience", pp.159~177 (2015), Springer
  41. Chen C, "Reinforcement learning in depression : a review of computational research", Neurosci Biobehav Rev, 55, pp.247~267 (2015)
  42. Deserno L, "Reinforcement learning and dopamine in schizophrenia : dimensions of symptoms or specific features of a disease group?", Front Psychiatry, 4, pp.172~ (2013)
  43. Maia TV, "Reinforcement learning, conditioning, and the brain : successes and challenges", Cogn Affect Behav Neurosci, 9, pp.343~364 (2009)
  44. Eshel N, "Arithmetic and local circuitry underlying dopamine prediction errors", Nature, 525, pp.243~246 (2015)
  45. Montague PR, "Computational roles for dopamine in behavioural control", Nature, 431, pp.760~767 (2004)
  46. Moustafa AA, "A dopaminergic basis for working memory, learning and attentional shifting in Parkinsonism", Neuropsychologia, 46, pp.3144~3156 (2008)
  47. Schultz W, "Explicit neural signals reflecting reward uncertainty", Philos Trans R Soc Lond B Biol Sci, 363, pp.3801~3811 (2008)
  48. Adams RA, "Computational psychiatry : towards a mathematically informed understanding of mental illness", J Neurol Neurosurg Psychiatry, 87, pp.53~63 (2016)
  49. Murray GK, "Substantia nigra", ventral tegmental reward prediction error disruption in psychosis, 2008, pp.13, 239~
  50. Corlett PR, "Disrupted prediction-error signal in psychosis : evidence for an associative account of delusions", Brain, 130(Pt 9), pp.2387~2400 (2007)
  51. Romaniuk L, "Midbrain activation during Pavlovian conditioning and delusional symptoms in schizophrenia", Arch Gen Psychiatry, 67, pp.1246~1254 (2010)
  52. Schlagenhauf F, "Reward feedback alterations in unmedicated schizophrenia patients : relevance for delusions", Biol Psychiatry, 65, pp.1032~1039 (2009)
  53. Kapur S, "Psychosis as a state of aberrant salience : a framework linking biology, phenomenology, and pharmacology in schizophrenia", Am J Psychiatry, 160, pp.13~23 (2003)
  54. Waltz JA, "Patients with schizophrenia have a reduced neural response to both unpredictable and predictable primary reinforcers", Neuropsychopharmacology, 34, pp.1567~1577 (2009)
  55. Gradin VB, "Expected value and prediction error abnormalities in depression and schizophrenia", Brain, 134(Pt 6), pp.1751~1764 (2011)
  56. Gold JM, "Negative symptoms and the failure to represent the expected reward value of actions : behavioral and computational modeling evidence", Arch Gen Psychiatry, 69, pp.129~138 (2012)
  57. Gold JM, "Negative symptoms of schizophrenia are associated with abnormal effort-cost computations", Biol Psychiatry, 74, pp.130~136 (2013)
  58. Garbusow M, "Pavlovian-to-instrumental transfer effects in the nucleus accumbens relate to relapse in alcohol dependence", Addict Biol, 21, pp.719~731 (2016)
  59. Sonuga-Barke EJ, "The dual pathway model of AD", HD : an elaboration of neuro-developmental characteristics, 2003, pp.27, 593~604
  60. Smith AJ, "A computational model of the functional role of the ventral-striatal D2 receptor in the expression of previously acquired behaviors", Neural Comput, 17, pp.361~395 (2005)
  61. Huys QJ, "Mapping anhedonia onto reinforcement learning : a behavioural metaanalysis", Biol Mood Anxiety Disord, 3, pp.12~ (2013)
  62. Yoshida W, "Cooperation and heterogeneity of the autistic mind", J Neurosci, 30, pp.8815~8818 (2010)
  63. Kishida KT, "Imaging models of valuation during social interaction in humans", Biol Psychiatry, 72, pp.93~100 (2012)
  64. Kishida KT, "Neuroeconomic approaches to mental disorders", Neuron, 67, pp.543~554 (2010)
  65. Sanfey AG, "Social decision-making : insights from game theory and neuroscience", Science, 318, pp.598~602 (2007)
  66. Yoshida W, "Neural mechanisms of belief inference during cooperative games", J Neurosci, 30, pp.10744~10751 (2010)
  67. Moutoussis M, "Bayesian inferences about the self(and others) : a review", Conscious Cogn, 25, pp.67~76 (2014)
  68. Mokhtari Hashtjini M, "Comparison of the effects of deep brain stimulation of the prelimbic cortex and basolateral amygdala for facilitation of extinction process of conditioned fear", Arch Neurosci, 7 (2020)
  69. Huys QJM, "A Bayesian formulation of behavioral control", Cognition, 113, pp.314~328 (2009)
  70. Maia TV, "Fear conditioning and social groups : statistics, not genetics", Cogn Sci, 33, pp.1232~1251 (2009)
  71. Browning M, "Anxious individuals have difficulty learning the causal statistics of aversive environments", Nat Neurosci, 18, pp.590~596 (2015)
  72. Fornito A, "Computational approaches to understanding mental dysfunction : progress, challenges, and new frontiers", Biol Psychiatry Cogn Neurosci Neuroimaging, 3, pp.728~730 (2018)
  73. Einevoll GT, "The scientific case for brain simulations", Neuron, 102, pp.735~744 (2019)
  74. Collins AG, "Opponent actor learning(OpAL) : modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive", Psychol Rev, 121, pp.337~366 (2014)
  75. Mohammad Reza Mohammadi, "EEG Classification of ADHD and Normal Children Using Non-linear Features and Neural Network", Biomedical Engineering Letters (BMEL), 6(2), pp.66~73 (2016), 대한의용생체공학회[DOI]
  76. Khaleghi A, "EEG classification of adolescents with type I and type II of bipolar disorder", Australas Phys Eng Sci Med, 38, pp.551~559 (2015)
  77. Mostafavi SA, "Is transcranial direct current stimulation an effective modality in reducing food craving? A systematic review and meta-analysis", Nutr Neurosci, 23, pp.55~67 (2020)
  78. Moeini M, "Characteristics of alpha band frequency in adolescents with bipolar II disorder : a resting-state QEEG study", Iran J Psychiatry, 10, pp.8~12 (2015)
  79. Khaleghi A, "Applicable features of electroencephalogram for ADHD diagnosis", Res Biomed Eng, 36, pp.1~11 (2020)
  80. Mohammad Reza Mohammadi, "Transsexualism: A Different Viewpoint to Brain Changes", Clinical Psychopharmacology and Neuroscience, 16(2), pp.136~143 (2018), 대한정신약물학회[DOI]
  81. Wiecki TV, "Computational cognitive biomarker for early-stage Huntington’s disease", PLoS One, 11, pp.e0148409~ (2016)
  82. Brodersen KH, "Dissecting psychiatric spectrum disorders by generative embedding", Neuroimage Clin, 4, pp.98~111 (2013)

관련 논문AI추천

이 간행물의 다른 논문 (19건)