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Engineering >> 2020, Volume 6, Issue 4 doi: 10.1016/j.eng.2019.06.008

A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric Disorders

a College of Computer and Information Science, Southwest University, Chongqing 400715, China
b Library of Chengdu University, Chengdu University, Chengdu 610106, China
c The Mental Health Center and Psychiatric Laboratory & the State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
d College of Computer Science, Sichuan University, Chengdu 610065, China

Received: 2019-01-21 Revised: 2019-06-07 Accepted: 2019-06-20 Available online: 2019-08-28

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Abstract

A number of brain research projects have recently been carried out to study the etiology and mechanisms of psychiatric disorders. Although psychiatric disorders are part of the brain sciences, psychiatrists still diagnose them based on subjective experience rather than by gaining insights into the pathophysiology of the diseases. Therefore, it is urgent to have a clear understanding of the etiology and pathogenesis of major psychiatric diseases, which can help in the development of effective treatments and interventions. Artificial intelligence (AI)-based applications are being quickly developed for psychiatric research and diagnosis, but there is no systematic review that summarizes their applications. For this reason, this study briefly reviews three main brain observation techniques used to study psychiatric disorders—namely, magnetic resonance imaging (MRI), electroencephalography (EEG), and kinesics-based diagnoses—along with related AI applications and algorithms. Finally, we discuss the challenges, opportunities, and future study directions of AI-based applications.

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References

[ 1 ] Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. JAMA Psychiatry 2015;72(4):334–41. link1

[ 2 ] GBD 2017 DALYs and HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392(10159):1859–922. link1

[ 3 ] The US Burden of Disease Collaborators. The state of US health, 1990–2016: burden of diseases, injuries, and risk factors among US states. JAMA 2018;319 (14):1444–72. link1

[ 4 ] Rose N. The Human Brain Project: social and ethical challenges. Neuron 2014;82(6):1212–5. link1

[ 5 ] Iritani S, Habuchi C, Sekiguchi H, Torii Y. Brain research and clinical psychiatry: establishment of a psychiatry brain bank in Japan. Nagoya J Med Sci 2018;80(3):309–15. link1

[ 6 ] Poo MM, Du JL, Ip NY, Xiong ZQ, Xu B, Tan T. China Brain Project: basic neuroscience, brain diseases, and brain-inspired computing. Neuron 2016;92 (3):591–6. link1

[ 7 ] Insel TR. The NIMH Research Domain Criteria (RDoC) project: precision medicine for paychiatry. Am J Psychiatry 2014;171(4):395–7. link1

[ 8 ] 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. link1

[ 9 ] Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med 2019;25:24–9. link1

[10] Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 2018;14:91–118. link1

[11] Sarma GP, Hay NJ, Safron A. AI safety and reproducibility: establishing robust foundations for the neuropsychology of human values. In: Gallina B, Skavhaug A, Schoitsch E, Bitsch F, editors. SAFECOMP 2018: computer safety, reliability, and security; 2018 Sep 18–21; Västerås, Sweden. Cham: Springer; 2018. p. 507–12. link1

[12] Stead WW. Clinical implications and challenges of artificial intelligence and deep learning. JAMA 2018;320(11):1107–8. link1

[13] Gao H, Yin Z, Cao Z, Zhang L. Developing an agent-based drug model to investigate the synergistic effects of drug combinations. Molecules 2017;22 (12):2209. link1

[14] Xia Y, Yang CW, Hu N, Yang ZZ, He XY, Li TT, et al. Exploring the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme patients by a novel survival analysis model. BMC Genomics 2017;18:950. link1

[15] Zhang L, Xiao M, Zhou JS, Yu J. Lineage-associated underrepresented permutations (LAUPs) of mammalian genomic sequences based on a Jellyfish-based LAUPs analysis application (JBLA). Bioinformatics 2018;34 (21):3624–30. link1

[16] Jan A, Meng HY, Gaus YFBA, Zhang F. Artificial intelligent system for automatic depression level analysis through visual and vocal expressions. IEEE Trans Cogn Dev Syst 2018;10(3):668–80. link1

[17] Wen HW, Liu Y, Wang SP, Li ZY, Zhang JS, Peng Y, et al. Multi-threshold white matter structural networks fusion for accurate diagnosis of early Tourette syndrome children. In: Armato SG, Petrick NA, editors. Medical Imaging 2017: Computer-Aided Diagnosis; 2017 Feb 11–16; Orlando, FL, USA. Bellingham: SPIE; 2017. p. 10134 1Q. link1

[18] Li T, Cheng Z, Zhang L. Developing a novel parameter estimation method for agent-based model in immune system simulation under the framework of history matching: a case study on influenza a virus infection. Int J Mol Sci 2017;18(12):2592. link1

[19] Peng H, Peng T, Wen J, Engler DA, Matsunami RK, Su J, et al. Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach. Bioinformatics 2014;30(13):1899–907. link1

[20] Luxton DD, editor. Artificial intelligence in behavioral and mental health care. London: Academic Press; 2015. link1

[21] Wallace R. Embodied cognition and its disorders. In: Wallace R, editor. Computational psychiatry. New York: Springer; 2017. p. 129–52. link1

[22] Rosen BR, Huang SY, Stufflebeam SM. Pushing the limits of human neuroimaging. JAMA 2015;314(10):993–4. link1

[23] Hategan A, Bourgeois JA, Cheng T, Young J. Neuropsychology and neuroimaging in clinical geriatric psychiatry. In: Hategan A, Bourgeois JA, Cheng T, Young J, editors. Geriatric psychiatry study guide. Cham: Springer; 2018. p. 23–38. link1

[24] Park MTM, Raznahan A, Shaw P, Gogtay N, Lerch JP, Chakravarty MM. Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia. J Psychiatry Neurosci 2018;43(3):201–12. link1

[25] Wintermark M, Colen R, Whitlow CT, Zaharchuk G. The vast potential and bright future of neuroimaging. Br J Radiol 2018;91(1087):20170505. link1

[26] Webb S. Deep learning for biology. Nature 2018;554(7693):555–7. link1

[27] Böhle M, Eitel F, Weygandt M, Ritter K. Visualizing evidence for Alzheimer’s disease in deep neural networks trained on structural MRI data. 2019. arXiv:1903.07317.

[28] Carin L, Pencina MJ. On deep learning for medical image analysis. JAMA 2018;320(11):1192–3. link1

[29] Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF. From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 2015;57:328–49. link1

[30] Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 2017;145(Pt B):137–65. link1

[31] Vieira S, Pinaya WHL, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev 2017;74(Pt A):58–75. link1

[32] Bengio Y. Learning deep architectures for AI. Hanover: Now Publishers Inc.; 2009. link1

[33] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44. link1

[34] Calhoun VD, Sui J. Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol Psychiatry Cogn Neurosci Neuroimaging 2016;1(3):230–44. link1

[35] Plis SM, Hjelm DR, Salakhutdinov R, Allen EA, Bockholt HJ, Long JD, et al. Deep learning for neuroimaging: a validation study. Front Neurosci 2014;8:229. link1

[36] Payan A, Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. 2015. arXiv:1502.02506.

[37] Hosseini-Asl E, Ghazal M, Mahmoud A, Aslantas A, Shalaby AM, Casanova MF, et al. Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Front Biosci 2018;23:584–96. link1

[38] Koyamada S, Shikauchi Y, Nakae K, Koyama M, Ishii S. Deep learning of fMRI big data: a novel approach to subject-transfer decoding. 2015. arXiv:1502.00093.

[39] Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin 2018;17:16–23. link1

[40] Grotegerd D, Suslow T, Bauer J, Ohrmann P, Arolt V, Stuhrmann A, et al. Discriminating unipolar and bipolar depression by means of fMRI and pattern classification: a pilot study. Eur Arch Psychiatry Clin Neurosci 2013;263 (2):119–31. link1

[41] Kermany DS, Goldbaum M, Cai W, Valentim CCS, Liang H, Baxter SL, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 2018;172(5):1122. link1

[42] Hannesdóttir DK, Doxie J, Bell MA, Ollendick TH, Wolfe CD. A longitudinal study of emotion regulation and anxiety in middle childhood: associations with frontal EEG asymmetry in early childhood. Dev Psychobiol 2010;52 (2):197–204. link1

[43] Avram J, Baltes FR, Miclea M, Miu AC. Frontal EEG activation asymmetry reflects cognitive biases in anxiety: evidence from an emotional face Stroop task. Appl Psychophysiol Biofeedback 2010;35(4):285–92. link1

[44] Thibodeau R, Jorgensen RS, Kim S. Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. J Abnorm Psychol 2006;115 (4):715–29. link1

[45] Hosseinifard B, Moradi MH, Rostami R. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal. Comput Methods Programs Biomed 2013;109(3):339–45. link1

[46] Field T, Diego M. Maternal depression effects on infant frontal EEG asymmetry. Int J Neurosci 2008;118(8):1081–108. link1

[47] Iosifescu DV, Greenwald S, Devlin P, Mischoulon D, Denninger JW, Alpert JE, et al. Frontal EEG predictors of treatment outcome in major depressive disorder. Eur Neuropsychopharmacol 2009;19(11):772–7. link1

[48] Bisch J, Kreifelts B, Bretscher J, Wildgruber D, Fallgatter A, Ethofer T. Emotion perception in adult attention-deficit hyperactivity disorder. J Neural Transm 2016;123(8):961–70. link1

[49] Lopez-Duran NL, Kuhlman KR, George C, Kovacs M. Facial emotion expression recognition by children at familial risk for depression: high-risk boys are oversensitive to sadness. J Child Psychol Psychiatry 2013;54(5):565–74. link1

[50] Ooi KEB, Lech M, Allen NB. Multichannel weighted speech classification system for prediction of major depression in adolescents. IEEE Trans Biomed Eng 2013;60(2):497–506. link1

[51] Scherer S, Stratou G, Mahmoud M, Boberg J, Gratch J, Rizzo A, et al. Automatic behavior descriptors for psychological disorder analysis. Proceedings of 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition; 2013 Apr 22–26; Shanghai, China. Piscataway: IEEE; 2013. link1

[52] Girard JM, Cohn JF, Mahoor MH, Mavadati S, Rosenwald DP. Social risk and depression: evidence from manual and automatic facial expression analysis. Proceedings of 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition; 2013 Apr 22–26; Shanghai, China. Piscataway: IEEE; 2013. link1

[53] Wang P, Barrett F, Martin E, Milonova M, Gur RE, Gur RC, et al. Automated video-based facial expression analysis of neuropsychiatric disorders. J Neurosci Methods 2008;168(1):224–38. link1

[54] Zhu Y, Shang Y, Shao Z, Guo G. Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Trans Affect Comput 2018;9(4):578–84. link1

[55] Kaletsch M, Pilgramm S, Bischoff M, Kindermann S, Sauerbier I, Stark R, et al. Major depressive disorder alters perception of emotional body movements. Front Psychiatry 2014;5:4. link1

[56] Dhamecha TI, Singh R, Vatsa M, Kumar A. Recognizing disguised faces: human and machine evaluation. PLoS ONE 2014;9(7):e99212. link1

[57] Li M, Xu H, Liu X, Lu S. Emotion recognition from multichannel EEG signals using k-nearest neighbor classification. Technol Health Care 2018;26(Suppl 1):509–19. link1

[58] Righi G, Peissig JJ, Tarr MJ. Recognizing disguised faces. Vis Cogn 2012;20 (2):143–69. link1

[59] Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. 3rd ed. Boca Raton: CRC Press; 2014. link1

[60] Tsigelny IF. Artificial intelligence in drug combination therapy. Brief Bioinform 2018 [Epub 2018 Feb 9]. link1

[61] Zhang L, Bai W, Yuan N, Du Z. Comprehensively benchmarking applications for detecting copy number variation. PLoS Comput Biol 2019;15(5): e1007069. link1

[62] Glick M, Jenkins JL, Nettles JH, Hitchings H, Davies JW. Enrichment of highthroughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and Laplacian-modified naive Bayesian classifiers. J Chem Inf Model 2006;46(1):193–200. link1

[63] Ferrante M, Redish AD, Oquendo MA, Averbeck BB, Kinnane ME, Gordon JA. Computational psychiatry: a report from the 2017 NIMH workshop on opportunities and challenges. Mol Psychiatry 2019;24:479–83. link1

[64] Friston KJ, Redish AD, Gordon JA. Computational nosology and precision psychiatry. Compr Psychiatry 2017;1:2–23. link1

[65] Anticevic A, Murray JD, editors. Computational psychiatry: mathematical modeling of mental illness. London: Academic Press; 2017. link1

[66] Grove TB, Yao B, Mueller SA, McLaughlin M, Ellingrod VL, McInnis MG, et al. A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis. Psychiatry Res 2018;265:271–8. link1

[67] Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression. 3rd ed. Toronto: John Wiley & Sons, Inc.; 2013. link1

[68] Pregibon D. Logistic regression diagnostics. Ann Stat 1981;9(4):705–24. link1

[69] Ng A. CS229 Lecture notes: Part G. 2000.

[70] Hagen E, Sømhovd M, Hesse M, Arnevik EA, Erga AH. Measuring cognitive impairment in young adults with polysubstance use disorder with MoCA or BRIEF-A—the significance of psychiatric symptoms. J Subst Abuse Treat 2019;97:21–7. link1

[71] Barker LC, Gruneir A, Fung K, Herrmann N, Kurdyak P, Lin E, et al. Predicting psychiatric readmission: sex-specific models to predict 30-day readmission following acute psychiatric hospitalization. Soc Psychiatry Psychiatr Epidemiol 2018;53(2):139–49. link1

[72] Shen CC, Hu LY, Tsai SJ, Yang AC, Chen PM, Hu YH. Risk stratification for the early diagnosis of borderline personality disorder using psychiatric co– morbidities. Early Interv Psychiatry 2018;12(4):605–12. link1

[73] Zhang L, Li J, Yin K, Jiang Z, Li T, Hu R, et al. Computed tomography angiography-based analysis of high-risk intracerebral haemorrhage patients by employing a mathematical model. BMC Bioinform 2019;20(Suppl 7):193. link1

[74] Su J, Zhang H. A fast decision tree learning algorithm. In: Proceedings of the 21st National Conference on Artificial Intelligence; 2006 Jul 16–20; Boston, MA, USA. Palo Alto. AAAI Press; 2006. p. 500–5. link1

[75] Nowozin S. Improved information gain estimates for decision tree induction. 2012. arXiv:1206.4620.

[76] Quinlan JR. Induction of decision trees. Mach Learn 1986;1(1):81–106. link1

[77] Carpenter KLH, Sprechmann P, Calderbank R, Sapiro G, Egger HL. Quantifying risk for anxiety disorders in preschool children: a machine learning approach. PLoS ONE 2016;11(11):e0165524. link1

[78] Sattler AF, Whiteside SPH, Bentley JP, Young J. Development and validation of a brief screening procedure for pediatric obsessive-compulsive disorder derived from the Spence Children’s Anxiety Scale. J Obsessive Compuls Relat Disord 2018;16:29–35. link1

[79] Doshi AA, Sevugan P, Swarnalatha P. Modified support vector machine algorithm to reduce misclassification and optimizing time complexity. In: Swarnalatha P, Sevugan P, editors. Big data analytics for satellite image processing and remote sensing. Hershey: IGI Global; 2018. p. 34–56. link1

[80] Peng Z, Hu Q, Dang J. Multi-kernel SVM based depression recognition using social media data. Int J Mach Learn Cybern 2019;10(1):43–57. link1

[81] Al-Shargie F, Tang TB, Badruddin N, Kiguchi M. Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. Med Biol Eng Comput 2018;56(1):125–36. link1

[82] Li J, Fu A, Zhang L. An overview of scoring functions used for protein-ligand interactions in molecular docking. Interdiscip Sci Comput Life Sci 2019;11 (2):320–8. link1

[83] Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 2018;136(7):803–10. link1

[84] Hinton G. Deep learning—a technology with the potential to transform health care. JAMA 2018;320(11):1101–2. link1

[85] Patlatzoglou K, Chennu S, Boly M, Noirhomme Q, Bonhomme V, Brichant JF, et al. Deep neural networks for automatic classification of anesthetic-induced unconsciousness. In: Wang S, Yamamoto V, Su J, Yang Y, Jones E, Iasemidis L, editors. BI 2018: brain informatics; 2018 Dec 7–9; Arlington, TX, USA. Cham: Springer; 2018. p. 216–25. link1

[86] Riva-Posse P, Choi KS, Holtzheimer PE, Crowell AL, Garlow SJ, Rajendra JK, et al. A connectomic approach for subcallosal cingulate deep brain stimulation surgery: prospective targeting in treatment-resistant depression. Mol Psychiatry 2018;23:843–9. link1

[87] Sánchez I, Soriano-Mas C, Verdejo-García A, Cardoner N, Fernández-Aranda F, Menchón JM, et al. Analysis of feature importance in deep neural networks in psychiatric disorders using magnetic resonance imaging [presentation]. The 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine; 2019 May 11–16; Montreal, QC, Canada, 2019. link1

[88] Khan A, Liu Q, Wang K. iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes. BMC Bioinform 2018;19:501. link1

[89] Zhang QS, Zhu SC. Visual interpretability for deep learning:a survey. Front Inform Technol Electron 2018;19(1):27–39. link1

[90] Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, Crawford GE, et al. The PsychENCODE project. Nat Neurosci 2015;18:1707–12. link1

[91] Alain G, Bengio Y. Understanding intermediate layers using linear classifier probes. 2018. arXiv: 1610.01644v4.

[92] Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H. Understanding neural networks through deep visualization. 2015. arXiv:1506.06579.

[93] Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M. Striving for simplicity: the all convolutional net. 2014. arXiv:1412.6806.

[94] Kindermans PJ, Schütt KT, Alber M, Müller KR, Erhan D, Kim B, et al. Learning how to explain neural networks: PatternNet and PatternAttribution. 2017. arXiv:1705.05598.

[95] Zhang Q, Yang Y, Ma H, Wu YN. Interpreting CNNs via decision trees. In: Proceedings of 2019 IEEE Conference on Computer Vision and Pattern Recognition; 2019 Jun 16–20; Long Beach. p. 6261–70. link1

[96] Zhuang YT, Wu F, Chen C, Pan YH. Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inform Technol Electron 2017;18(1):3–14. link1

[97] Price II WN, Cohen IG. Privacy in the age of medical big data. Nat Med 2019;25:37–43. link1

[98] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44–56. link1

[99] Zhang L, Liu Y, Wang M, Wu Z, Li N, Zhang J, et al. EZH2-, CHD4-, and IDHlinked epigenetic perturbation and its association with survival in glioma patients. J Mol Cell Biol 2017;9(6):477–88. link1

[100] Zhang L, Qiao M, Gao H, Hu B, Tan H, Zhou X, et al. Investigation of mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model and experimental optimization/validation. Nanoscale 2016;8(31):14877–87. link1

[101] Zhang L, Tao W, Feng H, Chen Y. Transcriptional and genomic targets of neural stem cells for functional recovery after hemorrhagic stroke. Stem Cells Int 2017;2017:2412890. link1

[102] Zhang L, Zhang S. Using, game theory to investigate the epigenetic control mechanisms of embryo development: comment on: ‘‘Epigenetic game theory: how to compute the epigenetic control of maternal-to-zygotic transition” by Qian Wang, et al.. Phys Life Rev 2017;20:140–2. link1

[103] Jeffries J. Book review: psychopharmacology: Stahl’s essential psychopharmacology: neuroscientific basic and practical applications. Third Edition. Can J Psychiatry 2011;56(5):312–3. link1

[104] Zhang L, Zheng C, Li T, Xing L, Zeng H, Li T, et al. Building up a robust risk mathematical platform to predict colorectal cancer. Complexity 2017;2017:8917258. link1

[105] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP. Automated EEGbased screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 2018;161:103–13. link1

[106] Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry 2019 [Epub 2019 Feb 15]. 6 G.-D. Liu et al. / Engineering xxx (xxxx) xxx Please cite this article as: G.-D. Liu, Y. C. Li, W. Zhang et al., A Brief Review of Artificial Intelligence Applications and Algorithms for Psychiatric D link1

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