Please wait a minute...
Advances in Psychological Science    2020, Vol. 28 Issue (2) : 252-265     DOI: 10.3724/SP.J.1042.2020.00252
Regular Articles |
The classification of schizophrenia based on brain structural features: A machine learning approach
ZHENG Hong1,2,PU Cheng-cheng3,WANG Yi1,2(),Raymond C. K. CHAN1,2
1 Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
2 Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
3 Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
Download: PDF(1434 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks     Supporting Info
Guide   
Abstract  

Machine learning is a promising approach for mental disorders. In recent years, machine learning based on T1 weighted imaging and Diffusion Tensor Imaging (DTI) data has been used to investigate the psychopathology and underlying mechanisms of schizophrenia patients and high-risk population. The findings from the previous literature suggest that structural features of frontal lobe and temporal lobe can improve classification performance. In addition, the combination of behavioural performances and the features of brain structure is superior to the single-modality structural images on classification accuracy. However, the existing empirical studies classifying schizophrenia patients or high-risk population from controls are limited in sample size and generalization ability.

Keywords structural Magnetic Resonance Imaging      Diffusion Tensor Imaging      machine learning      schizophrenia      high risk population     
ZTFLH:  R395  
Corresponding Authors: Yi WANG     E-mail: wangyi@psych.ac.cn
Issue Date: 25 December 2019
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Hong ZHENG
Cheng-cheng PU
Yi WANG
C. K. CHAN Raymond
Cite this article:   
Hong ZHENG,Cheng-cheng PU,Yi WANG, et al. The classification of schizophrenia based on brain structural features: A machine learning approach[J]. Advances in Psychological Science, 2020, 28(2): 252-265.
URL:  
http://journal.psych.ac.cn/xlkxjz/EN/10.3724/SP.J.1042.2020.00252     OR     http://journal.psych.ac.cn/xlkxjz/EN/Y2020/V28/I2/252
  
  
  
[1] 周志华 . (2016). 机器学习. 清华大学出版社.
[2] Ardekani, B. A., Tabesh, A., Sevy, S., Robinson, D. G., Bilder, R. M., & Szeszko, P. R . ( 2011). Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers. Human Brain Mapping, 32( 1), 1-9.
url: https://doi.org/10.1002/hbm.20995
[3] Bakhshi, K., & Chance, S. A .( 2015). The neuropathology of schizophrenia: A selective review of past studies and emerging themes in brain structure and cytoarchitecture. Neuroscience. 33, 82-102.
url: https://doi.org/10.1016/j.neuroscience. 2015.06.028
[4] Beleites, C., Neugebauer, U., Bocklitz, T., Krafft, C., & Popp, J . ( 2013). Sample size planning for classification models. Analytica Chimica Acta, 760, 25-33. doi: 10.1016/j.aca. 2012.11.007
doi: 10.1016/j.aca. 2012.11.007
[5] Bengio, Y., Courville, A., & Vincent, P .( 2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35(8), 1798-1828.
url: https://doi.org/10.1109/TPAMI.2013.50
[6] Borgwardt, S., Koutsouleris, N., Aston, J., Studerus, E., Smieskova, R., Riecher-Rössler, A., & Meisenzahl, E. M . ( 2013). Distinguishing prodromal from first-episode psychosis using neuroanatomical single-subject pattern recognition. Schizophrenia Bulletin, 39( 5), 1105-1114.
url: https://doi.org/ 10.1093/s chbul/sbs095
[7] Carpenter, W. T., & Buchanan, R. W . ( 1994). Schizophrenia. New England Journal of Medicine, 330( 10), 681-690.
url: https://doi.org/10.1056/NEJM199403103301006
[8] Chin, R., You, A. X. B., Meng, F. W., Zhou, J., & Sim, K . ( 2018). Recognition of schizophrenia with regularized support vector machine and sequential region of interest selection using structural magnetic resonance imaging. Scientific Reports, 8( 1), 13858.
url: https://doi.org/10.1038/s41598- 018-32290-9
[9] Chu, W.-L., Huang, M.-W., Jian, B.-L., Hsu, C.-Y., & Cheng, K.-S . ( 2016). A correlative classification study of schizophrenic patients with results of clinical evaluation and structural magnetic resonance images. Behavioural Neurology, 2016, 1-11.
url: https://doi.org/10.1155/2016/7849526
[10] de Moura, A. M., Pinaya, W. H. L., Gadelha, A., Zugman, A., Noto, C., Cordeiro, Q., … Sato, J. R .( 2018). Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach. Psychiatry Research - Neuroimaging, 275(March), 14-20.
url: https://doi.org/10.1016/j.pscychresns.2018.03.003
[11] Deng, Y., Hung, K. S. Y., Lui, S. S. Y., Chui, W. W. H., Lee, J. C. W., Wang, Y., … Cheung, E. F. C .( 2019). Tractography-based classification in distinguishing patients with first-episode schizophrenia from healthy individuals. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 88, 66-73.
url: https://doi.org/10.1016/j.pnpbp. 2018.06.010
[12] de Pierrefeu, A., Löfstedt, T., Laidi, C., Hadj-Selem, F., Bourgin, J., Hajek, T., … Duchesnay, E . ( 2018). Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity. Acta Psychiatrica Scandinavica. 138 (6), 571-580.
url: https://doi.org/10.1111/acps.12964
[13] Dluhoš, P., Schwarz, D., Cahn, W., van Haren, N., Kahn, R., Španiel, F., Schnack, H .( 2017). Multi-center machine learning in imaging psychiatry: A meta-model approach. NeuroImage, 155(April), 10-24.
url: https://doi.org/10.1016/ j.neuroimage.2017.03.027
[14] Dwyer, D. B., Falkai, P., & Koutsouleris, N . ( 2018). Machine learning approaches for clinical psychology and psychiatry. Annual Review of Clinical Psychology, 14(1), 91-118.
url: https://doi.org/10.1146/annurev-clinpsy-032816-045037
[15] Dyrba, M., Grothe, M., Kirste, T., & Teipel, S. J . ( 2015). Multimodal analysis of functional and structural disconnection in Alzheimer’s disease using multiple kernel SVM. Human Brain Mapping, 36(6), 2118-2131.
url: https://doi.org/10.1002/ hbm.22759
[16] Ebdrup, B. H., Axelsen, M. C., Bak, N., Fagerlund, B., Oranje, B., Raghava, J. M., … Glenthøj, B. Y . ( 2018). Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients. Psychological Medicine.
url: https://doi.org/10.1017/S0033291718003781
[17] Gould, I. C., Shepherd, A. M., Laurens, K. R., Cairns, M. J., Carr, V. J., & Green, M. J . ( 2014). Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach. NeuroImage: Clinical, 6, 229-236.
[18] Greenstein, D., Malley, J. D., Weisinger, B., Clasen, L., & Gogtay, N., & .( 2012). Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls. Frontiers in Psychiatry, 3(June), 1-12.
url: https://doi.org/10.3389/fpsyt.2012.00053
[19] Guo, S., Palaniyappan, L., Liddle, P. F., & Feng, J . ( 2016). Dynamic cerebral reorganization in the pathophysiology of schizophrenia: A MRI-derived cortical thickness study. Psychological Medicine, 46( 10), 2201-2214.
url: https://doi. org/10.1017/S0033291716000994
[20] Guyon, I., Weston, J., Barnhill, S., & Vapnik, V . ( 2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46( 1-3), 389-422.
url: https://doi. org/10.1023/A:1012487302797
[21] Iwabuchi, S. J., Liddle, P. F., & Palaniyappan, L .( 2013). Clinical utility of machine-learning approaches in schizophrenia: Improving diagnostic confidence for translational neuroimaging. Frontiers in Psychiatry, 4(August), 1-9.
url: https://doi.org/10.3389/fpsyt.2013.00095
[22] Kambeitz, J., Kambeitz-Ilankovic, L., Leucht, S., Wood, S., Davatzikos, C., Malchow, B., … Koutsouleris, N .( 2015). Detecting neuroimaging biomarkers for schizophrenia: A meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology, 40 (7), 1742-1751.
url: https://doi. org/10.1038/npp.2015.22
[23] Karageorgiou, E., Schulz, S. C., Gollub, R. L., Andreasen, N. C., Ho, B. C., Lauriello, J., ... & Georgopoulos, A. P . ( 2011). Neuropsychological testing and structural magnetic resonance imaging as diagnostic biomarkers early in the course of schizophrenia and related psychoses. Neuroinformatics, 9( 4), 321-333.
[24] Lecun, Y., Bengio, Y., & Hinton, G . ( 2015). Deep learning. Nature, 521, 436-444.
url: https://doi.org/10.1038/nature14539
[25] Lu, X. B., Yang, Y. Z., Wu, F. C., Gao, M. J., Xu, Y., Zhang, Y., … Wu, K . ( 2016). Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images. Medicine (United States), 95( 30), e3973.
url: https://doi.org/10.1097/ MD.0000000000003973
[26] Lu, X.-B., Zhang, Y., Yang, D.-Y., Yang, Y.-Z., Wu, F.-C., Ning, Y.-P., & Wu, K . ( 2018). Analysis of first-episode and chronic schizophrenia using multi-modal magnetic resonance imaging. European Review for Medical and Pharmacological Sciences, 22( 19), 6422-6435.
url: https://doi. org/10.26355/eurrev_201810_16055
[27] Madsen, K. H., Krohne, L. G., Cai, X.-L., Wang, Y., & Chan, R. C. K . ( 2018). Perspectives on machine learning for classification of Schizotypy using fMRI data. Schizophrenia bulletin, 44( suppl_2), S480-S490.
url: https://doi.org/10.1093/ schbul/sby026
[28] Meoded, A., Poretti, A., Mori, S., & Zhang, J . ( 2017). Diffusion Tensor Imaging (DTI). Reference Module in Neuroscience and Biobehavioral Psychology, (March 2016), 1-11.
url: https://doi.org/10.1016/B978-0-12-809324-5.02472-X
[29] Mikolas, P., Hlinka, J., Skoch, A., Pitra, Z., Frodl, T., Spaniel, F., & Hajek, T . ( 2018). Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry, 18( 1), 97.
url: https:// doi.org/10.1186/s12888-018-1678-y
[30] Mitchell, T . ( 1997). Machine Learning. McGraw-Hill,New York.
[31] Nieuwenhuis, M., van Haren, N. E. M., Hulshoff Pol, H. E., Cahn, W., Kahn, R. S., & Schnack, H. G . ( 2012). Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples. NeuroImage, 61(3), 606-612.
url: https://doi.org/10. 1016/j.neuroimage.2012.03.079
[32] Palaniyappan, L . ( 2017). Progressive cortical reorganisation: A framework for investigating structural changes in schizophrenia. Neuroscience and Biobehavioral Reviews, 79, 1-3.
url: https://doi.org/10.1016/j.neubiorev.2017.04.028
[33] Peng, B., Lu, J., Saxena, A., Zhou, Z. Y., Zhang, T., Wang, S. H., & Dai, Y. K .( 2017). Examining brain morphometry associated with self-esteem in young adults using multilevel-ROI-features-based classification method. Frontiers in Computational Neuroscience, 11(May), 1-10.
url: https://doi.org/10.3389/fncom.2017.00037
[34] Peruzzo, D., Castellani, U., Perlini, C., Bellani, M., Marinelli, V., Rambaldelli, G., … Brambilla, P . ( 2015). Classification of first-episode psychosis: A multi-modal multi-feature approach integrating structural and diffusion imaging. Journal of Neural Transmission, 122(6), 897-905.
url: https://doi.org/10.1007/s00702-014-1324-x
[35] Pettersson-Yeo, W., Benetti, S., Marquand, A. F., Dell’Acqua, F., Williams, S. C. R., Allen, P., … Mechelli, A . ( 2013). Using genetic, cognitive and multi-modal neuroimaging data to identify ultra-high-risk and first-episode psychosis at the individual level. Psychological Medicine, 43(12), 2547-2562.
url: https://doi.org/10.1017/S003329171300024X
[36] Pinaya, W. H. L., Gadelha, A., Doyle, O. M., Noto, C., Zugman, A., Cordeiro, Q., … Sato, J. R . ( 2016). Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Scientific Reports, 6( 1), 38897.
url: https://doi.org/10.1038/srep38897
[37] Qureshi, M. N. I., Oh, J., Cho, D., Jo, H. J., & Lee, B . ( 2017). Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Frontiers in Neuroinformatics, 11(September), 1-14.
url: https://doi.org/10.3389/fninf.2017.00059
[38] Rathi, Y., Malcolm, J., Michailovich, O., Goldstein, J., Seidman, L., McCarley, R. W., & Westin, C.-F . ( 2010). Biomarkers for identifying first-episode schizophrenia patients using diffusion weighted imaging. Med Image Comput Comput-Assist Interv, 13, 657-665.
[39] Riley, P . ( 2019). Three pitfalls to avoid in machine learning. Nature, 572( 7767), 27-29.
[40] Salvador, R., Radua, J., Canales-Rodríguez, E. J., Solanes, A., Sarró, S., Goikolea, J. M., … Pomarol-Clote, E . ( 2017). Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis. PLoS ONE, 12( 4), e0175683.
url: https://doi.org/ 10.1371/journal.pone.0175683
[41] Samuel, A. L . ( 1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3( 3), 210-229.
url: https://doi.org/10.1147/rd.33.0210
[42] Schmidhuber, J . ( 2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
url: https://doi. org/10.1016/j.neunet.2014.09.003
[43] Schnack, H. G., Nieuwenhuis, M., van Haren, N. E. M., Abramovic, L., Scheewe, T. W., Brouwer, R. M., … Kahn, R. S . ( 2014). Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. NeuroImage, 84, 299-306.
[44] Squarcina, L., Castellani, U., Bellani, M., Perlini, C., Lasalvia, A., Dusi, N., … Brambilla, P .( 2017). Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques. NeuroImage, 145(Pt B), 238-245.
url: https://doi. org/10.1016/j.neuroimage.2015.12.007
[45] Sui, J., Castro, E., He, H., Bridwell, D., Du, Y. H., Pearlson, G. D., … Calhoun, V. D .( 2014). Combination of fMRI- sMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection. Conference Proceedings?: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014(2), 3889-3892.
url: https://doi.org/10.1109/ EMBC.2014.6944473
[46] Sui, J., He, H., Yu, Q. B., Chen, J. Y., Rogers, J., Pearlson, G. D., … Calhoun, V. D .( 2013). Combination of resting state fMRI, DTI, and sMRI data to discriminate schizophrenia by N-way MCCA + jICA. Frontiers in Human Neuroscience, 7(May), 1-14.
url: https://doi.org/10.3389/ fnhum.2013.00235
[47] Takayanagi, Y., Takahashi, T., Orikabe, L., Mozue, Y., Kawasaki, Y., Nakamura, K., .. Suzuki, M . ( 2011). Classification of first-episode schizophrenia patients and healthy subjects by automated MRI measures of regional brain volume and cortical thickness. PloS ONE, 6( 6), e21047.
[48] Tas, C., Mogulkoc, H., Eryilmaz, G., Gogcegoz-Gul, I., Erguzel, T. T., Metin, B., & Tarhan, N. K . ( 2018). Discriminating schizophrenia and schizo-obsessive disorder: A structural MRI study combining VBM and machine learning methods. Neural Computing and Applications, 29( 2), 377- 387.
url: https://doi.org/10.1007/s00521-016-2451-0
[49] Valli, I., Marquand, A. F., Mechelli, A., Raffin, M., Allen, P., Seal, M. L., & McGuire, P . ( 2016). Identifying individuals at high risk of psychosis: Predictive utility of support vector machine using structural and functional MRI data. Frontiers in Psychiatry, 7, 52.
url: https://doi.org/10.3389/ fpsyt.2016.00052
[50] Wang, L. B., Shen, H., Li, B. J., & Hu, D. W . ( 2011). Classification of schizophrenic patients and healthy controls using multiple spatially independent components of structural MRI data. Frontiers of Electrical and Electronic Engineering in China, 6( 2), 353-362.
url: https://doi.org/ 10.1007/s11460-011-0142-2
[51] Winterburn, J. L., Voineskos, A. N., Devenyi, G. A., Plitman, E., de la Fuente-Sandoval, C., Bhagwat, N., … Chakravarty, M. M .( 2017). Can we accurately classify schizophrenia patients from healthy controls using magnetic resonance imaging and machine learning? A multi-method and multi-dataset study. Schizophrenia Research.
url: https://doi. org/10.1016/j.schres.2017.11.038
[52] Xiao, Y., Yan, Z. H., Zhao, Y. J., Tao, B., Sun, H. Q., Li, F., … Lui, S .( 2017). Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI. Schizophrenia Research.
url: https://doi.org/10.1016/j.schres.2017.11.037
[53] Zarogianni, E., Storkey, A. J., Johnstone, E. C., Owens, D. G. C., & CLawrie, S. M .( 2017). Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features. Schizophrenia Research, 181, 6-12.
url: https://doi.org/10.1016/j.schres.2016.08.027
[54] Zanetti, M. V., Schaufelberger, M. S., Doshi, J., Ou, Y. M., Ferreira, L. K., Menezes, P. R., ... Busatto, G. F . ( 2013). Neuroanatomical pattern classification in a population- based sample of first-episode schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 43, 116-125.
[1] DONG Jianyu,WEI Wenqi,WU Ke,NI Na,WANG Canfei,FU Ying,PENG Xin. The application of machine learning in depression[J]. Advances in Psychological Science, 2020, 28(2): 266-274.
[2] OU Jianxin,WU Yin,LIU Jinting,LI Hong. Computational psychiatry: A new perspective on research and clinical applications in depression[J]. Advances in Psychological Science, 2020, 28(1): 111-127.
[3] CAO Yi,YANG Xiaohu. Speech perception in schizophrenia[J]. Advances in Psychological Science, 2019, 27(6): 1025-1035.
[4] Xiaofei DENG,Jianyou GUO. Roles of impaired parvalbumin positive interneurons in schizophrenic pathology[J]. Advances in Psychological Science, 2018, 26(11): 1992-2002.
[5] ZHU Chuan-Lin; Li Ping; Luo Wen-Bo; Qi Zheng-Yang; He Wei-Qi. Emotion regulation in schizophrenia[J]. Advances in Psychological Science, 2016, 24(4): 556-572.
[6] GUO Yafei; JIN Shenghua; WANG Jianping; WU Linhua; AIDI Ma. Changes of Schizophrenia Spectrum Disorder in DSM-5: #br# Dispute between Categorical and Dimensional Approaches[J]. Advances in Psychological Science, 2015, 23(8): 1428-1436.
[7] XUE Xiaofang;LI Man;WANG Weiwen;SHAO Feng. The Animal Model and Neurobiological Mechanisms of Maternal Separation[J]. Advances in Psychological Science, 2013, 21(6): 990-998.
[8] WU Chao;WU Xihong;LI Liang. Speech Recognition in Schizophrenic under Masking Conditions[J]. Advances in Psychological Science, 2013, 21(6): 958-964.
[9] DU Yi;LI Liang. Animal Model and Neural Mechanisms of Top-Down Modulation of Auditory Sensorimotor Gating[J]. , 2011, 19(7): 944-958.
[10] WANG Yu-Na;CHAN Raymond. Self Impairment and Schizophrenia[J]. , 2010, 18(12): 1882-1891.
[11] SHI Yan-Fang;CHAN Raymond C. K.. Anhedonia in Schizophrenia Spectrum Disorders[J]. , 2010, 18(09): 1430-1439.
[12] SHAO Feng;WANG Wei-Wen;LIU Mei;JIN Jian. Latent Inhibition as an Animal Model of Schizophrenia[J]. , 2008, 16(03): 392-398.
[13] LI Liang;LI Nan-Xin. Establishing New Animal Models for Studying Schizophrenia[J]. , 2008, 16(03): 399-403.
[14] Mo Shuliang;Chen Chuqiao. Perception and Recognition of Facial Expressions of Emotion in Schizophrenia[J]. , 2008, 16(02): 266-273.
[15] Zhu Chunyan,Wang Kai,Lee TMC. Executive Function and Schizophrenia[J]. , 2004, 12(05): 743-751.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Copyright © Advances in Psychological Science
Support by Beijing Magtech