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REVIEW

Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review

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Pages 1605-1627 | Published online: 19 Jun 2019

References

  • Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet. 2016;388(10039):86–97. doi:10.1016/S0140-6736(15)01121-6
  • McGrath J, Saha S, Chant D, Welham J. Schizophrenia: a concise overview of incidence, prevalence, and mortality. Epidemiol Rev. 2008;30(1):67–76. doi:10.1093/epirev/mxn001
  • Simeone JC, Ward AJ, Rotella P, Collins J, Windisch R. An evaluation of variation in published estimates of schizophrenia prevalence from 1990─2013: a systematic literature review. BMC Psychiatry. 2015;15(1):193. doi:10.1186/s12888-015-0578-7
  • American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington (VA): American Psychiatric Association; 2013.
  • The ICD-10 classification of mental and behavioural disorders clinical descriptions and diagnostic guidelines. Available from: http://www.who.int/classifications/icd/en/bluebook.pdf. Accessed February 19, 2018.
  • Kambeitz J, Kambeitz-Ilankovic L, Leucht S, et al. Detecting neuroimaging biomarkers for schizophrenia: a meta-analysis of multivariate pattern recognition studies. Neuropsychopharmacology. 2015;40(7):1742–1751. doi:10.1038/npp.2015.22
  • 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;57:328–349. doi:10.1016/j.neubiorev.2015.08.001
  • Veronese E, Castellani U, Peruzzo D, Bellani M, Brambilla P. Machine learning approaches: from theory to application in schizophrenia. Comput Math Methods Med. 2013;2013:1–12. doi:10.1155/2013/867924
  • Vapnik VN. An overview of statistical learning theory. IEEE Trans Neural Networks. 1999;10(5):988–999. doi:10.1109/72.788640
  • Krystal JH, Murray JD, Chekroud AM, et al. Computational psychiatry and the challenge of schizophrenia. Schizophr Bull. 2017;43(3):473–475. doi:10.1093/schbul/sbx025
  • Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. 2009;21;6(7):e1000100. doi:10.1371/journal.pmed.1000100
  • Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary? Control Clin Trials. 1996;17(1):1–12. Available from: http://www.ncbi.nlm.nih.gov/pubmed/8721797. Accessed November 22, 2017.
  • Salvador R, Radua J, Canales-Rodríguez EJ, et al. Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis. Hu D, ed. PLoS One. 2017;12(4):e0175683. doi:10.1371/journal.pone.0175683
  • Lu X, Yang Y, Wu F, et al. Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images. Medicine (Baltimore). 2016;95(30):e3973. doi:10.1097/MD.0000000000003973
  • Castellani U, Rossato E, Murino V, et al. Classification of schizophrenia using feature-based morphometry. J Neural Transm. 2012;119(3):395–404. doi:10.1007/s00702-011-0693-7
  • Xiao Y, Yan Z, Zhao Y, et al. Support vector machine-based classification of first episode drug-naïve schizophrenia patients and healthy controls using structural MRI. Schizophr Res. 2017. doi:10.1016/j.schres.2017.11.037
  • Greenstein D, Malley JD, Weisinger B, Clasen L, Gogtay N. Using multivariate machine learning methods and structural MRI to classify childhood onset schizophrenia and healthy controls. Front Psychiatry. 2012;3. doi:10.3389/fpsyt.2012.00053
  • Pinaya WHL, Gadelha A, Doyle OM, et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci Rep. 2016;6(1):38897. doi:10.1038/srep38897
  • Pinaya WHL, Mechelli A, Sato JR. Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: a large-scale multi-sample study. Hum Brain Mapp. 2019;40(3):944–954. doi:10.1002/hbm.24423
  • Iwabuchi SJ, Liddle PF, Palaniyappan L. Clinical utility of machine-learning approaches in schizophrenia: improving diagnostic confidence for translational neuroimaging. Front Psychiatry. 2013;4:95. doi:10.3389/fpsyt.2013.00095
  • Cabral C, Kambeitz-Ilankovic L, Kambeitz J, et al. Classifying schizophrenia using multimodal multivariate pattern recognition analysis: evaluating the impact of individual clinical profiles on the neurodiagnostic performance. Schizophr Bull. 2016;42(suppl 1):S110–S117. doi:10.1093/schbul/sbw053
  • Chen X, Liu C, He H, et al. Transdiagnostic differences in the resting-state functional connectivity of the prefrontal cortex in depression and schizophrenia. J Affect Disord. 2017;217:118–124. doi:10.1016/j.jad.2017.04.001
  • Koch SP, Hägele C, Haynes J-D, Heinz A, Schlagenhauf F, Sterzer P. Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns. Schwarz AJ, ed. PLoS One. 2015;10(3):e0119089. doi:10.1371/journal.pone.0119089
  • Yoon JH, Tamir D, Minzenberg MJ, Ragland JD, Ursu S, Carter CS. Multivariate pattern analysis of functional magnetic resonance imaging data reveals deficits in distributed representations in schizophrenia. Biol Psychiatry. 2008;64(12):1035–1041. doi:10.1016/j.biopsych.2008.07.025
  • Reavis EA, Lee J, Wynn JK, et al. Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data. Neuro Image Clin. 2017;16:491–497. doi:10.1016/j.nicl.2017.08.023
  • Wang S, Zhan Y, Zhang Y, et al. Abnormal long- and short-range functional connectivity in adolescent-onset schizophrenia patients: a resting-state fMRI study. Prog Neuropsychopharmacol Biol Psychiatry. 2017;81:445–451. doi:10.1016/j.pnpbp.2017.08.012
  • Guo W, Liu F, Chen J, et al. Using short-range and long-range functional connectivity to identify schizophrenia with a family-based case-control design. Psychiatry Res. 2017;264:60–67. doi:10.1016/j.pscychresns.2017.04.010
  • Wang S, Zhang Y, Lv L, et al. Abnormal regional homogeneity as a potential imaging biomarker for adolescent-onset schizophrenia: a resting-state fMRI study and support vector machine analysis. Schizophr Res. 2017. doi:10.1016/j.schres.2017.05.038
  • Liu Y, Zhang Y, Lv L, Wu R, Zhao J, Guo W. Abnormal neural activity as a potential biomarker for drug-naive first-episode adolescent-onset schizophrenia with coherence regional homogeneity and support vector machine analyses. Schizophr Res. 2017. doi:10.1016/j.schres.2017.04.028
  • Chyzhyk D, Savio A, Graña M. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Netw. 2015;68:23–33. doi:10.1016/j.neunet.2015.04.002
  • Zhu M, Jie N, Jiang T. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm. In: aylward S, Hadjiiski LM, eds. Int Soc Opt Photonics. 2014;9035:903522. doi:10.1117/12.2043240
  • Cao H, Duan J, Lin D, Shugart YY, Calhoun V, Wang Y-P. Sparse representation based biomarker selection for schizophrenia with integrated analysis of fMRI and SNPs. Neuroimage. 2014;102(Pt 1):220–228. doi:10.1016/j.neuroimage.2014.01.021
  • Yang H, Liu J, Sui J, Pearlson G, Calhoun VD. A hybrid machine learning method for fusing fMRI and genetic data: combining both improves classification of schizophrenia. Front Hum Neurosci. 2010;4:192. doi:10.3389/fnhum.2010.00192
  • Arbabshirani MR, Castro E, Calhoun VD. Accurate classification of schizophrenia patients based on novel resting-state fMRI features. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc Annu Conf. 2014;2014:6691–6694. doi:10.1109/EMBC.2014.6945163
  • Chen H, Uddin LQ, Duan X, et al. Shared atypical default mode and salience network functional connectivity between autism and schizophrenia. Autism Res. 2017;10(11):1776–1786. doi:10.1002/aur.1834
  • Matsubara T, Tashiro T, Uehara K. Deep neural generative model of functional MRI images for psychiatric disorder diagnosis. IEEE Trans Biomed Eng. 2015; 1-1. doi:10.1109/TBME.2019.2895663
  • Kim J, Calhoun VD, Shim E, Lee J-H. 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(Pt A):127–146. doi:10.1016/j.neuroimage.2015.05.018
  • Watanabe T, Kessler D, Scott C, Angstadt M, Sripada C. Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. Neuroimage. 2014;96:183–202. doi:10.1016/j.neuroimage.2014.03.067
  • Su L, Wang L, Shen H, Feng G, Hu D. Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI study. Front Hum Neurosci. 2013;7:702. doi:10.3389/fnhum.2013.00702
  • Bae Y, Kumarasamy K, Ali IM, Korfiatis P, Akkus Z, Erickson BJ. Differences between schizophrenic and normal subjects using network properties from fMRI. J Digit Imaging. 2017. doi:10.1007/s10278-017-0020-4
  • Pläschke RN, Cieslik EC, Müller VI, et al. On the integrity of functional brain networks in schizophrenia, Parkinson’s disease, and advanced age: evidence from connectivity-based single-subject classification. Hum Brain Mapp. 2017;38(12):5845–5858. doi:10.1002/hbm.23763
  • Liu Y, Guo W, Zhang Y, et al. Decreased resting-state interhemispheric functional connectivity correlated with neurocognitive deficits in drug-naive first-episode adolescent-onset schizophrenia. Int J Neuropsychopharmacol. 2018;21(1):33–41. doi:10.1093/ijnp/pyx095
  • Castro E, Gómez-Verdejo V, Martínez-Ramón M, Kiehl KA, Calhoun VD. A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia. Neuroimage. 2014;87:1–17. doi:10.1016/j.neuroimage.2013.10.065
  • Qureshi MNI, Oh J, Cho D, Jo HJ, Lee B. Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Front Neuroinform. 2017;11:59. doi:10.3389/fninf.2017.00059
  • Orban P, Dansereau C, Desbois L, et al. Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity. Schizophr Res. 2017. doi:10.1016/j.schres.2017.05.027
  • Zeng -L-L, Wang H, Hu P, et al. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBio Med. 2018;30:74–85. doi:10.1016/j.ebiom.2018.03.017
  • Amin MF, Plis SM, Chekroud A, et al. Reading the (functional) writing on the (structural) wall: multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia. Neuroimage. 2018;181:734–747. doi:10.1016/j.neuroimage.2018.07.047
  • Plis SM, Hjelm DR, Salakhutdinov R, et al. Deep learning for neuroimaging: a validation study. Front Neurosci. 2014;8. doi:10.3389/fnins.2014.00229