Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1386–1396 | Cite as

Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method

  • Junhua LiEmail author
  • Yu SunEmail author
  • Yi Huang
  • Anastasios Bezerianos
  • Rongjun YuEmail author
Original Research


Machine learning technique has long been utilized to assist disease diagnosis, increasing clinical physicians’ confidence in their decision and expediting the process of diagnosis. In this case, machine learning technique serves as a tool for distinguishing patients from healthy people. Additionally, it can also serve as an exploratory method to reveal intrinsic characteristics of a disease based on discriminative features, which was demonstrated in this study. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 148 participants (including patients with schizophrenia and healthy controls). Connective strengths were estimated by Pearson correlation for each pair of brain regions partitioned according to automated anatomical labelling atlas. Subsequently, consensus connections with high discriminative power were extracted under the circumstance of the best classification accuracy. Investigating these consensus connections, we found that schizophrenia group predominately exhibited weaker strengths of inter-regional connectivity compared to healthy group. Aberrant connectivities in both intra- and inter-hemispherical connections were observed. Within intra-hemispherical connections, the number of aberrant connections in the right hemisphere was more than that of the left hemisphere. In the exploration of large regions, we revealed that the serious dysconnectivities mainly appeared on temporal and occipital regions for the within-large-region connections; while connectivity disruption was observed on the connections from temporal region to occipital, insula and limbic regions for the between-large-region connections. The findings of this study corroborate previous conclusion of dysconnectivity in schizophrenia and further shed light on distribution patterns of dysconnectivity, which deepens the understanding of pathological mechanism of schizophrenia.


Resting-state fMRI Functional connectivity Schizophrenia Hemispherical distribution of connections Large-region connectivity 


Compliance with ethical standards

Conflict of interest

RY has received MOE Tier 2 grant (MOE2016-T2–1-015) from the Ministry of Education, Singapore. RY declares that the funder had no role in study design, implementation and data analysis, decision to publish, or preparation for the manuscript, and he has no conflict of interest. The data used in this study are publicly available. The owner of the data declares that all procedures performed in experiments involving human participants were in accordance with the ethical standards of the institutional review board of the University of New Mexico and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All participants provided their informed consent forms.

Supplementary material

11682_2018_9947_MOESM1_ESM.docx (81.1 mb)
ESM 1 (DOCX 83007 kb)


  1. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, D.C.: APA.Google Scholar
  2. Anderson, A., & Cohen, M. S. (2013). Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: An fMRI classification tutorial. Frontiers in Human Neuroscience, 7, 520.Google Scholar
  3. Andreasen, N. C., & Pierson, R. (2008). The role of the cerebellum in schizophrenia. Biological Psychiatry, 64, 81–88.CrossRefGoogle Scholar
  4. Andreasen, N. C., Nopoulos, P., Magnotta, V., Pierson, R., Ziebell, S., & Ho, B. C. (2011). Progressive brain change in schizophrenia: A prospective longitudinal study of first-episode schizophrenia. Biological Psychiatry, 70, 672–679.CrossRefGoogle Scholar
  5. Anticevic, A., Repovs, G., Krystal, J. H., & Barch, D. M. (2012). A broken filter: Prefrontal functional connectivity abnormalities in schizophrenia during working memory interference. Schizophrenia Research, 141, 8–14.CrossRefGoogle Scholar
  6. Arbabshirani, M. R., Kiehl, K. A., Pearlson, G. D., & Calhoun, V. D. (2013). Classification of schizophrenia patients based on resting-state functional network connectivity. Frontiers in Neuroscience, 7, 1–16.CrossRefGoogle Scholar
  7. Bleich-Cohen, M., Sharon, H., Weizman, R., Poyurovsky, M., Faragian, S., & Hendler, T. (2012). Diminished language lateralization in schizophrenia corresponds to impaired inter-hemispheric functional connectivity. Schizophrenia Research, 134, 131–136.CrossRefGoogle Scholar
  8. Camchong, J., MacDonald, A. W., Bell, C., Mueller, B. A., & Lim, K. O. (2011). Altered functional and anatomical connectivity in schizophrenia. Schizophrenia Bulletin, 37, 640–650.CrossRefGoogle Scholar
  9. Chang, X., Xi, Y.-B., Cui, L.-B., Wang, H.-N., Sun, J.-B., Zhu, Y.-Q., Huang, P., Collin, G., Liu, K., Xi, M., Qi, S., Tan, Q.-R., Miao, D.-M., & Yin, H. (2015). Distinct inter-hemispheric dysconnectivity in schizophrenia patients with and without auditory verbal hallucinations. Scientific Reports, 5, 11218.CrossRefGoogle Scholar
  10. Chen, Y. L., Tu, P. C., Lee, Y. C., Chen, Y. S., Li, C. T., & Su, T. P. (2013). Resting-state fMRI mapping of cerebellar functional dysconnections involving multiple large-scale networks in patients with schizophrenia. Schizophrenia Research, 149, 26–34.CrossRefGoogle Scholar
  11. Cheng, H., Newman, S., Goñi, J., Kent, J. S., Howell, J., Bolbecker, A., Puce, A., O’Donnell, B. F., & Hetrick, W. P. (2015). Nodal centrality of functional network in the differentiation of schizophrenia. Schizophrenia Research, 168, 345–352.CrossRefGoogle Scholar
  12. Collinson, S. L., Mackay, C. E., OJ, James, A. C. D., & Crow, T. J. (2009). Dichotic listening impairments in early onset schizophrenia are associated with reduced left temporal lobe volume. Schizophrenia Research, 112, 24–31.CrossRefGoogle Scholar
  13. Collinson, S. L., Gan, S. C., Woon, P. S., Kuswanto, C., Sum, M. Y., Yang, G. L., Lui, J. M., Sitoh, Y. Y., Nowinski, W. L., & Sim, K. (2014). Corpus callosum morphology in first-episode and chronic schizophrenia: Combined magnetic resonance and diffusion tensor imaging study of Chinese Singaporean patients. The British Journal of Psychiatry, 204, 55–60.CrossRefGoogle Scholar
  14. Davatzikos, C., Shen, D., Gur, R. C., Wu, X., Liu, D., Fan, Y., Hughett, P., Turetsky, B. I., & Gur, R. E. (2005). Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Archives of General Psychiatry, 62, 1218–1227.CrossRefGoogle Scholar
  15. Du, W., Calhoun, V. D., Li, H., Ma, S., Eichele, T., Kiehl, K. A., Pearlson, G. D., & Adali, T. (2012). High classification accuracy for schizophrenia with Rest and task fMRI data. Frontiers in Human Neuroscience, 6, 1–12. Scholar
  16. Fan, Y., Liu, Y., Wu, H., Hao, Y., Liu, H., Liu, Z., & Jiang, T. (2011). Discriminant analysis of functional connectivity patterns on Grassmann manifold. Neuroimage, 56, 2058–2067.CrossRefGoogle Scholar
  17. Fitzsimmons, J., Kubicki, M., & Shenton, M. E. (2013). Review of functional and anatomical brain connectivity findings in schizophrenia. Current Opinion in Psychiatry, 26, 172–187.CrossRefGoogle Scholar
  18. Ford, J. M., Roach, B. J., Jorgensen, K. W., Turner, J. A., Brown, G. G., Notestine, R., Bischoff-Grethe, A., Greve, D., Wible, C., Lauriello, J., Belger, A., Mueller, B. A., Calhoun, V., Preda, A., Keator, D., O’Leary, D. S., Lim, K. O., Glover, G., Potkin, S. G., & Mathalon, D. H. (2009). Tuning in to the voices: A multisite fMRI study of auditory hallucinations. Schizophrenia Bulletin, 35, 58–66.CrossRefGoogle Scholar
  19. Fornito, A., & Bullmore, E. T. (2015). Reconciling abnormalities of brain network structure and function in schizophrenia. Current Opinion in Neurobiology, 30, 44–50.CrossRefGoogle Scholar
  20. Friston, K., Brown, H. R., Siemerkus, J., & Stephan, K. E. (2016). The dysconnection hypothesis (2016). Schizophrenia Research, 176, 83–94.CrossRefGoogle Scholar
  21. Guo, W., Xiao, C., Liu, G., Wooderson, S. C., Zhang, Z., Zhang, J., Yu, L., & Liu, J. (2014). Decreased resting-state interhemispheric coordination in first-episode, drug-naive paranoid schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 48, 14–19.CrossRefGoogle Scholar
  22. Gur, R. E., Turetsky, B. I., Cowell, P. E., Finkelman, C., Maany, V., Grossman, R. I., Arnold, S. E., Bilker, W. B., & Gur, R. C. (2000). Temporolimbic volume reductions in schizophrenia. Archives of General Psychiatry, 57, 769–775.CrossRefGoogle Scholar
  23. Hoptman, M. J., Zuo, X. N., D’Angelo, D., Mauro, C. J., Butler, P. D., Milham, M. P., & Javitt, D. C. (2012). Decreased interhemispheric coordination in schizophrenia: A resting state fMRI study. Schizophrenia Research, 141, 1–7. Scholar
  24. Kim, J., Calhoun, V. D., Shim, E., & Lee, J. H. (2016). 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, 124, 127–146.CrossRefGoogle Scholar
  25. Lawrie, S. M., Buechel, C., Whalley, H. C., Frith, C. D., Friston, K. J., & Johnstone, E. C. (2002). Reduced frontotemporal functional connectivity in schizophrenia associated with auditory hallucinations. Biological Psychiatry, 51, 1008–1011.CrossRefGoogle Scholar
  26. Li, J., Wang, Y., Zhang, L., Cichocki, A., & Jung, T.-P. (2016). Decoding EEG in cognitive tasks with time-frequency and connectivity masks. IEEE Transactions on Cognitive and Developmental Systems, 8, 298–308. Scholar
  27. Liang, M., Zhou, Y., Jiang, T., Liu, Z., Tian, L., Liu, H., & Hao, Y. (2006). Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. Neuroreport, 17, 209–213.CrossRefGoogle Scholar
  28. Marín, O. (2012). Interneuron dysfunction in psychiatric disorders. Nature Reviews. Neuroscience, 13, 107–120.CrossRefGoogle Scholar
  29. Mayer, A. R., Ruhl, D., Merideth, F., Ling, J., Hanlon, F. M., Bustillo, J., & Cañive, J. (2013). Functional imaging of the hemodynamic sensory gating response in schizophrenia. Human Brain Mapping, 34, 2302–2312. Scholar
  30. Öngür, D., Lundy, M., Greenhouse, I., Shinn, A. K., Menon, V., Cohen, B. M., & Renshaw, P. F. (2010). Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Research: Neuroimaging, 183, 59–68.CrossRefGoogle Scholar
  31. Os, J. V., & Kapur, S. (2009). Schizophrenia. Lancet, 374, 635–645.CrossRefGoogle Scholar
  32. Pettersson-Yeo, W., Allen, P., Benetti, S., McGuire, P., & Mechelli, A. (2011). Dysconnectivity in schizophrenia: Where are we now? Neuroscience and Biobehavioral Reviews, 35, 1110–1124.CrossRefGoogle Scholar
  33. Rehme, A. K., Volz, L.J., Feis, D.-L., Bomilcar-Focke, I., Liebig, T., Eickhoff, S.B., Fink, G.R., Grefkes, C., (2014). Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cerebral Cortex, 25, 3046–3056.Google Scholar
  34. Rolland, B., Amad, A., Poulet, E., Bordet, R., Vignaud, A., Bation, R., Delmaire, C., Thomas, P., Cottencin, O., & Jardri, R. (2015). Resting-state functional connectivity of the nucleus accumbens in auditory and visual hallucinations in schizophrenia. Schizophrenia Bulletin, 41, 291–299.CrossRefGoogle Scholar
  35. Rubinov, M., Knock, S. A., Stam, C. J., Micheloyannis, S., Harris, A. W. F., Williams, L. M., & Breakspear, M. (2009). Small-world properties of nonlinear brain activity in schizophrenia. Human Brain Mapping, 30, 403–416.CrossRefGoogle Scholar
  36. Segal, D., Mehmet Haznedar, M., Hazlett, E. A., Entis, J. J., Newmark, R. E., Torosjan, Y., Schneiderman, J. S., Friedman, J., Chu, K. W., Tang, C. Y., Buchsbaum, M. S., & Hof, P. R. (2010). Diffusion tensor anisotropy in the cingulate gyrus in schizophrenia. Neuroimage, 50, 357–365.CrossRefGoogle Scholar
  37. Shen, H., Wang, L., Liu, Y., & Hu, D. (2010). Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage, 49, 3110–3121.CrossRefGoogle Scholar
  38. Song, X. W., Dong, Z. Y., Long, X. Y., Li, S. F., Zuo, X. N., Zhu, C. Z., He, Y., Yan, C. G., & Zang, Y. F. (2011). REST: A toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One, 6.
  39. Stephan, K. E., Friston, K. J., & Frith, C. D. (2009). Dysconnection in schizophrenia: From abnormal synaptic plasticity to failures of self-monitoring. Schizophrenia Bulletin, 35, 509–527.CrossRefGoogle Scholar
  40. Sugranyes, G., Kyriakopoulos, M., Dima, D., O’Muircheartaigh, J., Corrigall, R., Pendelbury, G., Hayes, D., Calhoun, V. D., & Frangou, S. (2012). Multimodal analyses identify linked functional and white matter abnormalities within the working memory network in schizophrenia. Schizophrenia Research, 138, 136–142.CrossRefGoogle Scholar
  41. Sun, Y., Chen, Y., Collinson, S.L., Bezerianos, A., Sim, K., (2015). Reduced hemispheric asymmetry of brain anatomical networks is linked to schizophrenia: A Connectome study. Cerebral Cortex, 27, 602–615.Google Scholar
  42. Takao, H., Abe, O., Yamasue, H., Aoki, S., Kasai, K., & Ohtomo, K. (2010). Cerebral asymmetry in patients with schizophrenia: A voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) study. Journal of Magnetic Resonance Imaging, 31, 221–226.CrossRefGoogle Scholar
  43. Tang, Y., Wang, L., Cao, F., & Tan, L. (2012). Identify schizophrenia using resting-state functional connectivity: An exploratory research and analysis. Biomedical Engineering Online, 11, 50.CrossRefGoogle Scholar
  44. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15, 273–289.CrossRefGoogle Scholar
  45. Venkataraman, A., Whitford, T. J., Westin, C.-F., Golland, P., & Kubicki, M. (2012). Whole brain resting state functional connectivity abnormalities in schizophrenia. Schizophrenia Research, 139, 7–12.CrossRefGoogle Scholar
  46. Walsh, T., McClellan, J. M., McCarthy, S. E., Addington, A. M., Pierce, S. B., Cooper, G. M., Nord, A. S., Kusenda, M., Malhotra, D., Bhandari, A., Stray, S. M., Rippey, C. F., Roccanova, P., Makarov, V., Lakshmi, B., Findling, R. L., Sikich, L., Stromberg, T., Merriman, B., Gogtay, N., Butler, P., Eckstrand, K., Noory, L., Gochman, P., Long, R., Chen, Z., Davis, S., Baker, C., Eichler, E. E., Meltzer, P. S., Nelson, S. F., Singleton, A. B., Lee, M. K., Rapoport, J. L., King, M.-C., & Sebat, J. (2008). Rare structural variants disrupt multiple genes in neurodevelopmental pathways in schizophrenia. Science, 320, 539–543.CrossRefGoogle Scholar
  47. Wheeler, A. L., & Voineskos, A. N. (2014). A review of structural neuroimaging in schizophrenia: From connectivity to connectomics. Frontiers in Human Neuroscience, 8, 653.CrossRefGoogle Scholar
  48. Whitfield-Gabrieli, S., Thermenos, H.W., Milanovic, S., Tsuang, M.T., Faraone, S. V, McCarley, R.W., Shenton, M.E., Green, A.I., Nieto-Castanon, A., LaViolette, P., Wojcik, J., Gabrieli, J.D.E., Seidman, L.J., (2009). Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proceedings of the National Academy of Sciences of the United States of America 106, 1279–1284.Google Scholar
  49. Yan. (2010). DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 1–7. Scholar
  50. Yoon, J. H., Nguyen, D. V., McVay, L. M., Deramo, P., Minzenberg, M. J., Ragland, J. D., Niendham, T., Solomon, M., & Carter, C. S. (2012). Automated classification of fMRI during cognitive control identifies more severely disorganized subjects with schizophrenia. Schizophrenia Research, 135, 28–33.CrossRefGoogle Scholar
  51. Yoon, J. H., Minzenberg, M. J., Raouf, S., D’Esposito, M., & Carter, C. S. (2013). Impaired prefrontal-basal ganglia functional connectivity and substantia nigra hyperactivity in schizophrenia. Biological Psychiatry, 74, 122–129.CrossRefGoogle Scholar
  52. Yu, Y., Shen, H., Zhang, H., Zeng, L.-L., Xue, Z., & Hu, D. (2013). Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings. Biomedical Engineering Online, 12, 10.CrossRefGoogle Scholar
  53. Zarogianni, E., Moorhead, T. W. J., & Lawrie, S. M. (2013). Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level. NeuroImage Clin., 3, 279–289.CrossRefGoogle Scholar
  54. Zhou, Y., Shu, N., Liu, Y., Song, M., Hao, Y., Liu, H., Yu, C., Liu, Z., & Jiang, T. (2008). Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schizophrenia Research, 100, 120–132.CrossRefGoogle Scholar
  55. Zipursky, R.B., Lim, K.O., Sullivan, E. V, Brown, B.W., Pfefferbaum, A., (1992). Widespread cerebral gray matter volume deficits in schizophrenia. Archives of General Psychiatry 49, 195–205.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Singapore Institute for Neurotechnology (SINAPSE), Centre for Life SciencesNational University of SingaporeSingaporeSingapore
  2. 2.Laboratory for Brainbionic Intelligence and Computational NeuroscienceWuyi UniversityJiangmenChina
  3. 3.Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anChina
  4. 4.Department of PsychologyNational University of SingaporeSingaporeSingapore
  5. 5.NUS Graduate School for Integrative Sciences and EngineeringNational University of SingaporeSingaporeSingapore

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