Skip to main content
SpringerLink
Log in

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

  • Original Research
  • Published:
Brain Imaging and Behavior Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, D.C.: APA.

    Google Scholar 

  • 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.

    PubMed  PubMed Central  Google Scholar 

  • Andreasen, N. C., & Pierson, R. (2008). The role of the cerebellum in schizophrenia. Biological Psychiatry, 64, 81–88.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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. https://doi.org/10.3389/fnhum.2012.00145.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Fornito, A., & Bullmore, E. T. (2015). Reconciling abnormalities of brain network structure and function in schizophrenia. Current Opinion in Neurobiology, 30, 44–50.

    Article  CAS  PubMed  Google Scholar 

  • Friston, K., Brown, H. R., Siemerkus, J., & Stephan, K. E. (2016). The dysconnection hypothesis (2016). Schizophrenia Research, 176, 83–94.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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. https://doi.org/10.1016/j.schres.2012.07.027.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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. https://doi.org/10.1109/TCDS.2016.2555952.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Marín, O. (2012). Interneuron dysfunction in psychiatric disorders. Nature Reviews. Neuroscience, 13, 107–120.

    Article  CAS  PubMed  Google Scholar 

  • 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. https://doi.org/10.1002/hbm.22065.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Os, J. V., & Kapur, S. (2009). Schizophrenia. Lancet, 374, 635–645.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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. https://doi.org/10.1371/journal.pone.0025031.

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Wheeler, A. L., & Voineskos, A. N. (2014). A review of structural neuroimaging in schizophrenia: From connectivity to connectomics. Frontiers in Human Neuroscience, 8, 653.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

  • Yan. (2010). DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 1–7. https://doi.org/10.3389/fnsys.2010.00013.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

Download references

Author information

Authors and Affiliations

  1. Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, Singapore, Singapore

    Junhua Li, Yu Sun, Anastasios Bezerianos & Rongjun Yu

  2. Laboratory for Brainbionic Intelligence and Computational Neuroscience, Wuyi University, Jiangmen, China

    Junhua Li

  3. Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China

    Junhua Li

  4. Department of Psychology, National University of Singapore, Singapore, Singapore

    Yi Huang & Rongjun Yu

  5. NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore

    Yi Huang

Authors
  1. Junhua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anastasios Bezerianos
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rongjun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Junhua Li, Yu Sun or Rongjun Yu.

Ethics declarations

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.

Electronic supplementary material

ESM 1

(DOCX 83007 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Sun, Y., Huang, Y. et al. Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method. Brain Imaging and Behavior 13, 1386–1396 (2019). https://doi.org/10.1007/s11682-018-9947-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11682-018-9947-4

Keywords

Search

Navigation