Intrinsic Discriminant Analysis of Functional Connectivity for Multiclass Classification

  • Dewen Hu
  • Ling-Li Zeng


Major depression and schizophrenia are two of the most serious diagnosed psychiatric disorders that share some similar behavioral symptoms. Whether these similar behavioral symptoms underlie any convergent psychiatric pathological mechanisms is to date unclear. In this chapter, the whole-brain resting-state functional magnetic resonance imaging of major depression and schizophrenia was investigated by using intrinsic discriminant analysis, which is a supervised linear dimensionality reduction method maximizing the inter-class difference while minimizing the intra-class difference. Thirty-two schizophrenic patients, 19 major depressive disorders, and 38 healthy controls underwent the resting-state functional magnetic resonance imaging scanning. Support vector machine in conjunction with intrinsic discriminant analysis was used to solve the multi-classification problem, resulting in a correct classification rate of 80.9% via leave-one-out cross-validation. It was revealed that the depression and schizophrenia groups both showed altered functional connections related to the medial prefrontal cortex, anterior cingulate cortex, thalamus, hippocampus, and cerebellum. However, the prefrontal cortex, amygdala, and temporal poles were found to be different affected between major depression and schizophrenia. This preliminary study suggests that altered connections within or across the default mode network and cerebellum might account for the common behavioral symptoms between major depression and schizophrenia. In addition, connections related to the prefrontal cortex and affective network showed promised as biomarkers in discrimination between the two disorders.


Functional connectivity Intrinsic discriminant analysis Multi-classification Depression Schizophrenia 



This chapter was modified from a paper reported by our group in PLoS ONE [42].


  1. 1.
    Hafner, H., Maurer, K., Trendler, G., Heiden, W.a.d., Schmidt, M.: The early course of schizophrenia and depression. Eur. Arch. Psychiatry Clin. Neurosci. 255, 167–173 (2005)PubMedCrossRefGoogle Scholar
  2. 2.
    Kessler, R.C., McGonagle, K.A., Zhao, S., Nelson, C.B., Hughes, M., Eshleman, S., Wittchen, H.U., Kendler, K.S.: Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the united states. Results from the national comorbidity survey. Psychiatry 51, 8–19 (1994)Google Scholar
  3. 3.
    Robins, L., Regier, D.: Psychiatric Disorders in America: The Epidemiological Catchment Area Study. Free Press, New York (1991)Google Scholar
  4. 4.
    Liddle, P.: Schizophrenic syndromes, cognitive performance and neurological dysfunction. Psychol. Med. 17, 49–57 (1987)PubMedCrossRefGoogle Scholar
  5. 5.
    Liddle, P.: The symptoms of chronic schizophrenia: a re-examination of the positive-negative dichotomy. Br. J. Psychiatry 151, 145–151 (1987)PubMedCrossRefGoogle Scholar
  6. 6.
    Van, O.J., Verdoux, H., Maurice-Tison, S., Gay, B., Liraud, F., Salamon, R., Bourgeois, M.: Self-reported psychosis-like symptoms and the continuum of psychosis. Soc. Psychiatry Psychiatr. Epidemiol. 34, 459–463 (1999)CrossRefGoogle Scholar
  7. 7.
    Maier, W., Lichtermann, D., Franke, P., Heun, R., Falkai, P., Rietschel, M.: The dichotomy of schizophrenia and affective disorders in extended pedigrees. Schizophr. Res. 57(2), 259–266 (2002). PubMedCrossRefPubMedCentralGoogle Scholar
  8. 8.
    Heckers, S., Stone, D., Walsh, J., Shick, J., Koul, P., Benes, F.: Differential hippocampal expression of glutamic acid decarboxylase 65 and 67 messenger rna in bipolar disorder and schizophrenia. Arch. Gen. Psychiatry 59, 521–529 (2002)PubMedCrossRefGoogle Scholar
  9. 9.
    Elkis, H., Friedman, L., Wise, A., Meltzer, H.: Meta-analysis of studies of ventricular enlargement and cortical sulcal prominence in mood disorders comparisons with controls or patients with schizophrenia. Arch. Gen. Psychiatry 52, 735–746 (1995)PubMedCrossRefGoogle Scholar
  10. 10.
    Mulholland, C., Cooper, S.: The symptom of depression in schizophrenia and its management. Adv. Psychiatr. Treat. 6, 169–177 (2000)CrossRefGoogle Scholar
  11. 11.
    Angelucci, F., Brenè, S., Mathé, A.: BDNF in schizophrenia, depression and corresponding animal models. Mol. Psychiatry 10, 345–352 (2005)PubMedCrossRefGoogle Scholar
  12. 12.
    Woodward, N.D., Rogers, B., Heckers, S.: Functional resting-state networks are differentially affected in schizophrenia. Schizophr. Res. 130(1), 86–93 (2011). PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., Hu, D.: Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135(Pt 5), 1498–1507 (2012). PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Kenna, H., Reiss, A.L., Schatzberg, A.F.: Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62, 429–437 (2007)PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Kenna, H., Reiss, A.L., Schatzberg, A.F.: Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol. Psychiatry 62(5), 429–437 (2007). Neurocircuitry and Neuroplasticity Abnormalities in Mood and Anxiety Disorders. PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Bluhm, R., Williamson, P., Lanius, R., Théberge, J., Densmore, M., Bartha, R., Neufeld, R., Osuch, E.: Resting state default-mode network connectivity in early depression using a seed region-of-interest analysis: decreased connectivity with caudate nucleus. Psychiatry Clin. Neurosci. 63(6), 754–761 (2009). PubMedCrossRefGoogle Scholar
  17. 17.
    Zhou, Y., Yu, C., Zheng, H., Liu, Y., Song, M., Qin, W., Li, K., Jiang, T.: Increased neural resources recruitment in the intrinsic organization in major depression. J. Affect. Disord. 121(3), 220–230 (2010). PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Sheline, Y.I., Price, J.L., Yan, Z., Mintun, M.A.: Resting-state functional MRI in depression unmasks increased connectivity between networks via the dorsal nexus. Proc. Natl. Acad. Sci. 107(24), 11020–11025 (2010). CrossRefGoogle Scholar
  19. 19.
    Mayberg, H.S.: Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment. Br. Med. Bull. 65, 193–207 (2003)PubMedCrossRefGoogle Scholar
  20. 20.
    Huang, X.-Q., Lui, S., Deng, W., Chan, R.C., Wu, Q.-Z., Jiang, L.-J., Zhang, J.-R., Jia, Z.-Y., Li, X.-L., Li, F., Chen, L., Li, T., Gong, Q.-Y.: Localization of cerebral functional deficits in treatment-naive, first-episode schizophrenia using resting-state fMRI. Neuroimage 49(4), 2901–2906 (2010). PubMedCrossRefGoogle Scholar
  21. 21.
    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.: Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc. Natl. Acad. Sci. CrossRefGoogle Scholar
  22. 22.
    Woodward, N.D., Rogers, B., Heckers, S.: Functional resting-state networks are differentially affected in schizophrenia. Schizophr. Res. 130, 86–93 (2011)PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Salvador, R., Sarro, S., Gomar, J., Ortiz-Gil, J., Vila, F., Capdevila, A.: Overall brain connectivity maps show cortico-subcortical abnormalities in schizophrenia. Hum. Brain Mapp. 31, 2003–2014 (2010)PubMedCrossRefGoogle Scholar
  24. 24.
    Boksman, K., Théberge, J., Williamson, P., Drost, D.J., Malla, A., Densmore, M., Takhar, J., Pavlosky, W., Menon, R.S., Neufeld, R.W.: A 4.0-T fMRI study of brain connectivity during word fluency in first-episode schizophrenia. Schizophr. Res. 75(2), 247–263 (2005). PubMedCrossRefGoogle Scholar
  25. 25.
    Garrity, A.G., Pearlson, G.D., McKiernan, K., Lloyd, D., Kiehl, K.A., Calhoun, V.D.: Aberrant “default mode” functional connectivity in schizophrenia. Am. J. Psychiatry 164(3), 450–475 (2007). PubMedCrossRefGoogle Scholar
  26. 26.
    Bluhm, R.L., Miller, J., Lanius, R.A., Osuch, E.A., Boksman, K., Neufeld, R., Théberge, J., Schaefer, B., Williamson, P.: Spontaneous low-frequency fluctuations in the bold signal in schizophrenic patients: anomalies in the default network. Schizophr. Bull. 33(4), 1004–1012 (2007)PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Micheloyannis, S., Pachou, E., Stam, C.J., Breakspear, M., Bitsios, P., Vourkas, M., Erimaki, S., Zervakis, M.: Small-world networks and disturbed functional connectivity in schizophrenia. Schizophr. Res. 87(1), 60–66 (2006). PubMedCrossRefGoogle Scholar
  28. 28.
    Fan, Y., Shen, D.G., Gur, R.C., Gur, R.E., Davatzikos, C.: Compare: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26, 93–105 (2007)PubMedCrossRefGoogle Scholar
  29. 29.
    Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45(1, Suppl. 1), S199–S209 (2009). Mathematics in Brain Imaging. PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Zhu, C.-Z., Zang, Y.-F., Cao, Q.-J., Yan, C.-G., He, Y., Jiang, T.-Z., Sui, M.-Q., Wang, Y.-F.: Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. Neuroimage 40(1), 110–120 (2008). PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Shen, H., Wang, L., Liu, Y., Hu, D.: Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage 49(4), 3110–3121 (2010). PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Pagani, M., Salmaso, D., Rodriguez, G., Nardo, D., Nobili, F.: Principal component analysis in mild and moderate alzheimer’s disease – a novel approach to clinical diagnosis. Psychiatry Res. Neuroimaging 173(1), 8–14 (2009). PubMedCrossRefPubMedCentralGoogle Scholar
  33. 33.
    Jafri, M.J., Pearlson, G.D., Calhoun, V.D.: A maximal-correlation approach using ICA for testing functional network connectivity applied to schizophrena. Biomed. Imaging Nano Macro ISBI 468–471 (2007)Google Scholar
  34. 34.
    Kawasaki, Y., Suzuki, M., Kherif, F., Takahashi, T., Zhou, S.-Y., Nakamura, K., Matsui, M., Sumiyoshi, T., Seto, H., Kurachi, M.: Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls. Neuroimage 34(1), 235–242 (2007). PubMedCrossRefPubMedCentralGoogle Scholar
  35. 35.
    Wang, Y., Wu, Y.: Face recognition using intrinsicfaces. Pattern Recognit. 43(10), 3580–3590 (2010). CrossRefGoogle Scholar
  36. 36.
    Vapnik, V.: The Natures of Statistical Learning Theory. Springer, New YorkGoogle Scholar
  37. 37.
    A. P. Association: Diagnostic and statistical manual of mental disorders, pp. 143–146. American Psychiatric Association, Washington (1994).
  38. 38.
    Kay, S., Fiszbein, A., Opler, L.: The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13, 261–276 (1987)CrossRefGoogle Scholar
  39. 39.
    Hamilton, M.: A rating scale for depression. J. Neurol. Neurosurg. Psychiatry 23, 56–62 (1960)PubMedPubMedCentralCrossRefGoogle Scholar
  40. 40.
    Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a acroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage. 15, 273–289 (2002)PubMedCrossRefGoogle Scholar
  41. 41.
    Achard, S., Salvador, R., Whitcher, B., Suckling, J., Bullmore, E.: A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 26(1), 63–72 (2006). PubMedCrossRefGoogle Scholar
  42. 42.
    Yu, Y., Shen, H., Zeng, L.L., Ma, Q.M., Hu, D.W.: Convergent and divergent functional connectivity patterns in schizophrenia and depression. PLoS ONE 8, e68250 (2013)PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)PubMedCrossRefGoogle Scholar
  44. 44.
    Raichle, M., Mintun, M.: Brain work and brain imaging. Annu. Rev. Neurosci. 29, 449–476 (2006)PubMedCrossRefGoogle Scholar
  45. 45.
    Liu, F., Guo, W., Yu, D., Gao, Q., Gao, K., Xue, Z., Du, H., Zhang, J., Tan, C., Liu, Z., Zhao, J., Chen, H.: Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PLoS ONE 7(7), e40968 (2012). PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Mørch, N., Hansen, L.K., Strother, S.C., Svarer, C., Rottenberg, D.A., Lautrup, B., Savoy, R., Paulson, O.B.: Nonlinear versus linear models in functional neuroimaging: learning curves and generalization crossover. In: Duncan, J., Gindi, G. (eds.) Information Processing in Medical Imaging, pp. 259–270. Springer, Berlin/Heidelberg (1997)CrossRefGoogle Scholar
  47. 47.
    Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V.: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. 100(1), 253–258 (2003). CrossRefGoogle Scholar
  48. 48.
    Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L.: A default mode of brain function. Proc. Natl. Acad. Sci. 98(2), 676–682 (2001). CrossRefGoogle Scholar
  49. 49.
    Botvinick, M., Braver, T., Barch, D., Carter, C., Cohen, J.: Conflict monitoring and cognitive control. Psychol. Rev. 108, 624–652 (2001)PubMedCrossRefGoogle Scholar
  50. 50.
    Koshino, H., Minamoto, T., Ikeda, T., Osaka, M., Otsuka, Y., Osaka, N.: Anterior medial prefrontal cortex exhibits activation during task preparation but deactivation during task execution. PLoS ONE 6, e22909 (2011)PubMedPubMedCentralCrossRefGoogle Scholar
  51. 51.
    Phelps, E.A.: Human emotion and memory: interactions of the amygdala and hippocampal complex. Curr. Opin. Neurobiol. 14(2), 198–202 (2004). PubMedCrossRefGoogle Scholar
  52. 52.
    LaBar, K.S., Cabeza, R.: Cognitive neuroscience of emotional memory. Nat. Rev. Neurosci. 7(1), 54–64 (2006). PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Talamini, L.M., Meeter, M., Elvevag, B., Murre, J.M., Goldberg, T.E.: Reduced parahippocampal connectivity produces schizophrenialike memory deficits in simulated neural circuits with reduced parahippocampal connectivity. Arch. Gen. Psychiatry 62, 485–493 (2005)PubMedCrossRefGoogle Scholar
  54. 54.
    Ilonen, T., Taiminen, T., Karlsson, H., Lauerma, H., Leinonen, K.-M., Wallenius, E., Tuimala, P., Salokangas, R.K.: Diagnostic efficiency of the rorschach schizophrenia and depression indices in identifying first-episode schizophrenia and severe depression. Psychiatry Res. 87(2), 183–192 (1999). PubMedCrossRefGoogle Scholar
  55. 55.
    Kohler, C., Gur, R.C., Swanson, C.L., Petty, R., Gur, R.E.: Depression in schizophrenia: I. Association with neuropsychological deficits. Biol. Psychiatry 43(3), 165–172 (1998). PubMedCrossRefGoogle Scholar
  56. 56.
    Buchanan, R., Vladar, K., Barta, P., Pearlson, G.: Structural evaluation of the prefrontal cortex in schizophrenia. Am. J. Psychiatry 155(8), 1049–1055 (1998). PubMedCrossRefGoogle Scholar
  57. 57.
    Buchsbaum, M., Hazlett, E.: Positron emission tomography studies of abnormal glucose metabolism in schizophrenia. Schizophr. Bull. 24, 343–364 (1998)PubMedCrossRefGoogle Scholar
  58. 58.
    Bearden, C., Hoffman, K., Cannon, T.: The neuropsychology and neuroanatomy of bipolar affective disorder: a critical review. Bipolar Disord. 3(3), 106–150 (2001). PubMedCrossRefGoogle Scholar
  59. 59.
    bin Guo, W., Liu, F., min Xue, Z., Yu, Y., qiong Ma, C., lian Tan, C., li Sun, X., dong Chen, J., ning Liu, Z., qing Xiao, C., fu Chen, H., ping Zhao, J.: Abnormal neural activities in first-episode, treatment-naïve, short-illness-duration, and treatment-response patients with major depressive disorder: a resting-state fMRI study. J. Affect. Disord. 135(1), 326–331 (2011). PubMedPubMedCentralCrossRefGoogle Scholar
  60. 60.
    Picard, H., Amado, I., MoucherMages, S., Olié, J.-P., Krebs, M.-O.: The role of the cerebellum in schizophrenia: an update of clinical cognitive, and functional evidences. Schizophr. Bull. 34, 155–172 (2008)PubMedCrossRefGoogle Scholar
  61. 61.
    Schmahmann, J.D., Caplan, D.: Cognition, emotion and the cerebellum. Brain 129, 290–292 (2006)PubMedCrossRefGoogle Scholar
  62. 62.
    Andreasen, N.C., Rezai, K., Alliger, R., Swayze, V.I., Flaum, M., Kirchner, P.: Hypofrontality in neuroleptic-naive patients and in patients with chronic schizophrenia: assessment with xenon 133 single photon emission computed tomography and the tower of London. Arch. Gen. Psychiatry 49, 943–958 (1992)PubMedCrossRefGoogle Scholar
  63. 63.
    Menon, V., Anagnoson, R., Mathalon, D., Glover, G., Pfefferbaum, A.: Functional neuroanatomy of auditory working memory in schizophrenia: relation to positive and negative symptoms. Neuroimage 13(3), 433–446 (2001). PubMedCrossRefGoogle Scholar
  64. 64.
    Landro, N.I., Stiles, T.C., Sletvold, H.: Neuropsychological function in nonpsychotic unipolar major depression. Neuropsychiatry Neuropsychol. Behav. Neurol. 14, 233–240 (2001)PubMedGoogle Scholar
  65. 65.
    Pelosi, L., Slade, T., Blumhardt, L., Sharma, V.: Working memory dysfunction in major depression: an event-related potential study. Clin. Neurophysiol. 111(9), 1531–1543 (2000). PubMedCrossRefGoogle Scholar
  66. 66.
    Merriam, E., Thase, M., Haas, G., Keshavan, M., Sweeney, J.: Prefrontal cortical dysfunction in depression determined by Wisconsin card sorting test performance. Am. J. Psychiatry 156(5), 780–782 (1999). http://10.1176/ajp.156.5.780.
  67. 67.
    Barch, D.M., Sheline, Y.I., Csernansky, J.G., Snyder, A.Z.: Working memory and prefrontal cortex dysfunction: specificity to schizophrenia compared with major depression. Biol. Psychiatry 53(5), 376–384 (2003). PubMedCrossRefGoogle Scholar
  68. 68.
    Ding, S.-L., Van Hoesen, G.W., Cassell, M.D., Poremba, A.: Parcellation of human temporal polar cortex: a combined analysis of multiple cytoarchitectonic, chemoarchitectonic, and pathological markers. J. Comp. Neurol. 514(6), 595–623 (2009). PubMedPubMedCentralCrossRefGoogle Scholar
  69. 69.
    Siever, L.J.: Neurobiology of aggression and violence. Am. J. Psychiatry 165, 429–442 (2008)PubMedPubMedCentralCrossRefGoogle Scholar
  70. 70.
    Wright, I., Rabe-Hesketh, S., Woodruff, P., David, A., Murray, R., Bullmore, E.: Meta-analysis of regional brain volumes in schizophrenia. Am. J. Psychiatry 157(1), 16–25 (2000). PubMedCrossRefGoogle Scholar
  71. 71.
    Savitz, J., Drevets, W.C.: Bipolar and major depressive disorder: neuroimaging the developmental-degenerative divide. Neurosci. Biobehav. Rev. 33(5), 699–771 (2009). Translational Aspects of Stopping and Response Control. CrossRefGoogle Scholar
  72. 72.
    Hamazaki, K., Hamazaki, T., Inadera, H.: Fatty acid composition in the postmortem amygdala of patients with schizophrenia, bipolar disorder, and major depressive disorder. J. Psychiatr. Res. 46(8), 1024–1028 (2012). PubMedCrossRefGoogle Scholar
  73. 73.
    Hinkley, L.B., Vinogradov, S., Guggisberg, A.G., Fisher, M., Findlay, A.M., Nagarajan, S.S.: Clinical symptoms and alpha band resting-state functional connectivity imaging in patients with schizophrenia: implications for novel approaches to treatment. Biol. Psychiatry 70(12), 1134–1142 (2011). Copy Number Variants and Schizophrenia Risk. PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Fox, M.D., Zhang, D.Y., Snyder, A.Z., Raichle, M.E.: The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101, 3270–3283 (2009)PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Murphy, K., Birn, R.M., Handwerker, D.A., Jones, T.B., Bandettini, P.A.: The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44(3), 893–905 (2009). PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dewen Hu
    • 1
  • Ling-Li Zeng
    • 1
  1. 1.College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina

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