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Computer Aided Diagnosis of Schizophrenia Based on Local-Activity Measures of Resting-State fMRI

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Book cover Hybrid Artificial Intelligence Systems (HAIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8480))

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Abstract

Resting state functional Magnetic Resonance Imaging (rs-fMRI) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions, such as Schizophrenia. One approach is to perform classification experiments on the data, using feature extraction methods that allow to localize the discriminant locations in the brain, so that further studies may assess the clinical value of such locations. The classification accuracy results ensure that the located brain regions have some relation to the disease. In this paper we explore the discriminant value of brain local activity measures for the classification of Schizophrenia patients. The extensive experimental work, carried out on a publicly available database, provides evidence that local activity measures such as Regional Homogeneity (ReHo) may be useful for such purposes.

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References

  1. Gregory Ashby, F.: Statistical Analysis of fMRI Data, 1st edn. The MIT Press (March 2011)

    Google Scholar 

  2. American Psychiatric Association. DSM-IV-TR: Diagnostic and Statistical Manual of Mental Disorders (Diagnostic & Statistical Manual of Mental Disorders, 4th text revision edn. American Psychiatric Press Inc. (July 2000)

    Google Scholar 

  3. Borrajo, M.L., Baruque, B., et al.: Hybrid neural intelligent system to predict business failure in small-to-medium size enterprise. International Journal of Neural Systems 21(04), 277–296 (2011) 00012

    Google Scholar 

  4. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  5. Calhoun, V.D., Sui, J., Kiehl, K., et al.: Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder. Frontiers in Neuropsychiatric Imaging and Stimulation 2, 75 (2012)

    Google Scholar 

  6. Castro, E., et al.: Characterization of groups using composite kernels and multi-source fMRI analysis data: Application to schizophrenia. NeuroImage 58(2), 526–536 (2011)

    Article  Google Scholar 

  7. Cox, R.W.: AFNI: what a long strange trip it’s been. NeuroImage 62(2), 743–747 (2012)

    Article  Google Scholar 

  8. Fekete, T., et al.: Combining classification with fMRI-Derived complex network measures for potential neurodiagnostics. PLoS ONE 8(5), e62867 (2013)

    Google Scholar 

  9. Fonov, V., Evans, A.C., Botteron, K., Almli, C.R., McKinstry, R.C., Collins, D.L.: Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54(1), 313–327 (2011) PMID: 20656036 PMCID: PMC2962759

    Article  Google Scholar 

  10. Fox, M.D., Zhang, D., Snyder, A.Z., Raichle, M.E.: The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology 101(6), 3270–3283 (2009) PMID: 19339462

    Article  Google Scholar 

  11. Friston, K.J., Williams, S., Howard, R., Frackowiak, R.S., Turner, R.: Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 35(3), 346–355 (1996) PMID: 8699946

    Article  Google Scholar 

  12. Jenkinson, M., et al.: FSL. NeuroImage 62(2), 782–790 (2012)

    Article  MathSciNet  Google Scholar 

  13. Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Medical Image Analysis 5(2), 143–156 (2001)

    Article  Google Scholar 

  14. Liu, D., Yan, C., Ren, J., Yao, L., Kiviniemi, V.J., Zang, Y.: Using coherence to measure regional homogeneity of restingstate fMRI signal. Frontiers in Systems Neuroscience 4, 24 (2010)

    Google Scholar 

  15. McGuire, P.K., Frith, C.D.: Disordered functional connectivity in schizophrenia. Psychological Medicine 26(4), 663–667 (1996) PMID: 8817700

    Article  Google Scholar 

  16. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., et al.: Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12, 2825–2830 (2011)

    MATH  Google Scholar 

  17. Savio, A., Graña, M.: Deformation based feature selection for computer aided diagnosis of alzheimer’s disease. Expert Systems with Applications 40(5), 1619–1628 (2013) 00006

    Article  Google Scholar 

  18. Charpentier, J., Savio, A.: Neural classifiers for schizophrenia diagnostic support on diffusion imaging data. Neural Network World 20, 935–949 (2010) 00005

    Google Scholar 

  19. Stark, D.E., Margulies, D.S., et al.: Regional variation in interhemispheric coordination of intrinsic hemodynamic fluctuations. The Journal of Neuroscience 28(51), 13754–13764 (2008)

    Article  Google Scholar 

  20. Tang, Y., Wang, L., Cao, F., Tan, L.: Identify schizophrenia using resting-state functional connectivity: an exploratory research and analysis. BioMedical Engineering OnLine 11(1), 50 (2012) PMID: 22898249

    Article  Google Scholar 

  21. Van Dijk, K.R.A., Sabuncu, M.R., Buckner, R.L.: The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59(1), 431–438 (2012)

    Article  Google Scholar 

  22. Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (September 1998)

    Google Scholar 

  23. Wernicke, C.: Grundriss der Psychiatrie in klinischen Vorlesungen / von Carl Wernicke. VDM Verlag Dr. Müller, Saarbrücken (2007)

    Google Scholar 

  24. Wozniak, M., Graña, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Information Fusion 16, 3–17 (2014)

    Article  Google Scholar 

  25. Wible, C.G., Shenton, M.E., Hokama, H., Kikinis, R., Jolesz, F.A., Metcalf, D., McCarley, R.W.: Prefrontal cortex and schizophrenia: A quantitative magnetic resonance imaging study. Archives of General Psychiatry 52(4), 279–288 (1995)

    Article  Google Scholar 

  26. Zang, Y.-F., et al.: Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain & Development 29(2), 83–91 (2007) PMID: 16919409

    Article  MathSciNet  Google Scholar 

  27. Zang, Y., et al.: Regional homogeneity approach to fMRI data analysis. NeuroImage 22(1), 394–400 (2004)

    Article  MathSciNet  Google Scholar 

  28. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 20(1), 45–57 (2001)

    Article  Google Scholar 

  29. Zuo, X.-N., Martino, A.D., Kelly, C., et al.: The oscillating brain: Complex and reliable. NeuroImage 49(2), 1432–1445 (2010) PMID: 19782143 PMCID: PMC2856476

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastián, Spain

    Alexandre Savio, Darya Chyzhyk & Manuel Graña

Authors
  1. Alexandre Savio
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  2. Darya Chyzhyk
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  3. Manuel Graña
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, 1678, Nicosia, Cyprus

    Marios Polycarpou

  2. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    André C. P. L. F. de Carvalho

  3. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of Salamanca, Plaza de la Merced S/N, 37008 Salamanca, Spain and University of A Coruna, Escuela Universitaria Politecnica, Departamento de Enxeñeria Industrial, A Coruna, Spain

    Héctor Quintian

  6. University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

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© 2014 Springer International Publishing Switzerland

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Savio, A., Chyzhyk, D., Graña, M. (2014). Computer Aided Diagnosis of Schizophrenia Based on Local-Activity Measures of Resting-State fMRI. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-07617-1_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07616-4

  • Online ISBN: 978-3-319-07617-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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