Psychiatric Disorders Classification with 3D Convolutional Neural Networks

  • Stefano Campese
  • Ivano LauriolaEmail author
  • Cristina Scarpazza
  • Giuseppe Sartori
  • Fabio Aiolli
Conference paper
Part of the Proceedings of the International Neural Networks Society book series (INNS, volume 1)


Recently, the literature showed that psychiatric disorders, such as Schizophrenia and Bipolar disorder, cause abnormalities in some brain regions. Therefore, several automatic mechanisms based on classical Machine Learning techniques have been used to recognize these diseases by means of the study of neuroimaging. A serious drawback of these approaches is that they consider only the intensity value of the points from neuroimages, without taking into account the spatiality information. Convolutional Neural Networks have subsequently applied to overcome the aforementioned issue, showing their empirical effectiveness on these tasks. However, generally Convolutional Neural Networks operate on 2D slices of the brain instead of the whole 3D structure.

This work aims to analyze the behavior of classical machine learning techniques against 2D and novel 3D Convolutional Neural Network models. An exhaustive empirical assessment has been performed to evaluate these methods on 4 real-world neuroimaging tasks, including Schizophrenia and Bipolar Disorder classification.


Deep Learning 3D Convolutional Neural Networks Neuroimaging Psychiatric disorders 


  1. 1.
    Statistical Parametric Mapping.
  2. 2.
    Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., Varoquaux, G.: Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014)CrossRefGoogle Scholar
  3. 3.
    Ashburner, J., Friston, K.J.: Voxel-based morphometry–the methods. Neuroimage 11(6), 805–821 (2000)CrossRefGoogle Scholar
  4. 4.
    Bledsoe, J.C., Xiao, D., Chaovalitwongse, A., Mehta, S., Grabowski, T.J., Semrud-Clikeman, M., Pliszka, S., Breiger, D.: Diagnostic classification of ADHD versus control: support vector machine classification using brief neuropsychological assessment. J. Atten. Disord. 1087054716649666 (2016)Google Scholar
  5. 5.
    Fan, Y., Resnick, S.M., Wu, X., Davatzikos, C.: Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. Neuroimage 41(2), 277–285 (2008)CrossRefGoogle Scholar
  6. 6.
    Gao, X.W., Hui, R.: A deep learning based approach to classification of CT brain images. In: SAI Computing Conference (SAI), pp. 28–31. IEEE (2016)Google Scholar
  7. 7.
    LeCun, Y., et al.: LeNet-5, Convolutional Neural Networks, p. 20 (2015).
  8. 8.
    Matsugu, M., Mori, K., Mitari, Y., Kaneda, Y.: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw. 16(5–6), 555–559 (2003)CrossRefGoogle Scholar
  9. 9.
    Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)Google Scholar
  10. 10.
    Orru, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G., Mechelli, A.: Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36(4), 1140–1152 (2012)CrossRefGoogle Scholar
  11. 11.
    Payan, A., Montana, G.: Predicting Alzheimer’s disease: a neuroimaging study with 3d convolutional neural networks. arXiv preprint arXiv:1502.02506 (2015)
  12. 12.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)Google Scholar
  13. 13.
    Sarraf, S., Tofighi, G.: Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. arXiv preprint arXiv:1603.08631 (2016)
  14. 14.
    Scarpazza, C., De Simone, M.S.: Voxel-based morphometry: current perspectives. Neurosci. Neuroecon. 5, 19–35 (2016)CrossRefGoogle Scholar
  15. 15.
    Schnack, H.G., Nieuwenhuis, M., van Haren, N.E., Abramovic, L., Scheewe, T.W., Brouwer, R.M., Pol, H.E.H., Kahn, R.S.: Can structural mri aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage 84, 299–306 (2014)CrossRefGoogle Scholar
  16. 16.
    Shioya, A., Saito, Y., Arima, K., Kakuta, Y., Yuzuriha, T., Tanaka, N., Murayama, S., Tamaoka, A.: Neurodegenerative changes in patients with clinical history of bipolar disorders. Neuropathology 35(3), 245–253 (2015)CrossRefGoogle Scholar
  17. 17.
    Vieira, S., Pinaya, W.H., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74, 58–75 (2017)CrossRefGoogle Scholar
  18. 18.
    Zipursky, R.B., Reilly, T.J., Murray, R.M.: The myth of schizophrenia as a progressive brain disease. Schizophr. Bull. 39(6), 1363–1372 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Stefano Campese
    • 1
  • Ivano Lauriola
    • 1
    • 2
    Email author
  • Cristina Scarpazza
    • 3
  • Giuseppe Sartori
    • 3
  • Fabio Aiolli
    • 1
  1. 1.Department of MathematicsUniversity of PadovaPadovaItaly
  2. 2.Bruno Kessler FoundationTrentoItaly
  3. 3.Department of General PsychologyUniversity of PadovaPadovaItaly

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