Advertisement

Employing Temporal Properties of Brain Activity for Classifying Autism Using Machine Learning

  • Preetam Srikar DammuEmail author
  • Raju Surampudi Bapi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)

Abstract

Exploration of brain imaging data with machine learning methods has been beneficial in identifying and probing the impacts of neurological disorders. Psychopathological ailments that disrupt brain activity can be discerned with the help of resting-state functional magnetic resonance imaging (rs-fMRI). Research has revealed that brain connectivity is dynamic in nature and that its dynamic properties are affected by brain disorders. In the literature, numerous approaches have been proposed for identifying the presence of Autism Spectral Disorder (ASD), yet most of them do not consider brain dynamics in their diagnostic process. Significant amount of knowledge can be procured by taking the evolution of brain connectivity over time into account. In this work, we propose a new approach that leverages brain dynamics in the classification of autistic and neurotypical subjects using rs-fMRI data. We examined the proposed method on a large multi-site dataset known as ABIDE (Autism Brain Imaging Data Exchange) and have achieved state-of-the-art classification results with an accuracy of 73.6%. Our work has shown that taking the temporal properties of brain connectivity into account improves the classification performance.

Keywords

rs-fMRI Autism detection Dynamic FC 

References

  1. 1.
    Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24(3), 663–676 (2014)CrossRefGoogle Scholar
  2. 2.
    Arbabshirani, M.R., Plis, S., Sui, J., Calhoun, V.D.: Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145, 137–165 (2017)CrossRefGoogle Scholar
  3. 3.
    Aylward, E.H., et al.: MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults. Neurology 53(9), 2145–2145 (1999)CrossRefGoogle Scholar
  4. 4.
    Chang, C., Glover, G.H.: Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50(1), 81–98 (2010)CrossRefGoogle Scholar
  5. 5.
    Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)Google Scholar
  6. 6.
    Craddock, C., et al.: The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives. Neuroinformatics (41) (2013)Google Scholar
  7. 7.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Heinsfeld, A.S., Franco, A.R., Craddock, R.C., Buchweitz, A., Meneguzzi, F.: Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage Clin. 17, 16–23 (2018)CrossRefGoogle Scholar
  9. 9.
    Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)Google Scholar
  10. 10.
    Huettel, S.A., Song, A.W., McCarthy, G., et al.: Functional magnetic resonance imaging, vol. 1. Sinauer Associates, Sunderland (2004)Google Scholar
  11. 11.
    Hutchison, R.M., et al.: Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013)CrossRefGoogle Scholar
  12. 12.
    Just, M.A., Keller, T.A., Kana, R.K.: A theory of autism based on frontal-posterior underconnectivity. In: Development and Brain Systems in Autism, pp. 35–63 (2013)Google Scholar
  13. 13.
    Kana, R.K., Keller, T.A., Cherkassky, V.L., Minshew, N.J., Just, M.A.: Atypical frontal-posterior synchronization of Theory of Mind regions in autism during mental state attribution. Soc. Neurosci. 4(2), 135–152 (2009)CrossRefGoogle Scholar
  14. 14.
    Khosla, M., Jamison, K., Kuceyeski, A., Sabuncu, M.R.: 3D convolutional neural networks for classification of functional connectomes. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 137–145. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00889-5_16CrossRefGoogle Scholar
  15. 15.
    Koshino, H., Carpenter, P.A., Minshew, N.J., Cherkassky, V.L., Keller, T.A., Just, M.A.: Functional connectivity in an fmri working memory task in high-functioning autism. Neuroimage 24(3), 810–821 (2005)CrossRefGoogle Scholar
  16. 16.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRefGoogle Scholar
  17. 17.
    Lord, C., Rutter, M., DiLavore, P.C., Risi, S., Gotham, K., Bishop, S., et al.: Autism diagnostic observation schedule: ADOS. Western Psychological Services, Los Angeles, CA (2012)Google Scholar
  18. 18.
    Ma, S., Calhoun, V.D., Phlypo, R., Adalı, T.: Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis. NeuroImage 90, 196–206 (2014)CrossRefGoogle Scholar
  19. 19.
    Naik, S., Subbareddy, O., Banerjee, A., Roy, D., Bapi, R.S.: Metastability of cortical bold signals in maturation and senescence. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 4564–4570. IEEE (2017)Google Scholar
  20. 20.
    Nielsen, J.A., et al.: Multisite functional connectivity mri classification of autism: ABIDE results. Front. Hum. Neurosci. 7, 599 (2013)CrossRefGoogle Scholar
  21. 21.
    Plitt, M., Barnes, K.A., Martin, A.: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage Clin. 7, 359–366 (2015)CrossRefGoogle Scholar
  22. 22.
    Price, T., Wee, C.-Y., Gao, W., Shen, D.: Multiple-network classification of childhood autism using functional connectivity dynamics. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 177–184. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10443-0_23CrossRefGoogle Scholar
  23. 23.
    Ryali, S., et al.: Temporal dynamics and developmental maturation of salience, default and central-executive network interactions revealed by variational bayes hidden Markov modeling. PLoS Comput. Biol. 12(12), e1005138 (2016)CrossRefGoogle Scholar
  24. 24.
    Schipul, S.E., Williams, D.L., Keller, T.A., Minshew, N.J., Just, M.A.: Distinctive neural processes during learning in autism. Cereb. Cortex 22(4), 937–950 (2011)CrossRefGoogle Scholar
  25. 25.
    Surampudi, S.G., Misra, J., Deco, G., Bapi, R.S., Sharma, A., Roy, D.: Resting state dynamics meets anatomical structure: temporal multiple kernel learning (tMKL) model. NeuroImage 184, 609–620 (2019)CrossRefGoogle Scholar
  26. 26.
    Vapnik, V.: The support vector method of function estimation. In: Suykens, J.A.K., Vandewalle, J. (eds.) Nonlinear Modeling, pp. 55–85. Springer, Boston (1998).  https://doi.org/10.1007/978-1-4615-5703-6_3CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer and Information SciencesUniversity of HyderabadHyderabadIndia

Personalised recommendations