Advertisement

Multiple-Network Classification of Childhood Autism Using Functional Connectivity Dynamics

  • True Price
  • Chong-Yaw Wee
  • Wei Gao
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Characterization of disease using stationary resting-state functional connectivity (FC) has provided important hallmarks of abnormal brain activation in many domains. Recent studies of resting-state functional magnetic resonance imaging (fMRI), however, suggest there is a considerable amount of additional knowledge to be gained by investigating the variability in FC over the course of a scan. While a few studies have begun to explore the properties of dynamic FC for characterizing disease, the analysis of dynamic FC over multiple networks at multiple time scales has yet to be fully examined. In this study, we combine dynamic connectivity features in a multi-network, multi-scale approach to evaluate the method’s potential in better classifying childhood autism. Specifically, from a set of group-level intrinsic connectivity networks (ICNs), we use sliding window correlations to compute intra-network connectivity on the subject level. We derive dynamic FC features for all ICNs over a large range of window sizes and then use a multiple kernel support vector machine (MK-SVM) model to combine a subset of these features for classification. We compare the performance our multi-network, dynamic approach to the best results obtained from single-network dynamic FC features and those obtained from both single- and multi-network static FC features. Our experiments show that integrating multiple networks on different dynamic scales has a clear superiority over these existing methods.

Keywords

Window Size Functional Connectivity Independent Component Analysis Childhood Autism Multiple Time Scale 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Beckmann, C., Mackay, C., Filippini, N.: SM, S.: Group comparison of resting-state fmri data using multi-subject ica and dual regression. In: OBHM (2009)Google Scholar
  2. 2.
    Chang, C., Glover, G.H.: Time–frequency dynamics of resting-state brain connectivity measured with fmri. NeuroImage 50(1), 81–98 (2010)CrossRefGoogle Scholar
  3. 3.
    Chao-Gan, Y., Yu-Feng, Z.: Dparsf: a matlab toolbox for pipeline data analysis of resting-state fmri. Front. Sys. Neurosci. 4 (2010)Google Scholar
  4. 4.
    Di Martino, A., Yan, C., Li, Q., Denio, E., Castellanos, F., Alaerts, K., Anderson, J., Assaf, M., Bookheimer, S., Dapretto, M., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatr. (2013)Google Scholar
  5. 5.
    Elton, A., Alcauter, S., Gao, W.: Network connectivity abnormality profile supports a categorical-dimensional hybrid model of adhd. Human Brain Mapping, n/a–n/a (2014)Google Scholar
  6. 6.
    Fox, M.D., Raichle, M.E.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8(9), 700–711 (2007)CrossRefGoogle Scholar
  7. 7.
    Garrity, A., Pearlson, G., McKiernan, K., Lloyd, D., Kiehl, K., Calhoun, V.: Aberrant default mode functional connectivity in schizophrenia. Am. J. Psychiat. 164(3), 450–457 (2007)CrossRefGoogle Scholar
  8. 8.
    Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V.: Default-mode network activity distinguishes alzheimer’s disease from healthy aging: evidence from functional mri. P. Natl. Acad. Sci. USA 101(13), 4637–4642 (2004)CrossRefGoogle Scholar
  9. 9.
    Hutchison, R.M., Womelsdorf, T., Allen, E.A., Bandettini, P.A., Calhoun, V.D., Corbetta, M., Penna, S.D., Duyn, J., Glover, G., Gonzalez-Castillo, J., et al.: Dynamic functional connectivity: Promises, issues, and interpretations. NeuroImage (2013)Google Scholar
  10. 10.
    Kelly Jr., R.E., Alexopoulos, G.S., Wang, Z., Gunning, F.M., Murphy, C.F., Morimoto, S.S., Kanellopoulos, D., Jia, Z., Lim, K.O., Hoptman, M.J.: Visual inspection of independent components: defining a procedure for artifact removal from fmri data. Journal of Neuroscience Methods 189(2), 233–245 (2010)CrossRefGoogle Scholar
  11. 11.
    Kennedy, D.P., Adolphs, R.: The social brain in psychiatric and neurological disorders. Trends. Cogn. Sci. 16(11), 559–572 (2012)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Leonardi, N., Richiardi, J., Gschwind, M., Simioni, S., Annoni, J.M., Schluep, M., Vuilleumier, P., Van De Ville, D.: Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest. NeuroImage 83, 937–950 (2013)CrossRefGoogle Scholar
  14. 14.
    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 (2014)Google Scholar
  15. 15.
    Mann, H.B., Whitney, D.R., et al.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50–60 (1947)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Spurious but systematic correlations in functional connectivity mri networks arise from subject motion. NeuroImage 59(3), 2142–2154 (2012)CrossRefGoogle Scholar
  17. 17.
    Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., et al.: Advances in functional and structural mr image analysis and implementation as fsl. NeuroImage 23, S208–S219 (2004)Google Scholar
  18. 18.
    Sridharan, D., Levitin, D.J., Menon, V.: A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. P. Natl. A. Sci. 105(34), 12569–12574 (2008)CrossRefGoogle Scholar
  19. 19.
    Uddin, L.Q., Supekar, K., Lynch, C.J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., Menon, V.: Salience network–based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70(8), 869–879 (2013)CrossRefGoogle Scholar
  20. 20.
    Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3), 856–867 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • True Price
    • 1
  • Chong-Yaw Wee
    • 1
  • Wei Gao
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and Biomedical Research Imaging Center (BRIC)The University of North Carolina at Chapel HillUSA

Personalised recommendations