Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification

  • Yingying Zhu
  • Xiaofeng Zhu
  • Han Zhang
  • Wei Gao
  • Dinggang Shen
  • Guorong WuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)


Functional magnetic resonance imaging (fMRI) provides a non-invasive way to investigate brain activity. Recently, convergent evidence shows that the correlations of spontaneous fluctuations between two distinct brain regions dynamically change even in resting state, due to the condition-dependent nature of brain activity. Thus, quantifying the patterns of functional connectivity (FC) in a short time period and changes of FC over time can potentially provide valuable insight into both individual-based diagnosis and group comparison. In light of this, we propose a novel computational method to robustly estimate both static and dynamic spatial-temporal connectivity patterns from the observed noisy signals of individual subject. We achieve this goal in two folds: (1) Construct static functional connectivity across brain regions. Due to low signal-to-noise ratio induced by possible non-neural noise, the estimated FC strength is very sensitive and it is hard to define a good threshold to distinguish between real and spurious connections. To alleviate this issue, we propose to optimize FC which is in consensus with not only the low level region-to-region signal correlations but also the similarity of high level principal connection patterns learned from the estimated link-to-link connections. Since brain network is intrinsically sparse, we also encourage sparsity during FC optimization. (2) Characterize dynamic functional connectivity along time. It is hard to synchronize the estimated dynamic FC patterns and the real cognitive state changes, even using learning-based methods. To address these limitations, we further extend above FC optimization method into the spatial-temporal domain by arranging the FC estimations along a set of overlapped sliding windows into a tensor structure as the window slides. Then we employ low rank constraint in the temporal domain assuming there are likely a small number of discrete states that the brain transverses during a short period of time. We applied the learned spatial-temporal patterns from fMRI images to identify autism subjects. Promising classification results have been achieved, suggesting high discrimination power and great potentials in computer assisted diagnosis.


  1. 1.
    Greicius, M., Srivastava, G., Reiss, A., Menon, V.: Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. PNAS 101, 4637–4642 (2004)CrossRefGoogle Scholar
  2. 2.
    Amaral, D.G., Schumann, C.M., Nordahl, C.W.: Neuroanatomy of autism. Trends Neurosci. 31, 137–145 (2008)CrossRefGoogle Scholar
  3. 3.
    van den Heuvel, M.P., Pol, H.E.H.: Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20, 519–534 (2010)CrossRefGoogle Scholar
  4. 4.
    Hutchison, R.M., Womelsdorf, T., Allen, E.A., Bandettini, P.A., Calhoun, V.D., Corbetta, M., Penna, S.D., Duyn, J.H., Glover, G.H., Gonzalez-Castillo, J., Handwerker, D.A., Keilholz, S., Kiviniemi, V., Leopold, D.A., de Pasquale, F., Sporns, O., Walter, M., Chang, C.: Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013)CrossRefGoogle Scholar
  5. 5.
    Wee, C.-Y., Yap, P.-T., Shen, D.: Diagnosis of autism spectrum disorders using temporally distinct resting-state functional connectivity networks. CNS Neurosci. Ther. 22, 212–219 (2016)CrossRefGoogle Scholar
  6. 6.
    Eavani, H., Satterthwaite, T.D., Gur, R.E., Gur, R.C., Davatzikos, C.: Unsupervised learning of functional network dynamics in resting state fMRI. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 426–437. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  8. 8.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2011)CrossRefzbMATHGoogle Scholar
  9. 9.
    Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: The 20th International Conference on Knowledge Discovery and Data Mining (2014)Google Scholar
  10. 10.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  11. 11.
    Urs, B., et al.: Dynamic reconfiguration of frontal brain networks during executive cognition in humans. PNAS 112, 11678–11683 (2015)CrossRefGoogle Scholar
  12. 12.
    Heung-Il, S., Lee, S.W., Shen, D.: A hybrid of deep network and hidden Markov model for MCI identification with resting-state fMRI. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 573–580. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  13. 13.
    Leonardi, N., et al.: Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. Neuroimage 83, 937–950 (2013)CrossRefGoogle Scholar
  14. 14.
    Zhu, Y., Lucey, S.: Convolutional sparse coding for trajectory reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 529–540 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yingying Zhu
    • 1
  • Xiaofeng Zhu
    • 1
  • Han Zhang
    • 1
  • Wei Gao
    • 2
  • Dinggang Shen
    • 1
  • Guorong Wu
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Biomedical Imaging Research Institute, Department of Biomedical Sciences and ImagingCedars-Sinai Medical CenterLos AngelesUSA

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