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Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis

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Connectomics in NeuroImaging (CNI 2017)

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Abstract

Many studies in the literature have validated the use of resting-state fMRI (rs-fMRI) for brain disorder/disease identification. Unlike the existing methods that mostly first estimate functional connectivity and then extract features with a graph theory, in this paper, we propose a novel method that directly models the temporal stochastic patterns inherent in BOLD signals for each Region Of Interest (ROI) individually. Specifically, we model temporal BOLD signal fluctuation of an individual ROI by means of Hidden Markov Models (HMMs), and then compute a regional BOLD signal likelihood with the trained HMMs. By regarding the BOLD signal likelihood of ROIs over a whole brain as features, we build a classifier that can discriminate subjects with Autism Spectrum Disorder (ASD) from Normal healthy Controls (NC). In addition, we also devise a method to further investigate the characteristics of temporal dynamics in rs-fMRI estimated by HMMs. For group comparison, we use the metrics of state occupancy rate and lifetime of the optimal hidden states that best represent the temporal BOLD signals. In our experiments with ABIDE cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies among competing methods. We could also identify the group differences in temporal dynamics between ASD and NC in terms of state occupancy rate and lifetime of individual states.

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Notes

  1. 1.

    ABIDE (http://fcon_1000.projects.nitrc.org/indi/abide) provides preprocessed rs-fMRI datasets for ASD and NC by performing four different preprocessing pipelines. In this work, we used datasets preprocessed by the Data Processing Assistant for Resting-State fMRI (DPARSF), a convenient plug-in software based on SPM and REST.

References

  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. Cerebral Cortex p. bhs352 (2012)

    Google Scholar 

  2. Chang, C., Glover, G.H.: Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50(1), 81–98 (2010)

    Article  Google Scholar 

  3. Chen, H., Duan, X., Liu, F., Lu, F., Ma, X., Zhang, Y., Uddin, L.Q., Chen, H.: Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity: a multi-center study. Prog. Neuropsychopharmacol. Biol. Psychiatry 64, 1–9 (2016)

    Article  Google Scholar 

  4. Craddock, R.C., Holtzheimer, P.E., Hu, X.P., Mayberg, H.S.: Disease state prediction from resting state functional connectivity. Magn. Reson. Med. 62(6), 1619–1628 (2009)

    Article  Google Scholar 

  5. Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., Anderson, J.S., Assaf, M., Bookheimer, S.Y., Dapretto, M., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659–667 (2014)

    Article  Google Scholar 

  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: Information Processing in Medical Imaging, vol. 23, p. 426. NIH Public Access (2013)

    Google Scholar 

  7. Fan, Y., Liu, Y., Wu, H., Hao, Y., Liu, H., Liu, Z., Jiang, T.: Discriminant analysis of functional connectivity patterns on Grassmann manifold. NeuroImage 56(4), 2058–2067 (2011)

    Article  Google Scholar 

  8. Friston, K.J., Williams, S., Howard, R., Frackowiak, R.S., Turner, R.: Movement-related effects in fMRI time-series. Magn. Reson. Med. 35(3), 346–355 (1996)

    Article  Google Scholar 

  9. Gilbert, C.D., Sigman, M.: Brain states: top-down influences in sensory processing. Neuron 54(5), 677–696 (2007)

    Article  Google Scholar 

  10. Leonardi, N., Van De Ville, D.: On spurious and real fluctuations of dynamic functional connectivity during rest. NeuroImage 104, 430–436 (2015)

    Article  Google Scholar 

  11. Li, X., Lim, C., Li, K., Guo, L., Liu, T.: Detecting brain state changes via fiber-centered functional connectivity analysis. Neuroinformatics 11(2), 193–210 (2013)

    Article  Google Scholar 

  12. Lindquist, M.A., Xu, Y., Nebel, M.B., Caffo, B.S.: Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach. NeuroImage 101, 531–546 (2014)

    Article  Google Scholar 

  13. Nielsen, J.A., Zielinski, B.A., Fletcher, P.T., Alexander, A.L., Lange, N., Bigler, E.D., Lainhart, J.E., Anderson, J.S.: Multisite functional connectivity MRI classification of autism: Abide results. Front. Hum. Neurosci. 7, 599 (2013)

    Article  Google Scholar 

  14. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)

    Article  Google Scholar 

  15. Ryali, S., Supekar, K., Chen, T., Kochalka, J., Cai, W., Nicholas, J., Padmanabhan, A., Menon, V.: 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)

    Article  Google Scholar 

  16. Smith, S.M., Miller, K.L., Moeller, S., Xu, J., Auerbach, E.J., Woolrich, M.W., Beckmann, C.F., Jenkinson, M., Andersson, J., Glasser, M.F., et al.: Temporally-independent functional modes of spontaneous brain activity. Proc. Natl. Acad. Sci. 109(8), 3131–3136 (2012)

    Article  Google Scholar 

  17. Suk, H.I., Wee, C.Y., Lee, S.W., Shen, D.: State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129(1), 292–307 (2016)

    Article  Google Scholar 

  18. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1), 273–289 (2002)

    Article  Google Scholar 

  19. Washington, S.D., Gordon, E.M., Brar, J., Warburton, S., Sawyer, A.T., Wolfe, A., Mease-Ference, E.R., Girton, L., Hailu, A., Mbwana, J., et al.: Dysmaturation of the default mode network in autism. Hum. Brain Mapp. 35(4), 1284–1296 (2014)

    Article  Google Scholar 

  20. Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 1–58 (2006)

    Google Scholar 

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Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1C1A1A01052216) and also partially supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (No. 2017-0-00451).

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Correspondence to Heung-Il Suk .

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Jun, E., Suk, HI. (2017). Region-Wise Stochastic Pattern Modeling for Autism Spectrum Disorder Identification and Temporal Dynamics Analysis. In: Wu, G., Laurienti, P., Bonilha, L., Munsell, B. (eds) Connectomics in NeuroImaging. CNI 2017. Lecture Notes in Computer Science(), vol 10511. Springer, Cham. https://doi.org/10.1007/978-3-319-67159-8_17

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  • DOI: https://doi.org/10.1007/978-3-319-67159-8_17

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-67159-8

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