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.
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References
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)
Chang, C., Glover, G.H.: Time–frequency dynamics of resting-state brain connectivity measured with fmri. NeuroImage 50(1), 81–98 (2010)
Chao-Gan, Y., Yu-Feng, Z.: Dparsf: a matlab toolbox for pipeline data analysis of resting-state fmri. Front. Sys. Neurosci. 4 (2010)
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)
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)
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)
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)
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)
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)
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)
Kennedy, D.P., Adolphs, R.: The social brain in psychiatric and neurological disorders. Trends. Cogn. Sci. 16(11), 559–572 (2012)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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Price, T., Wee, CY., Gao, W., Shen, D. (2014). Multiple-Network Classification of Childhood Autism Using Functional Connectivity Dynamics. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8675. Springer, Cham. https://doi.org/10.1007/978-3-319-10443-0_23
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DOI: https://doi.org/10.1007/978-3-319-10443-0_23
Publisher Name: Springer, Cham
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