Temporally Dynamic Resting-State Functional Connectivity Networks for Early MCI Identification
Resting-state functional Magnetic Resonance Imaging (R-fMRI) scan provides a rich characterization of the dynamic changes or temporal variabilities caused by neural interactions that may happen within the scan duration. Multiple functional connectivity networks can be estimated from R-fMRI time series to effectively capture subtle yet short neural connectivity changes induced by disease pathologies. To effectively extract the temporally dynamic information, we utilize a sliding window approach to generate multiple shorter, yet overlapping sub-series from a full R-fMRI time series. Whole-brain sliding window correlations are computed based on these sub-series to generate a series of temporal networks, characterize the neural interactions between brain regions at different time scales. Individual estimation of these temporal networks overlooks the intrinsic temporal smoothness between successive overlapping R-fMRI sub-series. To handle this problem, we suggest to jointly estimate temporal networks by maximizing a penalized log likelihood via a fused lasso regularization: 1) l1-norm penalty ensures a sparse solution; 2) fused regularization preserves the temporal smoothness while allows correlation variability. The estimated temporal networks were applied for early Mild Cognitive Impairment (eMCI) identification, and our results demonstrate the importance of including temporally dynamic R-fMRI scan information for accurate diagnosis of eMCI.
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- 3.Danaher, P., Wang, P., Witten, D.M.: The joint graphical lasso for inverse covariance estimation across multiple classes. Arxiv preprint arXiv:1111.0324 (2012)Google Scholar
- 4.Fennema-Notestine, C., Hagler Jr., D.J., McEvoy, L.K., Fleisher, A.S., Wu, E.H., Karow, D.S., Dale, A.M., Alzheimer’s Disease Neuroimaging Initiative: Structural MRI biomarkers for preclinical and mild Alzheimer’s disease. Hum. Brain Mapp. 30(10), 3238–3253 (2009)Google Scholar
- 8.Sheline, Y.I., Raichle, M.E.: Resting state functional connectivity in preclinical Alzheimer’s disease. Biol. Psychiatry (in press, 2013)Google Scholar
- 9.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., Van Essen, D.C., Feinberg, D.A., Yacoub, E.S., Ugurbil, K.: Temporally-independent functional modes of spontaneous brain activity. Proc. Natl. Acad. Sci. U. S. A. 109(8), 3131 (2012)CrossRefGoogle Scholar
- 12.Supekar, K., Menon, V., Rubin, D., Musen, M., Greicius, M.D.: Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput. Biol. 4, e1000100 (2008)Google Scholar
- 14.Whitwell, J.L., Przybelski, S.A., Weigand, S.D., Knopman, D.S., Boeve, B.F., Petersen, R.C., Jack Jr., C.R.: 3D maps from multiple mri illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer’s disease. Brain 130(7), 1777–1786 (2007)CrossRefGoogle Scholar
- 15.Yang, S., Pan, Z., Shen, X., Wonka, P., Ye, J.: Fused multiple graphical lasso. Arxiv preprint arXiv:1209.2139 (2012)Google Scholar