Unsupervised Learning of Functional Network Dynamics in Resting State fMRI
Research in recent years has provided some evidence of temporal non-stationarity of functional connectivity in resting state fMRI. In this paper, we present a novel methodology that can decode connectivity dynamics into a temporal sequence of hidden network “states” for each subject, using a Hidden Markov Modeling (HMM) framework. Each state is characterized by a unique covariance matrix or whole-brain network. Our model generates these covariance matrices from a common but unknown set of sparse basis networks, which capture the range of functional activity co-variations of regions of interest (ROIs). Distinct hidden states arise due to a variation in the strengths of these basis networks. Thus, our generative model combines a HMM framework with sparse basis learning of positive definite matrices. Results on simulated fMRI data show that our method can effectively recover underlying basis networks as well as hidden states. We apply this method on a normative dataset of resting state fMRI scans. Results indicate that the functional activity of a subject at any point during the scan is composed of combinations of overlapping task-positive/negative pairs of networks as revealed by our basis. Distinct hidden temporal states are produced due to a different set of basis networks dominating the covariance pattern in each state.
Keywordsresting state fMRI functional connectivity temporal network dynamics
Unable to display preview. Download preview PDF.
- 5.Hutchison, R.M., Womelsdorf, T., Gati, J.S., Everling, S., Menon, R.S.: Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Human Brain Mapping (2012)Google Scholar
- 7.Sra, S., Cherian, A.: Generalized dictionary learning for symmetric positive definite matrices with application to nearest neighbor retrieval. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS (LNAI), vol. 6913, pp. 318–332. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 8.Sivalingam, R., Boley, D., Morellas, V., Papanikolopoulos, N.: Positive definite dictionary learning for region covariances. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1013–1019. IEEE (2011)Google Scholar
- 9.Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The Annals of Mathematical Statistics, 164–171 (1970)Google Scholar
- 15.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. Proceedings of the National Academy of Sciences 109(8), 3131–3136 (2012)CrossRefGoogle Scholar