A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI.

Background: There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods.

New Methods: We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation.

Results: The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data.

Comparison With Existing Methods: Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC.

Conclusion: We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.