Morphological Component Analysis of Functional MRI Brain Networks

Objective:
Sparse representations have been utilized to identify functional connectivity (FC) of networks, while ICA employs the assumption of independence among the network sources to demonstrate FC. Here, we investigate a sparse decomposition method based on Morphological Component Analysis and K-SVD dictionary learning — MCA-KSVD — and contrast the effect of the sparsity constraint vs. the independency constraint on FC and denoising.
Methods:
Using a K-SVD algorithm, fMRI signals are decomposed into morphological components which have sparse spatial overlap. We present simulations when the independency assumption of ICA fails and MCA-KSVD recovers more accurate spatial-temporal structures. Denoising performance of both methods is investigated at various noise levels. A comprehensive experimental study was conducted on resting-state and task fMRI.
Results:
Validations show that ICA is advantageous when network components are well-separated and sparse. In such cases, the MCA-KSVD method has modest value over ICA in terms of network delineation but is significantly more effective in reducing spatial and temporal noise. Results demonstrate that the sparsity constraint yields sparser networks with higher spatial resolution while suppressing weak signals. Temporally, this localization effect yields higher contrast-to-noise ratios (CNRs) of time series.
Conclusion:
While marginally improving the spatial decomposition, MCA-KSVD denoises fMRI data much more effectively than ICA, preserving network structures and improving CNR, especially for weak networks.
Significance:
A sparsity-based decomposition approach may be useful for investigating functional connectivity in noisy cases. It may serve as an efficient decomposition method for reduced acquisition time and may prove useful for detecting weak network activations.