Identification of multi-scale hierarchical brain functional networks using deep matrix factorization
This work addresses the need for personalized brain network analysis in neuroscience, though it appears incremental as it builds on existing matrix factorization methods.
The authors tackled the problem of identifying subject-specific functional brain networks at multiple spatial scales with hierarchical organization from fMRI data, and demonstrated that their method's connectivity measures better predict subject-specific functional activations compared to alternative techniques.
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organization from resting state fMRI data. Our method is built upon a deep semi-nonnegative matrix factorization framework to jointly detect the FNs at multiple scales with a hierarchical organization, enhanced by group sparsity regularization that helps identify subject-specific FNs without loss of inter-subject comparability. The proposed method has been validated for predicting subject-specific functional activations based on functional connectivity measures of the hierarchical multi-scale FNs of the same subjects. Experimental results have demonstrated that our method could obtain subject-specific multi-scale hierarchical FNs and their functional connectivity measures across different scales could better predict subject-specific functional activations than those obtained by alternative techniques.