2018/08/29 by Iyer, Aditi, Tang, Bingjing, Rao, Vinayak +1
#Computation (stat.CO) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (stat.ML) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.1809.01046
We propose a novel two-phase approach to functional network estimation of multi-subject functional Magnetic Resonance Imaging (fMRI) data, which applies model-based image segmentation to determine a group-representative connectivity map. In our approach, we first improve clustering-based Independent Component Analysis (ICA) to generate maps of components occurring consistently across subjects, and then estimate the group-representative map through MAP-MRF (Maximum a priori - Markov random field) labeling. For the latter, we provide a novel and efficient variational Bayes algorithm. We study the performance of the proposed method using synthesized data following a theoretical model, and demonstrate its viability in blind extraction of group-representative functional networks using simulated fMRI data. We anticipate the proposed method will be applied in identifying common neuronal characteristics in a population, and could be further extended to real-world clinical diagnosis.