Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA.

ABSTRACT: Dynamic functional connectivity analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective way for extracting spatiotemporal networks. Independent vector analysis (IVA) is a joint blind source separation technique that allows for estimation of spatial and temporal features while successfully preserving variability. However, its performance is affected for higher number of datasets. Hence, we develop an effecti

SUBMITTER: Bhinge S 

PROVIDER: S-EPMC7060979 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature