Extraction of time-varying spatio-temporal networks using parameter-tuned constrained IVA
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Date
2019-07
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Department of Computer Science and Electrical Engineering
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Citation of Original Publication
S. Bhinge, R. Mowakeaa, V. D. Calhoun and T. Adalı, "Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA," in IEEE Transactions on Medical Imaging, vol. 38, no. 7, pp. 1715-1725, July 2019. doi: 10.1109/TMI.2019.2893651 keywords: {Functional magnetic resonance imaging;Feature extraction;Estimation;Data models;Adaptation models;Blind source separation;Independent component analysis;Blind source separation;connectivity analysis;dimensionality reduction;fMRI analysis}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8624617&isnumber=8750971
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©2019 IEEE
©2019 IEEE
Abstract
Dynamic functional connectivity (dFC) 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 spatio-temporal networks. Independent vector analysis 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 effective two-stage method to extract time-varying spatial and temporal features using IVA, mitigating the problems with higher number of datasets while preserving the variability across subjects and time. The first stage is used to extract reference signals using group independent component analysis (GICA) that are used in a parameter-tuned constrained IVA (pt-cIVA) framework to estimate time-varying representations of these signals by preserving the variability through tuning the constraint parameter. This approach effectively captures variability across time from a large-scale resting-state fMRI data acquired from healthy controls and patients with schizophrenia and identifies more functionally relevant connections that are significantly different among healthy controls and patients with schizophrenia, compared with the widely used GICA method alone.