TY - JOUR
T1 - Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA
AU - Bhinge, Suchita
AU - Mowakeaa, Rami
AU - Calhoun, Vince D.
AU - Adali, Tülay
N1 - Funding Information:
Manuscript received October 23, 2018; revised December 28, 2018 and January 6, 2019; accepted January 10, 2019. Date of publication January 23, 2019; date of current version June 28, 2019. This work was supported in part by the National Institute of Biomedical Imaging and Bioengineering under Grant R01 EB 020407, in part by the National Science Foundation under Grant 1631838, and in part by the National Science Foundation-Computing and Communication Foundations under Grant 1618551. (Corresponding author: Suchita Bhinge.) S. Bhinge, R. Mowakeaa, and T. Adalı are with the Department of Electrical and Computer Engineering, University of Maryland, Baltimore, MD 21250 USA (e-mail: suchita1@umbc.edu).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 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 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 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.
AB - 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 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 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.
KW - Blind source separation
KW - connectivity analysis
KW - dimensionality reduction
KW - fMRI analysis
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U2 - 10.1109/TMI.2019.2893651
DO - 10.1109/TMI.2019.2893651
M3 - Article
C2 - 30676948
AN - SCOPUS:85068485050
SN - 0278-0062
VL - 38
SP - 1715
EP - 1725
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 7
M1 - 8624617
ER -