TY - JOUR
T1 - Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI
AU - Zhou, Zhongxing
AU - Cai, Biao
AU - Zhang, Gemeng
AU - Zhang, Aiying
AU - Calhoun, Vince D.
AU - Wang, Yu Ping
N1 - Funding Information:
The authors would like to thank the partial support by NIH ( P20 GM109068 , R01 MH104680 , R01 MH107354 , R01 MH103220 ) and NSF ( #1539067 ). Data are provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657 ) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research ; and by the McDonnell Center for Systems Neuroscience at Washington University.
Publisher Copyright:
© 2020
PY - 2020/11/1
Y1 - 2020/11/1
N2 - Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.
AB - Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.
KW - Dynamic functional network connectivity
KW - Multivariate empirical mode decomposition
KW - Phase locking value
KW - Resting state fMRI
UR - http://www.scopus.com/inward/record.url?scp=85088803641&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85088803641&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2020.117190
DO - 10.1016/j.neuroimage.2020.117190
M3 - Article
C2 - 32711063
AN - SCOPUS:85088803641
SN - 1053-8119
VL - 221
JO - NeuroImage
JF - NeuroImage
M1 - 117190
ER -