title: A sticky weighted regression model for time-varying resting-state brain connectivity estimation.
abstract: Despite recent progress on brain connectivity modeling using neuroimaging data such as fMRI, most current approaches assume that brain connectivity networks have time-invariant topology/coefficients. This is clearly problematic as the brain is inherently nonstationary. Here, we present a time-varying model to investigate the temporal dynamics of brain connectivity networks. The proposed method allows for abrupt changes in network structure via a fused least absolute shrinkage and selection operator (LASSO) scheme, as well as recovery of time-varying networks with smoothly changing coefficients via a weighted regression technique. Simulations demonstrate that the proposed method yields improved accuracy on estimating time-dependent connectivity patterns when compared to a static sparse regression model or a weighted time-varying regression model. When applied to real resting-state fMRI datasets from Parkinson's disease (PD) and control subjects, significantly different temporal and spatial patterns were found to be associated with PD. Specifically, PD subjects demonstrated reduced network variability over time, which may be related to impaired cognitive flexibility previously reported in PD. The temporal dynamic properties of brain connectivity in PD subjects may provide insights into brain dynamics associated with PD and may serve as a potential biomarker in future studies.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
authors.forename: Aiping
authors.initials: A
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, School of Medical Engineering, Hefei University of Technology, Hefei, China.
authors.forename: Xun
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Medicine (Neurology) and Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada.
authors.forename: Martin J
authors.initials: MJ
authors.lastname: McKeown
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
authors.forename: Z Jane
authors.initials: ZJ
authors.lastname: Wang
mesh_terms: D001921:Brain / Q000503:physiopathology*; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D009415:Nerve Net / Q000503:physiopathology*; D010300:Parkinson Disease / Q000503:physiopathology*; D015203:Reproducibility of Results; D012146:Rest / Q000502:physiology; D012680:Sensitivity and Specificity