With advances in experimental therapeutics for Alzheimer's Disease (AD) the need for an accurate, non-invasive and widely available AD biomarker is more pressing than ever. Resting-state functional connectivity in default mode network is a candidate biomarker that is gaining traction in the field. However, the traditional stationary measurement of the default mode network connectivity cannot capture complicated dynamic patterns of functional connectivity that exist in the brain. Here, we have proposed a novel, reliable technique, based on Dynamic Condtional Correlation model, to quantify the dynamic functional connectivity in the brain and evaluated its sensitivity to cerebrospinal fluid biomarkers in AD.
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