The Switching Rates of Dynamic Functional Networks Differently Contribute to Cross-Sectional and Longitudinal Cognition in Mild Cognitive Impairment.

Abstract

The relationship between switching rate of multilayer functional network and cognitive ability in mild cognitive impairment (MCI) and Alzheimers' disease remains unclear. We followed up MCI patients for one year and analyzed the association of switching rates with cognitive decline. The iterative and ordinal Louvain algorithm tracked the switching of functional networks, while elastic network regression and Bayesian belief networks were used to test the relationship between network switching rate and cognitive performance cross-sectionally and longitudinally. The switching rate of the default mode network positively correlated with better cognitive function, while that of salience and executive control network was negatively associated with memory and executive function. The lower default mode network (DMN) switching rate predicted MCI progression to dementia, while the lower sensorimotor network switching rate heralded in slower cognitive decline. The present study investigated the predictive effect of switching rate on cognitive performance, as well as MCI progression to dementia. The inverse effect from different functional networks may become useful for early diagnosis and revealing the mechanism of neural networks in cognitive decline.