Resting-state functional connectivity dynamics in healthy aging: an approach through Network Change Point Detection.

The present study aims to assess the impact of age on the short-term temporal dynamics of the topological properties of the undirected and weighted whole-brain functional connectivity (FC) networks. We studied the association between the participant's age and the number of significant change points detected through Network Change Point Detection (NCPD) algorithm. Secondary, we defined as state the rs-fMRI subsequence between two significant change points, obtaining the FC network in each state and participant and characterized their network topological properties. The data comprises the rs-fMRI sequences of 114 healthy individuals combined from three different studies conducted at the Department of Medicine, School of Medicine and Health Sciences, University of Barcelona. Participants were healthy people in the absence of any pathology that could interfere with the scanning procedures, as well as any chronic illness that implied a short-lived situation. Topological properties of everyone's FC networks were characterized by their network strength, transitivity, characteristic path length and small-worldness, analysing the effect of age in those observed distributions. To that effect, we constructed a mixed linear model for each network topological property with age, state and state duration in the linear predictor. Several statistically significant relationships have been estimated between the indicators of the FC networks that show a certain regular pattern of change in the networks during the time of registration at resting fMRI paradigm. These dynamic changes seem to be related to the age of each group studied. Healthy aging could be characterized by FC dynamics patterns.