Abstract
Normal dynamic change in human brain occurs with age increasing, yet much remains unknown regarding how brain develops, matures, and ages. Functional connectivity analysis of the resting-state brain is a powerful method for revealing the intrinsic features of functional networks, and micro-states, which are the intrinsic patterns of functional connectivity in dynamic network courses, and are suggested to be more informative of brain functional changes. The aim of this study is to explore the age-related changes in these micro-states of dynamic functional network. Three healthy groups were included: the young (ages 21–32 years), the adult (age 41–54 years), and the old (age 60–86 years). Sliding window correlation method was used to construct the dynamic connectivity networks, and then the micro-states were individually identified with clustering analysis. The distribution of age-related connectivity variations in several intrinsic networks for each micro-state was analyzed then. The micro-states showed substantial age-related changes in the transitions between states but not in the dwelling time. Also there was no age-related reorganization observed within any micro-state. But there were reorganizations observed in the transition between them. These results suggested that the identified micro-states represented certain underlying connectivity patterns in functional brain system, which are similar to the intrinsic cognitive networks or resources. In addition, the dynamic transitions between these states were probable mechanisms of reorganization or compensation in functional brain networks with age increasing.
Highlights
Normalbrain aging refers to the degradative phenomena that occurs in brain structure, function and morphology with age increasing and manifests as a certain degree of brain dysfunction in elderly populations (Hedden and Gabrieli, 2004; Whalley et al, 2004)
Research has shown temporal dynamics of functional connectivity in resting state (Chang and Glover, 2010; Hutchison et al, 2013a; Feng et al, 2015; Leonardi and Van De Ville, 2015). This kind of dynamic functional connectivity, which varies over a matter of seconds, may be highly related to unconstrained mental activities (Hutchison et al, 2013b; Allen et al, 2014; Zalesky et al, 2014), as well as to neurologic diseases (Kaiser et al, 2015; Mayer et al, 2015; Braun et al, 2016; Wee et al, 2016)
The time-varying functional connectivity derived from sliding window correlation, which reflects the dynamics of functional brain networks, is expected to facilitate our understanding of the mechanisms of aging process
Summary
Normalbrain aging refers to the degradative phenomena that occurs in brain structure, function and morphology with age increasing and manifests as a certain degree of brain dysfunction in elderly populations (Hedden and Gabrieli, 2004; Whalley et al, 2004). Research has shown temporal dynamics of functional connectivity in resting state (Chang and Glover, 2010; Hutchison et al, 2013a; Feng et al, 2015; Leonardi and Van De Ville, 2015) This kind of dynamic functional connectivity, which varies over a matter of seconds, may be highly related to unconstrained mental activities (Hutchison et al, 2013b; Allen et al, 2014; Zalesky et al, 2014), as well as to neurologic diseases (Kaiser et al, 2015; Mayer et al, 2015; Braun et al, 2016; Wee et al, 2016). These indexes of dynamic functional connectivity were time-averaged features that could not capture the connectivity patterns of the spontaneous fluctuations
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