Age-Related Decline in the Variation of Dynamic Functional Connectivity: A Resting State Analysis

Abstract

Normal aging is typically characterized by abnormal resting-state functional connectivity (FC), including decreasing connectivity within networks and increasing connectivity between networks, under the assumption that the FC over the scan time was stationary. In fact, the resting-state FC has been shown in recent years to vary over time even within minutes, thus showing the great potential of intrinsic interactions and organization of the brain. In this article, we assumed that the dynamic FC consisted of an intrinsic dynamic balance in the resting brain and was altered with increasing age. Two groups of individuals (N = 36, ages 20–25 for the young group; N = 32, ages 60–85 for the senior group) were recruited from the public data of the Nathan Kline Institute. Phase randomization was first used to examine the reliability of the dynamic FC. Next, the variation in the dynamic FC and the energy ratio of the dynamic FC fluctuations within a higher frequency band were calculated and further checked for differences between groups by non-parametric permutation tests. The results robustly showed modularization of the dynamic FC variation, which declined with aging; moreover, the FC variation of the inter-network connections, which mainly consisted of the frontal-parietal network-associated and occipital-associated connections, decreased. In addition, a higher energy ratio in the higher FC fluctuation frequency band was observed in the senior group, which indicated the frequency interactions in the FC fluctuations. These results highly supported the basis of abnormality and compensation in the aging brain and might provide new insights into both aging and relevant compensatory mechanisms.

Dynamic Functional Connectivity between Order and Randomness and its Evolution across the Human Adult Lifespan

“…We found that this typical speed decreases with age, and this not only for empirical rest and task data but also for both considered surrogate types. Overall, our findings hint toward a reduced network variability in aging, in the same direction as other dFC analyses (Chen et al, 2017) and previous reports of reduced variability in elderly already at the level of the BOLD signal itself (Grady & Garrett 2014). Other studies, however, reported increased "noise" in the elderly relative to the younger subjects, at least at specific scales and in certain regions (Yang et al, 2013).…”

“…As we age, our brain undergoes characteristic structural and functional changes, with a tendency toward increased structural 'disconnection' (Salat, 2011), disruptions in rs-FC (Andrews-Hanna et al, 2007;Betzel et al, 2014) and modified structural-to-functional connectivity inter-relations (Zimmermann et al, 2016). Analogously, changes in dFC have been reported at the level of the temporal stability of FC network modules (Davison et al, 2016;Schlesinger et al, 2016), general or specific network variability (Qin et al, 2015;Chen et al, 2017), "FC state" occupancy (Hutchison & Morton, 2015;Viviano et al, 2017), and complexity of phase synchrony (Nobukawa et al, 2019). We complement these previous findings and show that dFC random walks may occur at an increasingly reduced speed and complexity with age, slowing down and becoming increasingly more "random".…”

“…To this heterogeneity of regional involvement, may correspond a parallel heterogeneity of dFC properties, which we fully ignore in this first study. It is known that variance of FC links in time (Chen et al, 2017) or even the fractal scaling properties of fMRI signals (Maxim et al, 2005;Churchill et al, 2016;Dong et al, 2018) are affected heterogeneously across brain regions. In a companion study (Lombardo et al, submitted jointly) we make a further step forward, probing variations of cognitive performance in selective attention and other cognitive functions, before and after sleep deprivation.…”

“…It may reflect the sampling of an internal repertoire of alternative dynamical states (Hansen et al, 2015;Golos et al, 2015;Cabral et al, 2017a;Glomb et al, 2017). From a biomarking perspective, pathological conditions, such as Alzheimer's dementia, schizophrenia and other mental disorders (Jones et al, 2012;Damaraju et al, 2014;Braun et al, 2018), or differences in general attributes like gender (Yaesoubi et al, 2015) or development and aging (Hutchison & Morton, 2015;Qin et al, 2015;Davison et al, 2016;Schlesinger et al, 2016;Chen et al, 2017;Viviano et al, 2017), may alter dFC more than they affect time-averaged FC.…”

Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

“…9 Whereas previous studies have primarily reported results from various estimates of static FC (sFC; the temporal correlation between two brain regions across the entire time-series), there is an increasing awareness of the relevance of dynamic FC (dFC; the variability in the temporal correlations across the time-series). 12,13 Interestingly, sFC and dFC capture distinct properties of brain network dynamics, 14,15 and may therefore provide complementary information in depression. 16 Here, in order to address symptom heterogeneity in depression, we used high dimensional data-driven clustering (HDDC) 17 based on item scores on the Beck's Depression (BDI-II) and Beck's Anxiety (BAI) inventories to identify groups of individuals with distinct symptom profiles among 1084 subjects with or without a history of a diagnosis of depression.…”