Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models. Issue 3 (15th November 2021)

• Record Type: Journal Article
• Authors: Moretto, Manuela; Silvestri, Erica; Zangrossi, Andrea; Corbetta, Maurizio; Bertoldo, Alessandra
• Is Part Of: Human brain mapping. Volume 43:Issue 3(2022)
• Page Start: 1129
• Page End: 1144
• Publication Date: 2021-11-15
• Subjects: brain states -- dynamic functional connectivity -- healthy aging -- Hidden Markov Models -- resting state networks; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25714
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: During normal aging, the brain undergoes structural and functional changes. Many studies applied static functional connectivity (FC) analysis on resting state functional magnetic resonance imaging (rs‐fMRI) data showing a link between aging and the increase of between‐networks connectivity. However, it has been demonstrated that FC is not static but varies over time. By employing the dynamic data‐driven approach of Hidden Markov Models, this study aims to investigate how aging is related to specific characteristics of dynamic brain states. Rs‐fMRI data of 88 subjects, equally distributed in young and old were analyzed. The best model resulted to be with six states, which we characterized not only in terms of FC and mean BOLD activation, but also uncertainty of the estimates. We found two states were mostly occupied by young subjects, whereas three other states by old subjects. A graph‐based analysis revealed a decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects. Indeed, while young subjects tend to cycle in a loop of states characterized by a high segregation of the networks, old subjects' loops feature high integration, with a crucial intermediary role played by the dorsal attention network. These results suggest that the employed mathematical approach captures the complex and rich brain's dynamics underpinning the aging process. Abstract : Normal aging is associated with brain structural and functional changes. In this study, we applied the dynamic data‐driven approach of Hidden Markov Models on resting state functional MRI data of 88 subjects, equally distributed in young and old, to investigate how aging is related to specific characteristics of dynamic brain states. After estimating six states, we found that two states were mostly occupied by young subjects, whereas three other states by old subjects. A decrease in strength with the increase of age, and an overall more integrated topology of states occupied by old subjects were also observed.