Unveiling whole‐brain dynamics in normal aging through Hidden Markov Models

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.

A Hidden Markov Model reveals magnetoencephalography spectral frequency-specific abnormalities of brain state power and phase-coupling in neuropathic pain

“…They correspond to commonly observed functional resting-state networks (at the spatial level within the DPC 4 , 6 ) including the DMN, SMN, visual, frontoparietal, attentional, and ANP. The spatial maps primarily showed patterns that are in line with the previous work 32 , 33 , 50 that examined the large-scale UK Biobank data. This provides confidence in the generalizability of the HMM in our study which had a modest sample size.…”

Long-term stress shapes dynamic reconfiguration of functional brain networks across multi-task demands

“…The number of states was decided according to the free energy (80). Previous HMM studies had also confirmed that 6-12 states were appropriate for describing brain function temporal dynamics (43,45,(81)(82)(83). Beyond the group-level fractional occupancy, mean lifetime, and transition probability, we calculated each of these three parameters for each participant during each task (see HMM-MAR wiki).…”

The dynamic functional connectivity fingerprint of high-grade gliomas

“…The choice of the model order was performed as in 30 . In brief, after tting the model with the number of states ranging from 2 to 15, different indices were evaluated: the free-energy and the average loglikelihood (avLL), as indices of goodness of the t and the coe cients of variation (CVs) to quantify the precision of the estimates.…”

“…A Wilcoxon's rank sum test followed by multiple comparison correction (FDR, α = .05) was applied to test for signi cant differences between states graph metrics. Finally, we applied the Louvain's community detection algorithm 63 on the thresholded FCs matrices, exploiting the framework described in 30 . Once obtained the organisation of RSNs in communities for each state, we computed the Jaccard index to evaluate the similarity between states modular organisation.…”

“…In recent years HMM has been employed to assess dynamic FC in both healthy [29][30][31] and diseased brain [32][33][34] , providing evidence that in clinical populations, such as schizophrenia 34 or multiple sclerosis patients 32 , dynamic descriptors of brain states accurately predict symptoms severity 34 or strongly relate to medications 32 . Despite these works, to the best of our knowledge, studies on functional dynamics in brain tumours are still missing in the literature.…”