PMID- 34783122
OWN - NLM
STAT- MEDLINE
DCOM- 20220328
LR  - 20220328
IS  - 1097-0193 (Electronic)
IS  - 1065-9471 (Print)
IS  - 1065-9471 (Linking)
VI  - 43
IP  - 3
DP  - 2022 Feb 15
TI  - Unveiling whole-brain dynamics in normal aging through Hidden Markov Models.
PG  - 1129-1144
LID - 10.1002/hbm.25714 [doi]
AB  - 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.
CI  - (c) 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
FAU - Moretto, Manuela
AU  - Moretto M
AUID- ORCID: 0000-0003-4178-1854
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
AD  - Padova Neuroscience Center, University of Padova, Padova, Italy.
FAU - Silvestri, Erica
AU  - Silvestri E
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
AD  - Padova Neuroscience Center, University of Padova, Padova, Italy.
FAU - Zangrossi, Andrea
AU  - Zangrossi A
AD  - Padova Neuroscience Center, University of Padova, Padova, Italy.
FAU - Corbetta, Maurizio
AU  - Corbetta M
AD  - Padova Neuroscience Center, University of Padova, Padova, Italy.
AD  - Department of Neuroscience, University of Padova, Padova, Italy.
FAU - Bertoldo, Alessandra
AU  - Bertoldo A
AUID- ORCID: 0000-0002-6262-6354
AD  - Department of Information Engineering, University of Padova, Padova, Italy.
AD  - Padova Neuroscience Center, University of Padova, Padova, Italy.
LA  - eng
PT  - Journal Article
DEP - 20211115
PL  - United States
TA  - Hum Brain Mapp
JT  - Human brain mapping
JID - 9419065
SB  - IM
MH  - Adult
MH  - Aged
MH  - Aged, 80 and over
MH  - Aging/*physiology
MH  - Brain/diagnostic imaging/*physiology
MH  - *Connectome
MH  - Female
MH  - Humans
MH  - Magnetic Resonance Imaging
MH  - Male
MH  - Markov Chains
MH  - Middle Aged
MH  - *Models, Statistical
MH  - Nerve Net/diagnostic imaging/*physiology
MH  - Young Adult
PMC - PMC8764474
OTO - NOTNLM
OT  - Hidden Markov Models
OT  - brain states
OT  - dynamic functional connectivity
OT  - healthy aging
OT  - resting state networks
EDAT- 2021/11/17 06:00
MHDA- 2022/03/29 06:00
PMCR- 2021/11/15
CRDT- 2021/11/16 07:03
PHST- 2021/10/24 00:00 [revised]
PHST- 2021/08/05 00:00 [received]
PHST- 2021/10/31 00:00 [accepted]
PHST- 2021/11/17 06:00 [pubmed]
PHST- 2022/03/29 06:00 [medline]
PHST- 2021/11/16 07:03 [entrez]
PHST- 2021/11/15 00:00 [pmc-release]
AID - HBM25714 [pii]
AID - 10.1002/hbm.25714 [doi]
PST - ppublish
SO  - Hum Brain Mapp. 2022 Feb 15;43(3):1129-1144. doi: 10.1002/hbm.25714. Epub 2021 
      Nov 15.