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Related Concept Videos

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

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Unveiling whole-brain dynamics in normal aging through Hidden Markov Models.

Manuela Moretto1,2, Erica Silvestri1,2, Andrea Zangrossi2

  • 1Department of Information Engineering, University of Padova, Padova, Italy.

Human Brain Mapping
|November 16, 2021
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Summary

Aging brains exhibit altered dynamic functional connectivity, shifting towards more integrated network states. This study reveals distinct brain states associated with aging using advanced computational models for better understanding brain changes.

Keywords:
Hidden Markov Modelsbrain statesdynamic functional connectivityhealthy agingresting state networks

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Gerontology

Background:

  • Normal aging involves significant brain structural and functional alterations.
  • Previous research linked aging to increased static functional connectivity (FC) between brain networks.
  • Emerging evidence highlights that brain FC is dynamic, not static, necessitating advanced analytical approaches.

Purpose of the Study:

  • To investigate the relationship between aging and specific characteristics of dynamic brain states.
  • To apply a data-driven dynamic approach, Hidden Markov Models (HMM), to resting-state functional magnetic resonance imaging (rs-fMRI) data.
  • To characterize age-related differences in brain network dynamics and topology.

Main Methods:

  • Analysis of rs-fMRI data from 88 subjects (young and old).
  • Application of Hidden Markov Models to identify distinct dynamic brain states.
  • Characterization of states using FC, mean BOLD activation, and estimation uncertainty.
  • Graph-based analysis to assess network topology and integration.

Main Results:

  • A six-state model best described the dynamic brain activity.
  • Younger subjects predominantly occupied two states, while older subjects occupied three distinct states.
  • Older adults showed decreased functional connectivity strength and a more integrated network topology.
  • Age-related changes included a shift from network segregation in young adults to network integration in older adults, with the dorsal attention network playing a key role.

Conclusions:

  • Dynamic functional connectivity analysis using HMM effectively captures age-related brain changes.
  • Aging is associated with a transition towards more integrated brain network states.
  • These findings provide novel insights into the complex neural dynamics underlying the aging process.