Differences in dynamic functional connectivity across the Alzheimer’s disease spectrum

Background To examine differences in dynamic functional connectivity (DFC) in cognitively normal subjects (CN), and patients with mild cognitive impairment (MCI) or Alzheimer’s disease (AD). Method Resting‐state functional magnetic resonance imaging (rsfMRI data of 76 participants (40 CN, 24 MCI, 11...

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Published inAlzheimer's & dementia Vol. 19; no. S3
Main Authors Pfeil, Julia, Hönig, Merle C., Drzezga, Alexander, van Eimeren, Thilo
Format Journal Article
LanguageEnglish
Published 01.06.2023
Online AccessGet full text
ISSN1552-5260
1552-5279
DOI10.1002/alz.063240

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Abstract Background To examine differences in dynamic functional connectivity (DFC) in cognitively normal subjects (CN), and patients with mild cognitive impairment (MCI) or Alzheimer’s disease (AD). Method Resting‐state functional magnetic resonance imaging (rsfMRI data of 76 participants (40 CN, 24 MCI, 11 AD) from the ADNI database (http://adni.loni.usc.edu/) was used for analysis. Groups were matched according to age, sex and education. rsfMRI data were pre‐processed using the CONN toolbox. For data reduction purposes, spatial independent and principal component analyses were performed on the normalized rsfMRI using GIFT. Resulting meaningful independent components were clustered into 13 functional resting‐state networks. Using GIFT, a sliding window approach was employed to determine dynamic functional connectivity states across the scan period of 9 minutes. Afterwards, non‐parametric t‐tests were performed comparing the groups in measures of DFC such as dwell time (i.e. staying in a state), number of transitions (i.e. switching between states), fraction time (i.e. total time spent in a state), and meta state characteristics. Result The DFC analysis resulted in 4 distinct functional connectivity states, which were characterized by either global connectivity patterns or distinct connectivity patterns between certain rs‐networks. Group comparisons yielded that one state was particularly more occupied by the AD group compared to the CN group (p = .02) and the MCI group (p = .001). The AD group showed a lower meta state span and number of meta states compares to the MCI and CN groups. Conclusion Network reorganization may influence dynamic functional connectivity patterns in pre‐ and clinical AD.
AbstractList Background To examine differences in dynamic functional connectivity (DFC) in cognitively normal subjects (CN), and patients with mild cognitive impairment (MCI) or Alzheimer’s disease (AD). Method Resting‐state functional magnetic resonance imaging (rsfMRI data of 76 participants (40 CN, 24 MCI, 11 AD) from the ADNI database (http://adni.loni.usc.edu/) was used for analysis. Groups were matched according to age, sex and education. rsfMRI data were pre‐processed using the CONN toolbox. For data reduction purposes, spatial independent and principal component analyses were performed on the normalized rsfMRI using GIFT. Resulting meaningful independent components were clustered into 13 functional resting‐state networks. Using GIFT, a sliding window approach was employed to determine dynamic functional connectivity states across the scan period of 9 minutes. Afterwards, non‐parametric t‐tests were performed comparing the groups in measures of DFC such as dwell time (i.e. staying in a state), number of transitions (i.e. switching between states), fraction time (i.e. total time spent in a state), and meta state characteristics. Result The DFC analysis resulted in 4 distinct functional connectivity states, which were characterized by either global connectivity patterns or distinct connectivity patterns between certain rs‐networks. Group comparisons yielded that one state was particularly more occupied by the AD group compared to the CN group (p = .02) and the MCI group (p = .001). The AD group showed a lower meta state span and number of meta states compares to the MCI and CN groups. Conclusion Network reorganization may influence dynamic functional connectivity patterns in pre‐ and clinical AD.
Author Pfeil, Julia
Hönig, Merle C.
Drzezga, Alexander
van Eimeren, Thilo
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  organization: University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Nuclear Medicine, Cologne
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  givenname: Merle C.
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  givenname: Thilo
  surname: van Eimeren
  fullname: van Eimeren, Thilo
  organization: German Center for Neurodegenerative Diseases (DZNE), Bonn/Cologne
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