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Recurrence quantification analysis of rs-fMRI data: A method to detect subtle changes in the TgF344-AD rat model.

Arash RezaeiMonica van den BergHajar MirlohiMarleen VerhoyeMahmood AmiriGeorgios A Keliris
Published in: Computer methods and programs in biomedicine (2024)
The results of this study demonstrate that RQA of rs-fMRI data is a potent approach that can detect subtle changes which might be missed by other methodologies due to the brain's non-linear dynamics. Moreover, this study provides helpful information about specific areas involved in AD pathology at very early stages of the disease in a very promising rat model of AD. Our results provide valuable information for the development of early detection methods and novel diagnosis tools for AD.
Keyphrases
  • resting state
  • functional connectivity
  • electronic health record
  • healthcare
  • health information
  • signaling pathway