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Recurrence quantification analysis of rs-fMRI data: A method to detect subtle changes in the TgF344-AD rat model
Arash Rezaei1, Monica van den Berg2, Hajar Mirlohi1
1Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Computer Methods and Programs in Biomedicine
|September 11, 2024
Summary
Recurrence Quantification Analysis (RQA) on resting-state fMRI data detected early Alzheimer's disease (AD) changes in rat brain networks. This method reveals subtle neuropathological effects, aiding in the development of early AD detection tools.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Medical Imaging
Background:
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia worldwide.
- The preclinical stage of AD can last over a decade, highlighting the need for early detection methods.
- Understanding early neuropathological effects on brain function is crucial for timely intervention.
Purpose of the Study:
- To investigate early neuropathological changes in Alzheimer's disease using resting-state functional magnetic resonance imaging (rs-fMRI).
- To apply Recurrence Quantification Analysis (RQA) to identify alterations in brain network dynamics associated with the AD phenotype and aging.
- To pinpoint specific brain regions within the default mode-like network (DMLN) affected at preclinical stages of AD.
Main Methods:
- rs-fMRI data acquired from TgF344-AD rats and wild-type (WT) littermates at 4 and 6 months of age.
- Recurrence Quantification Analysis (RQA) applied to resting-state fMRI data, focusing on the default mode-like network (DMLN).
- Recurrence Plots (RP) used to represent and analyze the dynamical system of brain activity over time to identify affected DMLN regions.
Main Results:
- Significant AD-related changes were identified in multiple DMLN regions (e.g., Hippocampal fields CA1 and CA3, V1, V2) in 4- and 6-month-old rats.
- Aging WT rats exhibited decreased predictability in brain activity, whereas AD rats showed a reduced decline in predictability.
- RQA successfully identified subtle alterations in brain network dynamics indicative of the AD phenotype at early stages.
Conclusions:
- RQA of rs-fMRI data is a powerful method for detecting subtle, non-linear changes in brain dynamics missed by other techniques.
- This study provides valuable insights into specific brain regions affected by AD pathology in a rat model at very early stages.
- The findings support the development of novel diagnostic tools and early detection methods for Alzheimer's disease.
