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Functional dynamic network connectivity differentiates biological patterns in the Alzheimer's disease continuum
Lorenzo Pini1, Lorenza Brusini2, Alessandra Griffa3
1Padova Neuroscience Center, University of Padova, Via Giuseppe Orus 2/B, 35131 Padova, Italy; Department of Neuroscience, University of Padova, via Belzoni 160, 35121 Padova, Italy.
Neurobiology of Disease
|March 13, 2025
Summary
Alzheimer's disease is a network syndrome. Brain connectivity patterns reveal distinct biological profiles in patients, aiding in identifying diverse Alzheimer's disease pathologies.
Area of Science:
- Neuroscience
- Medical Imaging
- Molecular Biology
Background:
- Alzheimer's disease (AD) is increasingly viewed as a network-based syndrome.
- Network abnormalities correlate with molecular hallmarks like amyloid-beta and tau accumulation, leading to neurodegeneration.
- Understanding the interplay between molecular pathology and brain network dynamics is crucial for AD diagnosis.
Purpose of the Study:
- To investigate if distinct biological patterns of Alzheimer's disease (AD) align with specific abnormalities in brain dynamic connectivity.
- To combine molecular data with resting-state functional magnetic resonance imaging (rs-fMRI) to identify patient clusters.
- To explore the relationship between macro-scale network alterations and molecular pathology in AD.
Main Methods:
- Utilized a data-driven approach to identify patient clusters based on biological patterns.
- Employed resting-state functional magnetic resonance imaging (rs-fMRI) to assess brain dynamic connectivity.
- Combined rs-fMRI data with molecular markers (amyloid-beta, tau, neurodegeneration) for comprehensive analysis.
Main Results:
- Identified three distinct patient clusters with varying degrees of pathological alterations and clinical presentations.
- Discovered two key connectivity patterns within the default mode network and occipito-temporal cortex, associated with typical and atypical AD profiles.
- Found that dynamic connectivity markers can differentiate AD patients with similar clinical profiles but divergent underlying pathologies.
Conclusions:
- Macro-scale brain network alterations are fundamentally linked to molecular changes in Alzheimer's disease.
- Dynamic functional connectivity analysis offers a valuable tool for identifying AD patient subgroups with distinct pathological underpinnings.
- This approach can aid in personalized diagnostics and therapeutic strategies for Alzheimer's disease.
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