What is it about?
This is an exploratory study aimed to quantitatively investigate functional connectivity patterns and graph theory metrics (global-efficiency, local-efficiency, betweenness-centrality, and clustering-coefficient) in 8 resting-state brain networks (default mode network, sensorimotor network, visual network, salience network, dorsal attention network, frontoparietal network, language network and cerebellar network) to differentiate Amnestic Mild Cognitive Impairment, late-onset Alzheimer's disease and Normal subjects.
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Why is it important?
We reported resting state connectivity changes in late-onset Alzheimer’s disease and amnestic MCI compared to healthy people. There have been limited imaging studies characterizing imaging traits between these three groups. Most studies have investigated functional connectivity in the default mode network. The current exploratory study reports findings from ROI-based connectivity analyses and graph theory analyses in 8 resting state networks.
Perspectives
This paper has provided comprehensive information regarding changes in functional connectivity in the three groups, which can be helpful in other researchers' future studies on resting-state networks. As the graph-theoretic measures and the strength of connections in brain networks are examined, interesting quantitative information is provided.
Hamidreza Saligheh Rad
Tehran University of Medical Sciences
Read the Original
This page is a summary of: Quantitative Assessment of Resting‐State Functional Connectivity
MRI
to Differentiate Amnestic Mild Cognitive Impairment, Late‐Onset Alzheimer's Disease From Normal Subjects, Journal of Magnetic Resonance Imaging, October 2022, Wiley,
DOI: 10.1002/jmri.28469.
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