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Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
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Directed progression brain networks in Alzheimer's disease: properties and classification
Eric J Friedman1, Karl Young, Danial Asif
11 International Computer Science Institute , Berkeley, California.
Brain Connectivity
|June 6, 2014
Summary
This study introduces directed progression networks (DPNets) to map Alzheimer's disease (AD) spread in the brain. DPNets reveal new insights into AD progression and offer novel markers for differentiating normal cognition from AD.
Area of Science:
- Neuroscience
- Medical Imaging
- Network Science
Background:
- Alzheimer's disease (AD) progression is complex and challenging to model.
- Existing brain network analyses often overlook temporal dynamics and directional spread of pathologies.
- Characterizing the temporal spread of neurodegenerative diseases is crucial for understanding disease mechanisms.
Purpose of the Study:
- To introduce a novel approach, directed progression networks (DPNets), for characterizing the temporal spread of pathologies like AD.
- To apply DPNets to longitudinal magnetic resonance imaging (MRI) data for AD.
- To identify new network-based markers for differentiating normal cognition (NC) from AD.
Main Methods:
- Construction of DPNets using inferred directions of pathology spread from longitudinal cortical thickness measurements.
- Characterization of DPNet properties, contrasting them with traditional correlation or functional networks.
- Utilizing nodal variations (standard deviations) alongside mean values for network classification.
- Application of data-mining methodologies for subject classification based on global network measures.
Main Results:
- DPNets provide novel insights into the temporal progression of Alzheimer's disease.
- Network properties derived from DPNets can serve as markers to differentiate between normal cognition and AD.
- Nodal variations, not just average properties, are significant for network-based classification.
- Unlike many brain networks, DPNets do not exhibit high clustering or small-world properties.
Conclusions:
- Directed progression networks offer a new framework for studying the temporal dynamics of neurodegenerative diseases.
- DPNets provide valuable insights into Alzheimer's disease progression and offer potential for early diagnosis.
- The study highlights the importance of considering directional spread and nodal variability in brain network analysis.
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