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Published on: January 2, 2012
Small-World Properties in Mild Cognitive Impairment and Early Alzheimer's Disease: A Cortical Thickness MRI Study
1Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, 4th Floor, 660 First Avenue, New York City, NY 10016, USA.
Background:
Small-world network consists of networks with local specialization and global integration. Our objective is to detect small-world properties alteration based on cortical thickness in mild cognitive impairment (MCI) including stables and converters, and early Alzheimer's disease (AD) compared to controls.
Methods:
MRI scans of 13 controls, 10 MCI, and 10 with early AD were retrospectively analyzed; 11 MCI converters, 11 MCI stables, and 10 controls from the ADNI website were also included.
Results:
There were significantly decreased local efficiencies in patients with MCI and AD compared to controls; and MCI patients showed increased global efficiency compared to AD and controls. The MCI converters experience the worst local efficiency during the converting period to AD; the stables, however, have highest local and global efficiency.
Conclusions:
The abnormal cortical thickness-based small-world properties in MCI and AD as well as the distinct patterns between two MCI subtypes suggest that small-world network analysis has the potential to better differentiate different stages of early dementia.
Insights
Small-world network analysis reveals altered brain connectivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Changes in local and global efficiency may help differentiate early dementia stages.
Area of Science:
- Neuroscience
- Network Science
- Medical Imaging
Background:
- Small-world networks balance local specialization and global integration.
- Investigating alterations in these networks based on cortical thickness is crucial for understanding neurodegenerative diseases.
- Mild cognitive impairment (MCI) and early Alzheimer's disease (AD) represent key stages of cognitive decline.
Purpose of the Study:
- To detect changes in small-world network properties using cortical thickness in MCI and early AD patients compared to controls.
- To differentiate between stable MCI and MCI converters to AD.
Main Methods:
- Retrospective analysis of MRI scans from controls, MCI, and early AD patients.
- Utilized data from the ADNI database, including MCI converters and stables.
- Focused on cortical thickness as a basis for network analysis.
Main Results:
- MCI and AD patients exhibited significantly decreased local efficiency compared to controls.
- MCI patients demonstrated increased global efficiency relative to AD and controls.
- MCI converters showed the lowest local efficiency, while MCI stables had the highest local and global efficiency.
Conclusions:
- Abnormal small-world network properties, identified via cortical thickness, are present in MCI and AD.
- Distinct network patterns between MCI subtypes suggest potential for differentiation.
- Small-world network analysis shows promise for distinguishing early dementia stages.
