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language
eng
Author
Ozasa, Kentaro
Yamaguchi, Shuhei
Description
Resting-state functional connectivity is one promising biomarker for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, it is still not known how accurately network analysis identifies AD and MCI across multiple sites. In this study, we examined whether resting-state functional connectivity data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) could identify patients with AD and MCI at our site. We implemented an index based on the functional connectivity frequency distribution, and compared performance for AD and MCI identification with multi-voxel pattern analysis. The multi-voxel pattern analysis using a connectivity map of the default mode network showed good performance, with an accuracy of 81.9% for AD and MCI identification within the ADNI, but the classification model obtained from the ADNI failed to classify AD, MCI, and healthy elderly adults from our site, with an accuracy of only 43.1%. In contrast, a functional connectivity index of the medial temporal lobe based on the frequency distribution showed moderate performance, with an accuracy of 76.5 - 80.3% for AD identification within the ADNI. The performance of this index was similar for our data, with an accuracy of 73.9 - 82.6%. The frequency distribution-based index of functional connectivity could be a good biomarker for AD across multiple sites.
Subject
Resting-state functional MRI
Alzheimer’s disease
default mode network
multi-voxel pattern analysis
frequency-distribution-based analysis
hippocampus
Journal Title
Brain connectivity
Volume
7
Issue
7
ISSN
2158-0014
ISSN(Online)
2158-0022
Published Date
2017-09-01
DOI
PubMed ID
Publisher
Mary Ann Liebert
NII Type
Journal Article
Format
PDF
Text Version
著者版
OAI-PMH Set
Faculty of Medicine
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