Can a Resting-State Functional Connectivity Index Identify Patients with Alzheimer's Disease and Mild Cognitive...

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 multivoxel pattern analysis. The multivoxel 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.