Identification of Mild Cognitive Impairment Conversion Using Augmented Resting-State Functional Connectivity Under Multi-Modal Parcellation

Abstract

Mild cognitive impairment (MCI) is a transitional stage between normal aging and Alzheimer’s disease (AD), with a high risk of converting to AD. We propose a classification framework with a data augment method to identify MCI converter (MCI-C) and MCI non-converter (MCI-NC). Resting-state functional magnetic resonance images (rs-fMRI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) are processed as augmented resting-state functional connectivity by staggered sliding window (SSW) method proposed by us under Human Connectome Project (HCP) multi-modal parcellation. The HCP brain atlas provides a more detailed cortical parcellation of the brain, allowing for more precise localization of brain regions related to MCI and AD. Finally, the framework archive 88% accuracy in the task of identifying MCI-C. 46 brain regions are suggested as potential MCI-to-AD biomarkers.

Keywords

• Alzheimer’s disease (AD)
• human connectome project (HCP)
• machine learning
• MCI converter
• mild cognitive impairment (MCI)