Open AccessIdentification of Mild Cognitive Impairment Conversion using Augmented Resting-state Functional Connectivity under Multi-Modal Parcellation
Author(s) -
Jinhua Sheng,
He Huang,
Qiao Zhang,
Zongjing Li,
Haodi Zhu,
Jialei Wang,
Ziyi Ying,
Jing Zeng
Publication year - 2023
Publication title -
ieee access
Language(s) - English
Resource type - Journals
ISSN - 2169-3536
DOI - 10.1109/access.2023.3342921
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.