학술논문

Predicting Progression from Mild Cognitive Impairment to Alzheimer’s Disease using MRI-based Cortical Features and a Two-State Markov Model
Document Type
Conference
Source
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) Biomedical Imaging (ISBI), 2021 IEEE 18th International Symposium on. :1145-1149 Apr, 2021
Subject
Bioengineering
Computing and Processing
Photonics and Electrooptics
Signal Processing and Analysis
Atmospheric measurements
Magnetic resonance imaging
Computed tomography
Biomedical measurement
Predictive models
Markov processes
Particle measurements
Alzheimer’s disease
MRI
Markov model
sulcal morphometry.
Language
ISSN
1945-8452
Abstract
Magnetic resonance imaging (MRI) has a potential for early diagnosis of individuals at risk for developing Alzheimer’s disease (AD). Cognitive performance in healthy elderly people and in those with mild cognitive impairment (MCI) has been associated with measures of cortical gyrification [1] and thickness (CT) [2], yet the extent to which sulcal measures can help to predict AD conversion above and beyond CT measures is not known. Here, we analyzed 721 participants with MCI from phases 1 and 2 of the Alzheimer’s Disease Neuroimaging Initiative, applying a two-state Markov model to study the conversion from MCI to AD condition. Our preliminary results suggest that MRI-based cortical features, including sulcal morphometry, may help to predict conversion from MCI to AD.