Abstract
Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group (“AD, MCI-C, and MCI-NC” or “MCI-C, MCI-NC, and HC”) and four-group (“AD, MCI-C, MCI-NC, and HC”) classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.
Highlights
Alzheimer’s disease (AD) is a neurodegenerative disorder, known as a disconnection syndrome that disturbs communication between different brain regions [1]
We proposed a machine learning algorithm to classify patients in the early stage of AD (MCI-C and mild cognitive impairment (MCI)-NC), patients with AD, and normal aging subjects (HC) by integrating rs-fMRI and Structural magnetic resonance imaging (sMRI) data
Our results revealed that the sequential feature collection (SFC) algorithm outperformed the discriminant correlation analysis (DCA) feature selection algorithm by providing an extra accuracy of >7% in four-group classification; and [3] we identified hub nodes of the rs-fMRI brain network in AD, MCI converter (MCI-C), MCI non-converter (MCI-NC), and healthy controls (HC), and found different hubs in patients within the early stage of AD
Summary
Alzheimer’s disease (AD) is a neurodegenerative disorder, known as a disconnection syndrome that disturbs communication between different brain regions [1]. Eskildsen et al used patterns of cortical thickness and identified cortical regions potentially discriminative for separating MCI patients into converters (MCIC), who received a diagnosis of AD dementia within 2 years, and non-converters (MCI-NC), who remained stable for 3 years [8]. They reported promising results for the prediction of patients with prodromal AD progressing to probable AD. Beheshti et al utilized a voxel based morphometric technique to extract the global and local gray matter volumes, and used these volumes to classify AD and healthy controls (HC) [9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
