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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurol. | doi: 10.3389/fneur.2019.00904

Identification of the early stage of Alzheimer’s disease using structural MRI and resting-state fMRI

Seyed Hani Hojjati1, Ata Ebrahimzadeh2 and  Abbas Babajani-Feremi1, 3, 4*
  • 1Department of Pediatrics, University of Tennessee Health Sciences Center, United States
  • 2Faculty of Electrical & Computer Engineering, Babol Noshirvani University of Technology, Iran
  • 3Le Bonheur Children's Hospital, United States
  • 4Department of Anatomy and Neurobiology, College of Medicine, University of Tennessee Health Science Center, United States

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.

Keywords: Alzheimer’s disease (AD), mild cognitive impairment (MCI), Resting-state fMRI (R-fMRI), graph theory, machine learning, Hub nodes

Received: 04 Apr 2019; Accepted: 05 Aug 2019.

Edited by:

Filippo Cieri, Lou Ruvo Center for Brain Health, Cleveland Clinic, United States

Reviewed by:

Arun Bokde, Trinity College Dublin, Ireland
Jianhui Zhong, University of Rochester, United States  

Copyright: © 2019 Hojjati, Ebrahimzadeh and Babajani-Feremi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Abbas Babajani-Feremi, Department of Pediatrics, University of Tennessee Health Sciences Center, Memphis, 38103, Tennessee, United States, ababajan@uthsc.edu