Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory

Ali Khazaee, Ataollah Ebrahimzadeh, Abbas Babajani-Feremi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. After preprocessing of data, signals from 90 brain regions, segmented based on the automated anatomical labeling (AAL) atlas, were extracted and edges of the graph were calculated using the correlation between the signals of all pairs of the brain regions. Then a weighted undirected graph was constructed and graph measures were calculated. Fisher score feature selection algorithm were employed to choose most significant features. Finally, using the selected features, we were able to accurately classify patients with AD from healthy control ones with accuracy of 97.5%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD.

Original languageEnglish (US)
Title of host publication2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages252-257
Number of pages6
ISBN (Electronic)9781479974177
DOIs
StatePublished - Jan 1 2014
Event2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014 - Tehran, Iran, Islamic Republic of
Duration: Nov 26 2014Nov 28 2014

Other

Other2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014
CountryIran, Islamic Republic of
CityTehran
Period11/26/1411/28/14

Fingerprint

Graph theory
Brain
Labeling
Pattern recognition
Support vector machines
Learning systems
Magnetic Resonance Imaging
Feature extraction

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2014). Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory. In 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014 (pp. 252-257). [7043931] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBME.2014.7043931

Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory. / Khazaee, Ali; Ebrahimzadeh, Ataollah; Babajani-Feremi, Abbas.

2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 252-257 7043931.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Khazaee, A, Ebrahimzadeh, A & Babajani-Feremi, A 2014, Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory. in 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014., 7043931, Institute of Electrical and Electronics Engineers Inc., pp. 252-257, 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014, Tehran, Iran, Islamic Republic of, 11/26/14. https://doi.org/10.1109/ICBME.2014.7043931
Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory. In 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 252-257. 7043931 https://doi.org/10.1109/ICBME.2014.7043931
Khazaee, Ali ; Ebrahimzadeh, Ataollah ; Babajani-Feremi, Abbas. / Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory. 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 252-257
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