Volume 79, Issue 2 (May 2021)
Tehran Univ Med J 2021, 79(2): 102-111 |
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Mohammadi Kiani H, Shalbaf A, Maghsoudi A. Identification of mild cognitive impairment disease using brain
functional connectivity and graph analysis in fMRI data. Tehran Univ Med J 2021; 79 (2) :102-111
URL: http://tumj.tums.ac.ir/article-1-11202-en.html
URL: http://tumj.tums.ac.ir/article-1-11202-en.html
1- Department of Biomedical Engineering, Faculty of Medical Sciences and Technologies , Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,Shalbaf@sbmu.ac.ir
2- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. ,
Abstract: (1574 Views)
Background: Early diagnosis of patients in the early stages of Alzheimer's, known as mild cognitive impairment, is of great importance in the treatment of this disease. If a patient can be diagnosed at this stage, it is possible to treat or delay Alzheimer's disease. Resting-state functional magnetic resonance imaging (fMRI) is very common in the process of diagnosing Alzheimer's disease. In this study, we intend to separate subjects with mild cognitive impairment from healthy control based on fMRI data using brain functional connectivity and graph theory.
Methods: In this article, which was done from April to November 2020 in Tehran, after pre-processing the fMRI data, 116 brain regions were extracted using an Automated Anatomical Labeling atlas. Then, the functional connectivity matrix between the time signals of 116 brain regions was calculated using Pearson correlation and mutual information methods. Using functional connectivity calculations, the brain graph network was formed, followed by thresholding of the brain connectivity network to keep significant and strong edges while eliminating weaker edges that were likely noise. Finally, 11 global features were extracted from the graph network and after performing statistical analyses and selecting optimal features; the classification of 14 healthy individuals and 11 patients with mild cognitive impairment was performed using a support vector machine classifier.
Methods: In this article, which was done from April to November 2020 in Tehran, after pre-processing the fMRI data, 116 brain regions were extracted using an Automated Anatomical Labeling atlas. Then, the functional connectivity matrix between the time signals of 116 brain regions was calculated using Pearson correlation and mutual information methods. Using functional connectivity calculations, the brain graph network was formed, followed by thresholding of the brain connectivity network to keep significant and strong edges while eliminating weaker edges that were likely noise. Finally, 11 global features were extracted from the graph network and after performing statistical analyses and selecting optimal features; the classification of 14 healthy individuals and 11 patients with mild cognitive impairment was performed using a support vector machine classifier.
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Results: Calculations were showed that the mutual information algorithm as a functional connectivity method and five global features of the graph network, including average strength, eccentricity, local efficiency, coefficient clustering and transitivity, using the support vector machine classifier achieved the best performance with the accuracy, sensitivity and specificity of 84, 86 and 93 percent, respectively.
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Conclusion: Combining the features of brain graph and functional connectivity by the mutual information method with a machine learning approach, based on fMRI imaging analysis, is very effective in diagnosing mild cognitive impairment in the early stages of Alzheimer’s which consequently allows treating or delaying this disease.
Keywords: Alzheimer's disease, functional magnetic resonance imaging, machine learning, mild cognitive impairment.
Type of Study: Original Article |
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