Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

Investigation of Non-Linear Functional Connectivity in Alzheimer’s Disease utilizing Resting State fMRI Data and Graph Theory

Document Type : Full Research Paper

Authors

Hessam Ahmadi 1
Emad Fatemizadeh 2
Alimotie Nasrabadi 3

1 Ph.D. Student, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Associate Professor, School of Electrical Engineering, Sharif University of Technology, Tehran, Iran

3 Professor, Biomedical Engineering Department, Shahed University, Tehran, Iran

https://doi.org/10.22041/ijbme.2020.128322.1598
Abstract
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique for analyzing the brain functions through low-frequency fluctuations called the Blood-Oxygen-Level-Dependent (BOLD) signals. Measurement of the functional connectivity in brain networks is usually done by the fMRI time-series through Pearson Correlation Coefficients (PCC). As the PCC shows linear dependencies, in this study, non-linear relationships in the fMRI signals of the patients with Alzheimer's Disease (AD) were investigated using the kernel trick method. Kernel trick approach maps the input information into a higher dimension space and implements the linear calculations in a new space that is proportionate to the non-linear relationships in the primary space. After generating the weighted undirected brain graphs based on the Automated Anatomical Labeling (AAL) atlas, different kernel functions with different parameters were applied. Then the graph global measures including degree, strength, small-worldness, modularity, and efficiencies features were computed and the non-parametric permutation test was performed. According to the results, the kernel trick method showed more significant differences with AD and healthy subjects in comparison with the simple PCC and it could be because of the non-linear correlations that are not captured by the PCC. Among different kernel functions, the Polynomial function had the best performance. Applying this kernel, the classification was done by the Support Vector Machine (SVM) classifier. The achieved accuracy was equal to 98.68±0.79%. The Occipital and Temporal lobes and also the Default Mode Network (DMN) were analyzed and the kernel trick method showed more significant differences in all of them. It is worthwhile to mention that the right and left Angular areas of DMN showed no significant changes in none of the methods and it could be concluded that the AD does not affect this areas effectively.

Keywords

fMRI
Functional Connectivity
Alzheimer’s Disease
Graph Theory
Kernel Trick

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 14, Issue 3
Autumn 2020
Pages 235-249

  • Receive Date 28 May 2020
  • Revise Date 12 September 2020
  • Accept Date 13 September 2020

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