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A comparative study of correlation methods in functional connectivity analysis using fmri data of alzheimer’s patients

Ahmadi, H ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.31661/jbpe.v0i0.2007-1134
  3. Publisher: Shiraz University of Medical Sciences , 2023
  4. Abstract:
  5. Background: Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging tool, used in brain function research and is also a low-frequency signal, showing brain activation by means of Oxygen consumption. Objective: One of the reliable methods in brain functional connectivity analysis is the correlation method. In correlation analysis, the relationship between two time-series has been investigated. In fMRI analysis, the Pearson correlation is used while there are other methods. This study aims to investigate the different correlation methods in functional connectivity analysis. Material and Methods: In this analytical research, based on fMRI signals of Alzheimer’s Disease (AD) and healthy individuals from the ADNI database, brain functional networks were generated using correlation techniques, including Pearson, Kendall, and Spearman. Then, the global and nodal measures were calculated in the whole brain and in the most important resting-state network called Default Mode Network (DMN). The statistical analysis was performed using non-parametric permu-tation test. Results: Results show that although in nodal analysis, the performance of correlation methods was almost similar, in global features, the Spearman and Kendall were better in distinguishing AD subjects. Note that, nodal analysis reveals that the functional connectivity of the posterior areas in the brain was more damaged because of AD in comparison to frontal areas. Moreover, the functional connectivity of the dominant hemisphere was disrupted more. Conclusion: Although the Pearson method has limitations in capturing non-linear relationships, it is the most prevalent method. To have a comprehensive analysis, in-vestigating non-linear methods such as distance correlation is recommended. © Journal of Biomedical Physics and Engineering This is an Open Access article distributed under the terms of the Creative Com-mons Attribution-NonCommercial 4.0 Unported License, (http://creativecom-mons.org/licenses/by-nc/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
  6. Keywords:
  7. Alzheimer Disease ; Brain ; Brain Networks ; Correlation ; DMN Network ; fMRI ; Functional Connectivity ; Graph Measures ; Neuroimaging
  8. Source: Journal of Biomedical Physics and Engineering ; Volume 13, Issue 2 , 2023 , Pages 125-134 ; 22517200 (ISSN)
  9. URL: https://jbpe.sums.ac.ir/article_47498.html