Volume 17, Issue 4 (Journal of Control, V.17, N.4 Winter 2024)
JoC 2024, 17(4): 49-64 |
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Khaledi M, Feizollahi F, Behboudi M, Jalili C, Siyah Mansoory M. Proposing a solution for diagnosing MS disease using dynamic functional brain connectivity tools and intelligent neural network by experimental data. JoC 2024; 17 (4) :49-64
URL: http://joc.kntu.ac.ir/article-1-971-en.html
URL: http://joc.kntu.ac.ir/article-1-971-en.html
1- kermanshah university of medical sciences
2- Tarbiat Modares University
2- Tarbiat Modares University
Abstract: (1045 Views)
In MS disease, the damage imposed on the nerve fiber in structural data is not well detectable. Therefore, relying solely on structural data can lead to the concealment of the disease. This indicates the importance of functional data in the early diagnosis of MS disease. This article examines the analysis of fMRI data for two groups, healthy and MS patients, using dynamic functional brain connectivity tools and intelligent neural networks. Due to cognitive impairment caused by structural damage in the early stages of the disease, presenting a dynamic functional connectivity network model provides the ability to evaluate changes in the topological characteristics of the brain. To this end, a total of 60 subjects, including 30 healthy individuals and 30 patients aged 20 to 60 years, and disease duration ranging from 8 to 60 months with a mean of 30 months, and variable attack numbers, were selected. Subsequently, a complete dictionary with the time series of data from both two groups was extracted and finally, the modular structure concept from sparse weights was used to express the relationships between different brain regions. Reviewing the results showed that a total of 57 ROI regions from both healthy and patient groups were calculated out of which only 16 regions were common between the two groups.
Keywords: MS disease, fMRI imaging data, dynamic functional brain connectivity network, intelligent neural network.
Type of Article: Review paper |
Subject:
Special
Received: 2023/08/5 | Accepted: 2024/01/16 | ePublished ahead of print: 2024/01/20 | Published: 2024/01/21
Received: 2023/08/5 | Accepted: 2024/01/16 | ePublished ahead of print: 2024/01/20 | Published: 2024/01/21
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