Volume 17, Issue 4 (Journal of Control, V.17, N.4 Winter 2024)
JoC 2024, 17(4): 49-64 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
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: (1602 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
References
1. [1] D. T. Chard et al., "Mind the gap: from neurons to networks to outcomes in multiple sclerosis," Nat. Rev. Neurol., vol. 17, no. 3, pp. 173-184, 2021. [DOI:10.1038/s41582-020-00439-8]
2. [2] M. M. Schoonheim, T. A. Broeders, and J. J. Geurts, "The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics," NeuroImage Clin., p. 103108, 2022. [DOI:10.1016/j.nicl.2022.103108]
3. [3] C. T. Briels, D. N. Schoonhoven, C. J. Stam, H. de Waal, P. Scheltens, and A. A. Gouw, "Reproducibility of EEG functional connectivity in Alzheimer's disease," Alzheimers Res. Ther., vol. 12, no. 1, pp. 1-14, 2020. [DOI:10.1186/s13195-020-00632-3]
4. [4] M. Sjøgård et al., "Brain dysconnectivity relates to disability and cognitive impairment in multiple sclerosis," Hum. Brain Mapp., vol. 42, no. 3, pp. 626-643, 2021. [DOI:10.1002/hbm.25247]
5. [5] G. Castellazzi et al., "Functional connectivity alterations reveal complex mechanisms based on clinical and radiological status in mild relapsing remitting multiple sclerosis," Front. Neurol., vol. 9, p. 690, 2018. [DOI:10.3389/fneur.2018.00690]
6. [6] M. M. Schoonheim et al., "Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy," Mult. Scler. J., vol. 28, no. 1, pp. 61-70, 2022. [DOI:10.1177/13524585211008743]
7. [7] M. B. Vlaicu, "Epilepsy in multiple sclerosis as a network disease," Mult. Scler. Relat. Disord., vol. 36, p. 101390, 2019. [DOI:10.1016/j.msard.2019.101390]
8. [8] M.-V. A. Duong et al., "Altered functional connectivity related to white matter changes inside the working memory network at the very early stage of MS," J. Cereb. Blood Flow Metab., vol. 25, no. 10, pp. 1245-1253, 2005. [DOI:10.1038/sj.jcbfm.9600122]
9. [9] W. Majeed, M. Magnuson, and S. D. Keilholz, "Spatiotemporal dynamics of low frequency fluctuations in BOLD fMRI of the rat," J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med., vol. 30, no. 2, pp. 384-393, 2009. [DOI:10.1002/jmri.21848]
10. [10] K. J. Friston, "Functional and effective connectivity: a review," Brain Connect., vol. 1, no. 1, pp. 13-36, 2011. [DOI:10.1089/brain.2011.0008]
11. [11] R. M. Hutchison et al., "Dynamic functional connectivity: promise, issues, and interpretations," Neuroimage, vol. 80, pp. 360-378, 2013. [DOI:10.1016/j.neuroimage.2013.05.079]
12. [12] E. A. Allen, E. Damaraju, S. M. Plis, E. B. Erhardt, T. Eichele, and V. D. Calhoun, "Tracking whole-brain connectivity dynamics in the resting state," Cereb. Cortex, vol. 24, no. 3, pp. 663-676, 2014. [DOI:10.1093/cercor/bhs352]
13. [13] C. Chang, Z. Liu, M. C. Chen, X. Liu, and J. H. Duyn, "EEG correlates of time-varying BOLD functional connectivity," Neuroimage, vol. 72, pp. 227-236, 2013. [DOI:10.1016/j.neuroimage.2013.01.049]
14. [14] E. Tagliazucchi, F. Von Wegner, A. Morzelewski, V. Brodbeck, and H. Laufs, "Dynamic BOLD functional connectivity in humans and its electrophysiological correlates," Front. Hum. Neurosci., vol. 6, p. 339, 2012. [DOI:10.3389/fnhum.2012.00339]
15. [15] G. J. Thompson et al., "Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually," Hum. Brain Mapp., vol. 34, no. 12, pp. 3280-3298, 2013. [DOI:10.1002/hbm.22140]
16. [16] C. Chang and G. H. Glover, "Time-frequency dynamics of resting-state brain connectivity measured with fMRI," Neuroimage, vol. 50, no. 1, pp. 81-98, 2010. [DOI:10.1016/j.neuroimage.2009.12.011]
17. [17] D. A. Handwerker, V. Roopchansingh, J. Gonzalez-Castillo, and P. A. Bandettini, "Periodic changes in fMRI connectivity," Neuroimage, vol. 63, no. 3, pp. 1712-1719, 2012. [DOI:10.1016/j.neuroimage.2012.06.078]
18. [18] Z. Fu et al., "Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: an application to schizophrenia," Neuroimage, vol. 180, pp. 619-631, 2018. [DOI:10.1016/j.neuroimage.2017.09.035]
19. [19] M. Díez-Cirarda et al., "Dynamic functional connectivity in Parkinson's disease patients with mild cognitive impairment and normal cognition," NeuroImage Clin., vol. 17, pp. 847-855, 2018. [DOI:10.1016/j.nicl.2017.12.013]
20. [20] P. Tewarie et al., "Explaining the heterogeneity of functional connectivity findings in multiple sclerosis: An empirically informed modeling study," Hum. Brain Mapp., vol. 39, no. 6, pp. 2541-2548, 2018. [DOI:10.1002/hbm.24020]
21. [21] K. Lee, S. Tak, and J. C. Ye, "A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion," IEEE Trans. Med. Imaging, vol. 30, no. 5, pp. 1076-1089, 2010. [DOI:10.1109/TMI.2010.2097275]
22. [22] C.-Y. Wee, P.-T. Yap, D. Zhang, L. Wang, and D. Shen, "Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification," Brain Struct. Funct., vol. 219, no. 2, pp. 641-656, 2014. [DOI:10.1007/s00429-013-0524-8]
23. [23] J. Lv et al., "Sparse representation of whole-brain fMRI signals for identification of functional networks," Med. Image Anal., vol. 20, no. 1, pp. 112-134, 2015. [DOI:10.1016/j.media.2014.10.011]
24. [24] J. Lv et al., "Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data," Psychiatry Res. Neuroimaging, vol. 233, no. 2, pp. 254-268, 2015. [DOI:10.1016/j.pscychresns.2015.07.012]
25. [25] R. Yu, H. Zhang, L. An, X. Chen, Z. Wei, and D. Shen, "Connectivity strength-weighted sparse group representation-based brain network construction for M CI classification," Hum. Brain Mapp., vol. 38, no. 5, pp. 2370-2383, 2017. [DOI:10.1002/hbm.23524]
26. [26] X. Wang, Y. Ren, and W. Zhang, "Depression disorder classification of fMRI data using sparse low-rank functional brain network and graph-based features," Comput. Math. Methods Med., vol. 2017, 2017. [DOI:10.1155/2017/3609821]
27. [27] S.-J. Lin et al., "Both stationary and dynamic functional interhemispheric connectivity are strongly associated with performance on cognitive tests in multiple sclerosis," Front. Neurol., vol. 11, p. 407, 2020. [DOI:10.3389/fneur.2020.00407]
28. [28] C. H. Polman et al., "Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria," Ann. Neurol., vol. 69, no. 2, pp. 292-302, 2011. [DOI:10.1002/ana.22366]
29. [29] F. Azarmi, S. N. M. Ashtiani, A. Shalbaf, H. Behnam, and M. R. Daliri, "Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI," Comput. Biol. Med., vol. 115, p. 103495, 2019. [DOI:10.1016/j.compbiomed.2019.103495]
30. [30] S. Hao, C. Yang, Z. Li, and J. Ren, "Distinguishing patients with temporal lobe epilepsy from normal controls with the directed graph measures of resting-state fMRI," Seizure, vol. 96, pp. 25-33, 2022. [DOI:10.1016/j.seizure.2022.01.007]
31. [31] J. Huang et al., "Altered functional connectivity in white and gray matter in patients with multiple sclerosis," Front. Hum. Neurosci., vol. 14, p. 563048, 2020. [DOI:10.3389/fnhum.2020.563048]
32. [32] M. Rosoł, M. Młyńczak, and G. Cybulski, "Granger causality test with nonlinear neural-network-based methods: Python package and simulation study," Comput. Methods Programs Biomed., vol. 216, p. 106669, Apr. 2022. [DOI:10.1016/j.cmpb.2022.106669]
33. [33] L.-Q. Zhou et al., "Artificial intelligence in medical imaging of the liver," World J. Gastroenterol., vol. 25, no. 6, p. 672, 2019. [DOI:10.3748/wjg.v25.i6.672]
34. [34] Q. Zhang et al., "Comparing the effectiveness of brain structural imaging, resting-state fMRI, and naturalistic fMRI in recognizing social anxiety disorder in children and adolescents," Psychiatry Res. Neuroimaging, vol. 323, p. 111485, 2022. [DOI:10.1016/j.pscychresns.2022.111485]
35. [35] F. Tsouki and A. Williams, "Multifaceted involvement of microglia in gray matter pathology in multiple sclerosis," Stem Cells, vol. 39, no. 8, pp. 993-1007, 2021. [DOI:10.1002/stem.3374]
36. [36] R. Hemmatjo, M. Motamedzade, M. Aliabadi, O. Kalatpour, and M. Farhadian, "The effects of multiple firefighting activities on information processing and work performance in a smoke‐diving room: An intervention study," Hum. Factors Ergon. Manuf. Serv. Ind., vol. 27, no. 6, pp. 261-267, Nov. 2017. [DOI:10.1002/hfm.20709]
37. [37] M. Zhang, X. Huang, B. Li, H. Shang, and J. Yang, "Gray matter structural and functional alterations in idiopathic blepharospasm: A multimodal meta-analysis of VBM and functional neuroimaging studies," Front. Neurol., vol. 13, p. 889714, 2022. [DOI:10.3389/fneur.2022.889714]
38. [38] V. A. Politakis, A. Slana Ozimič, and G. Repovš, "Cognitive control challenge task across the lifespan," Front. Psychol., vol. 12, p. 789816, 2022. [DOI:10.3389/fpsyg.2021.789816]
39. [39] S. Spiteri, T. Hassa, D. Claros-Salinas, M. Schoenfeld, and C. Dettmers, "Functional MRI changes illustrating cognitive fatigue in patients with multiple sclerosis," in 25. Rehabilitationswissenschaftliches Kolloquium, 2016, p. 370. Accessed: Dec. 04, 2023. [DOI:10.1177/1352458517743090]
40. [40] M. A. Rocca, M. M. Schoonheim, P. Valsasina, J. J. Geurts, and M. Filippi, "Task-and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective," NeuroImage Clin., vol. 35, p. 103076, 2022. [DOI:10.1016/j.nicl.2022.103076]
41. [41] O. L. Gamboa et al., "Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks," Neuroimage, vol. 94, pp. 385-395, 2014. [DOI:10.1016/j.neuroimage.2013.12.008]
42. [42] A. D. Savva, G. D. Mitsis, and G. K. Matsopoulos, "Assessment of dynamic functional connectivity in resting‐state fMRI using the sliding window technique," Brain Behav., vol. 9, no. 4, p. e01255, Apr. 2019. [DOI:10.1002/brb3.1255]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
