Using network analysis to examine the connectivity between the brain regions in rs-fMRI data of FND patient and healthy participant : A single subject study
,
Abstract
Introduction: Functional neurological disorders (FND) is one of the most common causes of neuropathy, However, its cause continues to be mysterious. Understanding the underlying mechanisms of FND is crucial for treatment strategies. The study was conducted on brain images(rs-fMRI) taken from two volunteers (FND patient and healthy subject) who had the same characteristics.
Method: We fitted Gaussian Graphical Models to a single subject data using a network approach.
Results: Based on the results of the networks, the number of significant edges was more in the left hemisphere in the patient, but in the healthy person, the number of these non-zero edges was more in the right hemisphere. Both the networks related to the healthy person and the patient had high density. Therefore, it indicated that the regions considered by these 2 people were strongly related to each other. The results showed the existence of more links and positive relationships between the regions, most of which showed a strong relationship. Among these connections, there were also negative connections. The networks of the healthy participant with almost symmetrical structures and the patient with FND showed different characteristics, including asymmetry between the hemispheres.
Conclusion: this study is the first to demonstrate that the brain regions of both FND patient and healthy participant can be conceptualized as networks. The findings of this study add to a growing body of literature that FND patient brain regions can be analyzed using network approaches.
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2. Pick S, Goldstein LH, Perez DL, Nicholson TR. Emotional processing in functional neurological disorder: a review, biopsychosocial model and research agenda. J Neurol Neurosurg Psychiatry. 2019;90(6):704–11.
3. Bennett K, Diamond C, Hoeritzauer I, Gardiner P, McWhirter L, Carson A, et al. A practical review of functional neurological disorder (FND) for the general physician. Clin Med J R Coll Physicians London. 2021;21(1):28–36.
4. HOME FND Hope - FND Hope International [Internet]. [cited 2021 Oct 29]. Available from: https://fndhope.org/
5. Viswanathan G, de Araujo DB. Brain complex network analysis by means of resting state fMRI and graph analysis: Will it be helpful in clinical epilepsy? Epilepsy Behav. 2014;38:71–80.
6. Delnooz CCS, Pasman JW, Beckmann CF, van de Warrenburg BPC. Task-Free Functional MRI in Cervical Dystonia Reveals Multi-Network Changes That Partially Normalize with Botulinum Toxin. PLoS One. 2013;8(5).
7. Rosazza C, Minati L. Resting-state brain networks: literature review and clinical applications. Neurol Sci. 2011;32:773–85.
8. Mohammadi B, Kollewe K, Samii A, Beckmann CF, Dengler R, Münte TF. Changes in resting‐state brain networks in writer’s cramp. Hum Brain Mapp. 2012;33(4):840–8.
9. Zhang L, Guindani M, Vannucci M. Bayesian models for functional magnetic resonance imaging data analysis. Wiley Interdiscip Rev Comput Stat. 2015;7(1):21–41.
10. indquist MA. The statistical analysis of fMRI data. Stat Sci. 2008;439–64.
11. Tyc J, Selami T, Hensel DS, Hensel M. A Scoping Review of Voxel-Model Applications to Enable Multi-Domain Data Integration in Architectural Design and Urban Planning. Architecture. 2023;3(2):137–74.
12. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17(3):143–55.
13. Ranciati S, Roverato A, Luati A. Fused graphical lasso for brain networks with symmetries. arXiv Prepr arXiv200511785. 2020;
14. Schröter MS, Spoormaker VI, Schorer A, Wohlschläger A, Czisch M, Kochs EF, et al. Spatiotemporal reconfiguration of large-scale brain functional networks during propofol-induced loss of consciousness. J Neurosci. 2012;32(37):12832–40.
15. Bullmore ET, Bassett DS. Brain Graphs: Graphical Models of the Human Brain Connectome. http://dx.doi.org/101146/annurev-clinpsy-040510-143934 [Internet]. 2011 Mar 28 [cited 2021 Oct 7];7:113–40. Available from: https://www.annualreviews.org/doi/abs/10.1146/annurev-clinpsy-040510-143934
16. Zhang Z, Liao W, Chen H, Mantini D, Ding JR, Xu Q, et al. Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain [Internet]. 2011 Oct 1;134(10):2912–28. Available from: https://doi.org/10.1093/brain/awr223
17. Costantini G, Epskamp S, Borsboom D, Perugini M, Mõttus R, Waldorp LJ, et al. State of the aRt personality research: A tutorial on network analysis of personality data in R. J Res Pers. 2015 Feb 1;54:13–29.
18. Hevey D. Network analysis: a brief overview and tutorial. Heal Psychol Behav Med [Internet]. 2018 Jan 1 [cited 2023 May 24];6(1):301. Available from: /pmc/articles/PMC8114409/
19. Crucittia P, Latorab V, Marchioric M, Rapisardab A. Error and attack tolerance of complex networks. Physica A. 2004;340:388–94.
20. Rhemtulla M, Fried EI, Aggen SH, Tuerlinckx F, Kendler KS, Borsboom D. Network analysis of substance abuse and dependence symptoms. Drug Alcohol Depend. 2016;161:230–7.
21. Stanley ML, Moussa MN, Paolini BM, Lyday RG, Burdette JH, Laurienti PJ. Defining nodes in complex brain networks. Front Comput Neurosci. 2013;7:169.
22. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23:S208–19.
23. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. Fsl. Neuroimage. 2012;62(2):782–90.
24. Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;66(3):411–21.
25. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics [Internet]. 2008 Jul 1 [cited 2021 Oct 10];9(3):432–41. Available from: https://academic.oup.com/biostatistics/article/9/3/432/224260
26. Foygel R, Drton M. Extended Bayesian information criteria for Gaussian graphical models. Adv Neural Inf Process Syst 23 24th Annu Conf Neural Inf Process Syst 2010, NIPS 2010. 2010;1–14.
27. Chen J, Chen Z. Extended Bayesian information criteria for model selection with large model spaces. Biometrika. 2008;95(3):759–71.
28. Hand DJ. Statistical analysis of network data: Methods and models by eric d. kolaczyk. Wiley Online Library; 2010.
29. Borgatti SP. Centrality and network flow. Soc Networks. 2005 Jan 1;27(1):55–71.
30. Opsahl T, Agneessens F, networks JSS, 2010 undefined. Node centrality in weighted networks: Generalizing degree and shortest paths. Elsevier [Internet]. [cited 2023 May 29]; Available from: https://www.sciencedirect.com/science/article/pii/S0378873310000183
31. Mladenovici V, Ilie MD, Maricuțoiu L țP, Iancu DE. Approaches to teaching in higher education: the perspective of network analysis using the revised approaches to teaching inventory. High Educ. 2022 Aug 1;84(2):255–77.
32. Constantini G, Perugini M. Network Analysis for Psychological Situations. Oxford Handb Psychol Situations [Internet]. 2017 Jan 1 [cited 2023 May 30];269–86. Available from: https://academic.oup.com/edited-volume/34290/chapter/290684842
33. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods [Internet]. 2018 Feb 1 [cited 2023 May 10];50(1):195–212. Available from: https://link.springer.com/article/10.3758/s13428-017-0862-1
34. Fruchterman TMJ, Reingold EM. Graph drawing by force‐directed placement. Softw Pract Exp. 1991;21(11):1129–64.
35. Lauritzen SL. Graphical models. Vol. 17. Clarendon Press; 1996.
36. ction amount of milk and dairy products using network analysis in Turkey. Food Sci Technol. 2022;42:e125821.
37. Liu Y, Liang M, Zhou Y, He Y, Hao Y, Song M, et al. Disrupted small-world networks in schizophrenia. academic.oup.com [Internet]. [cited 2023 May 7]; Available from: https://academic.oup.com/brain/article-abstract/131/4/945/357118
38. Butler M, Shipston‐Sharman O, Seynaeve M, Bao J, Pick S, Bradley‐Westguard A, et al. International online survey of 1048 individuals with functional neurological disorder. Eur J Neurol. 2021;28(11):3591–602.
39. Sun Y, Chen Y, Collinson SL, Bezerianos A, Sim K. Reduced hemispheric asymmetry of brain anatomical networks is linked to schizophrenia: A connectome study. Cereb Cortex. 2017;27(1):602–15.
40. Wang Q, Su T, Zhou Y, Chou K, Chen I, Neuroimage TJ, et al. Anatomical insights into disrupted small-world networks in schizophrenia. Elsevier [Internet]. [cited 2023 May 7]; Available from: https://www.sciencedirect.com/science/article/pii/S1053811911010834
41. Ottet MC, Schaer M, Debbané M, Cammoun L, Thiran JP, Eliez S. Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations. Front Hum Neurosci. 2012 Jul 9;(JUL).
42. Hilland E, Landrø NI, Kraft B, Tamnes CK, Fried EI, Maglanoc LA, et al. Exploring the links between specific depression symptoms and brain structure: A network study. Psychiatry Clin Neurosci. 2020;74(3):220–1.
43. Wang S, Zhao S, Guo Y, Huang C, Zhang P, She L, et al. A network analysis of subjective well-being in Chinese high school students. BMC Public Health. 2023;23(1):1249.
| Files | ||
| Issue | Vol 9 No 4 (2023) | |
| Section | Articles | |
| DOI | https://doi.org/10.18502/jbe.v9i4.16671 | |
| Keywords | ||
| FND disease graphical lasso rs-fMRI Network analysis Gaussian graphical model | ||
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How to Cite
1.
Ahmadi S, Faghihzadeh E, Oghabian MA. Using network analysis to examine the connectivity between the brain regions in rs-fMRI data of FND patient and healthy participant : A single subject study. JBE. 2023;9(4):461- 473.
