[Home ] [Archive]    
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Home::
IJRR Information::
About the Journal
Editorial Board
Keywords
For Authors::
Aims and Scope
Call for Papers
Submission Instructions
For Reviewers::
Referee page
Subscription::
Subscription Form
News & Events::
Journal News
Web Mail::
::
Search in website

Advanced Search
..
Receive site information
Enter your Email in the following box to receive the site news and information.
..
ISSN
Hard Copy 2322-3243
Online 2345-4229
..
Online Submission
Now you can send your articles to IJRR office using the article submission system.
..

AWT IMAGE

AWT IMAGE
Volume 22, Issue 1 (1-2024)                   Int J Radiat Res 2024, 22(1): 185-192 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Vardian M, Oghabian M, Arbabi M, Ebrahimi T. Assessment of altered brain function in patients with psychogenic non-epileptic seizures using resting-state functional MRI. Int J Radiat Res 2024; 22 (1) :185-192
URL: http://ijrr.com/article-1-5253-en.html
Assessment of altered brain function in patients with psychogenic non-epileptic seizures using resting-state functional MRI
M. Vardian , M.A. Oghabian , M. Arbabi , T. Ebrahimi
Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran , vardian.medphys@gmail.com
Abstract:   (1609 Views)
Background: Psychogenic non-epileptic seizure (PNES) is a disease characterized by the alternations in the brain network. The current study aimed to assess the global and local brain network changes in various brain regions for the patients with PNES using functional magnetic resonance imaging (fMRI). Materials and Methods: The resting-state fMRI (rs-fMRI) data of 32 adults (ranged from 22-61 years; mean: 33.1±7.2), including 16 healthy controls and 16 PNES patients, were obtained. Several standard global network parameters, including small-worldness, average clustering coefficient, characteristic path length, and global efficiency, were investigated. Nodal characteristics, such as the degree of centrality (DC), betweenness centrality (BC), nodal efficiency (NF), nodal local efficiency (NLF), nodal clustering coefficient (NCC), and shortest route, were also determined independently for each node (region) to represent local changes in the brain network. The local and global parameters’ values were compared between healthy individuals and PNES patients using Mann-Whitney statistical test. Results: There was no significant difference among the global parameter values obtained from PNES patients and healthy individuals (P˃0.05). However, many local brain network parameters showed statistically significant differences in the functional connectivity networks (P˂0.05), including attentional, sensorimotor, default mode, executive control networks, and subcortical area. Conclusion: Although global brain network parameters calculated from fMRI images were similar between healthy and PNES participants, many local brain network parameters showed statistically significant differences. Our findings support PNES patients' hypoactivity in the regions associated with awareness and motor control as well as their hyperactivity in the areas associated with emotion and motion control.
Keywords: Brain networks, PNES, graph theory, functional connectivity, rs-fMRI.
Full-Text [PDF 1042 kb]   (580 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. 1. Zhang J (2019) Basic neural units of the brain: neurons, synapses and action potential. arXiv preprint arXiv:190601703. E-book.
2. Uddin LQ (2021) Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nature Reviews Neuroscience, 22(3): 167-179. [DOI:10.1038/s41583-021-00428-w]
3. Proctor RW, and Van Zandt T (2018) Human factors in simple and complex systems. CRC press. E-book, 3rd Edition.
4. Welch JD, Kozareva V, Ferreira A, et al. (2019) Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell, 177(7): 1873-1887. [DOI:10.1016/j.cell.2019.05.006]
5. Avena-Koenigsberger A, Misic B, Sporns O (2018) Communication dynamics in complex brain networks. Nature Reviews Neuroscience, 19(1): 17-33. [DOI:10.1038/nrn.2017.149]
6. King JA, Frank GK, Thompson PM, Ehrlich S (2018) Structural neuroimaging of anorexia nervosa: future directions in the quest for mechanisms underlying dynamic alterations. Biological Psychiatry, 83(3): 224-234. [DOI:10.1016/j.biopsych.2017.08.011]
7. Perez DL, Nicholson TR, Asadi-Pooya AA, et al. (2021) Neuroimaging in functional neurological disorder: state of the field and research agenda. NeuroImage: Clinical, 30:102623. [DOI:10.1016/j.nicl.2021.102623]
8. Gonzalez-Astudillo J, Cattai T, Bassignana G, et al. (2021) Network-based brain-computer interfaces: principles and applications. Journal of Neural Engineering, 18(1): 011001. [DOI:10.1088/1741-2552/abc760]
9. Wang W, Mei M, Gao Y, et al. (2020) Changes of brain structural network connection in Parkinson's disease patients with mild cognitive dysfunction: a study based on diffusion tensor imaging. Journal of Neurology, 267(4): 933-943. [DOI:10.1007/s00415-019-09645-x]
10. Kozlowska K, Chudleigh C, Cruz C, et al. (2018) Psychogenic non-epileptic seizures in children and adolescents: Part II-explanations to families, treatment, and group outcomes. Clinical Child Psychology and Psychiatry, 23(1): 160-176. [DOI:10.1177/1359104517730116]
11. Goldstein LH and Mellers JD (2012) Recent developments in our understanding of the semiology and treatment of psychogenic nonepileptic seizures. Current Neurology and Neuroscience Reports, 12(4): 436-444. [DOI:10.1007/s11910-012-0278-3]
12. Baslet G (2011) Psychogenic non-epileptic seizures: a model of their pathogenic mechanism. Seizure, 20(1): 1-13. [DOI:10.1016/j.seizure.2010.10.032]
13. Cerasa A and Labate A (2018) The meaning of anxiety in patients with PNES. Epilepsy & Behavior, 87: 248. [DOI:10.1016/j.yebeh.2018.07.012]
14. Lanzillotti AI, Sarudiansky M, Lombardi NR, Korman GP. (2021) Updated review on the diagnosis and primary management of psychogenic nonepileptic seizure disorders. Neuropsychiatric Disease and Treatment, 17: 1825-1838. [DOI:10.2147/NDT.S286710]
15. Dörfel D, Gärtner A, Scheffel C. (2020) Resting state cortico-limbic functional connectivity and dispositional use of emotion regulation strategies: A replication and extension study. Frontiers in Behavioral Neuroscience, 14: 128-141. [DOI:10.3389/fnbeh.2020.00128]
16. Diez I, Ortiz-Terán L, Williams B, et al. (2019) Corticolimbic fast-tracking: enhanced multimodal integration in functional neurological disorder. Journal of Neurology, Neurosurgery & Psychiatry, 90(8): 929-938. [DOI:10.1136/jnnp-2018-319657]
17. Syan SK, Smith M, Frey BN, et al. (2018) Resting-state functional connectivity in individuals with bipolar disorder during clinical remission: a systematic review. Journal of Psychiatry and Neuroscience, 43(5): 298-316. [DOI:10.1503/jpn.170175]
18. Amiri S, Mirbagheri MM, Asadi-Pooya AA, et al. (2021) Brain functional connectivity in individuals with psychogenic nonepileptic seizures (PNES): An application of graph theory. Epilepsy & Behavior, 114: 107565. [DOI:10.1016/j.yebeh.2020.107565]
19. Allendorfer JB, Nenert R, Hernando KA, et al. (2019) FMRI response to acute psychological stress differentiates patients with psychogenic non-epileptic seizures from healthy controls-A biochemical and neuroimaging biomarker study. NeuroImage: Clinical, 24: 101967. [DOI:10.1016/j.nicl.2019.101967]
20. Zhai Q, Rahardjo H, Satyanaga A, et al. (2021) Estimation of wetting hydraulic conductivity function for unsaturated sandy soil. Engineering Geology, 285: 106034. [DOI:10.1016/j.enggeo.2021.106034]
21. van der Kruijs SJ, Jagannathan SR, Bodde NM, et al. (2014) Resting-state networks and dissociation in psychogenic non-epileptic seizures. Journal of Psychiatric Research, 54: 126-133. [DOI:10.1016/j.jpsychires.2014.03.010]
22. Meier B, Rothen N, Walter S. (2014) Developmental aspects of synaesthesia across the adult lifespan. Frontiers in human neuroscience, 8: 129. [DOI:10.3389/fnhum.2014.00129]
23. Xia M, Wang J, He Y (2013) BrainNet Viewer: a network visualization tool for human brain connectomics. PloS one, 8(7): e68910. [DOI:10.1371/journal.pone.0068910]
24. Kocher M, Gleichgerrcht E, Nesland T, et al. (2015) Individual variability in the anatomical distribution of nodes participating in rich club structural networks. Frontiers in Neural Circuits, 9(4): 1-7. [DOI:10.3389/fncir.2015.00016]
25. van den Heuvel MP and Sporns O (2011) Rich-club organization of the human connectome. Journal of Neuroscience, 31(44): 15775-86. [DOI:10.1523/JNEUROSCI.3539-11.2011]
26. Senden M, Deco G, De Reus MA, et al. (2014) Rich club organization supports a diverse set of functional network configurations. NeuroImage, 96: 174-82. [DOI:10.1016/j.neuroimage.2014.03.066]
27. Januchowski-Hartley SR, Adams VM, Hermoso V (2018) The need for spatially explicit quantification of benefits in invasive-species management. Conservation Biology, 32(2): 287-93. [DOI:10.1111/cobi.13031]
28. De Bona AA, Fonseca KVO, Rosa MO, et al. (2016) Analysis of public bus transportation of a Brazilian city based on the theory of complex networks using the P-space. Mathematical Problems in Engineering, 2016: 3898762. [DOI:10.1155/2016/3898762]
29. Boccaletti S, Latora V, Moreno Y, et al. (2006) Complex networks: Structure and dynamics. Physics Reports, 424(4-5): 175-308. [DOI:10.1016/j.physrep.2005.10.009]
30. Fleischer V, Radetz A, Ciolac D, et al. (2019) Graph theoretical framework of brain networks in multiple sclerosis: a review of concepts. Neuroscience, 403: 35-53. [DOI:10.1016/j.neuroscience.2017.10.033]
31. Sporns O (2022) Graph theory methods: applications in brain networks. Dialogues in clinical neuroscience.
32. Dienstag A, Ben-Naim S, Gilad M, et al. (2019) Memory and motor control in patients with psychogenic nonepileptic seizures. Epilepsy & Behavior, 98: 279-284. [DOI:10.1016/j.yebeh.2019.07.026]
33. Li R, Li Y, An D, et al. (2015) Altered regional activity and inter-regional functional connectivity in psychogenic non-epileptic seizures. Scientific Reports, 5(1): 1-12. [DOI:10.1038/srep11635]
34. Pollatos O and Herbert BM (2018) Interoception: Definitions, dimensions, neural substrates. In: Embodiment in Psychotherapy, 15-27. [DOI:10.1007/978-3-319-92889-0_2]
35. Borsook D, Veggeberg R, Erpelding N, et al. (2016) The insula: a "hub of activity" in migraine. The Neuroscientist, 22(6): 632-652. [DOI:10.1177/1073858415601369]
36. Buldú JM, Bajo R, Maestú F, et al. (2011) Reorganization of functional networks in mild cognitive impairment. PLoS ONE, 6(5). [DOI:10.1371/journal.pone.0019584]
37. Brown RJ and Reuber M (2016) Towards an integrative theory of psychogenic non-epileptic seizures (PNES). Clinical Psychology Review, 47: 55-70. [DOI:10.1016/j.cpr.2016.06.003]
38. Nayeri A, Rafla-Yuan E, Farber-Eger E, et al. (2017) Pre-existing psychiatric illness is associated with increased risk of recurrent Takotsubo cardiomyopathy. Psychosomatics, 58(5): 527-532. [DOI:10.1016/j.psym.2017.04.008]
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
International Journal of Radiation Research
Persian site map - English site map - Created in 0.03 seconds with 50 queries by YEKTAWEB 4722