Individualized Morphological Covariation Network Aberrance Associated with Seizure Relapse After Antiseizure Medication Withdrawal

Overview

Journal Neurol Sci

Date 2025 Jan 11

PMID 39798068

Authors

Ge Tan

Xiuli Li

Ping Jiang

Du Lei

Fangzhou Liu

Yingchun Xu

Bochao Cheng

Qiyong Gong

Ling Liu

Affiliations

Soon will be listed here.

Abstract

This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups. Relative to the Control, the SF-group exhibited lower local efficiency, while the SR-group displayed lower global and local efficiency and longer characteristic path length. Both patient groups displayed reduced centrality in certain subcortical and cortical nodes than the Control, with a more pronounced reduction in the SR-group. Additionally, the SR-group exhibited lower centrality of the right pallidum than the SF-group. Decreased subcortical-cortical connectivity was found in both patient groups than the Control, with a more extensive decrease in the SR-group. Furthermore, an edge connecting the right pallidum and left middle temporal gyrus exhibited decreased connectivity in the SR-group than in the SF-group. A weaker small-worldization network upon medication withdrawal, potentially underpinned by node decentralization and subcortical-cortical decoupling, may elevate the risk of seizure relapse.

References

Kwan P, Brodie M . Early identification of refractory epilepsy. N Engl J Med. 2000; 342(5):314-9. DOI: 10.1056/NEJM200002033420503. View

Lamberink H, Otte W, Geerts A, Pavlovic M, Ramos-Lizana J, Marson A . Individualised prediction model of seizure recurrence and long-term outcomes after withdrawal of antiepileptic drugs in seizure-free patients: a systematic review and individual participant data meta-analysis. Lancet Neurol. 2017; 16(7):523-531. DOI: 10.1016/S1474-4422(17)30114-X. View

Lamberink H, Otte W, Geleijns K, Braun K . Antiepileptic drug withdrawal in medically and surgically treated patients: a meta-analysis of seizure recurrence and systematic review of its predictors. Epileptic Disord. 2015; 17(3):211-28. DOI: 10.1684/epd.2015.0764. View

Gleichgerrcht E, Kocher M, Bonilha L . Connectomics and graph theory analyses: Novel insights into network abnormalities in epilepsy. Epilepsia. 2015; 56(11):1660-8. DOI: 10.1111/epi.13133. View

Sporns O . Network attributes for segregation and integration in the human brain. Curr Opin Neurobiol. 2013; 23(2):162-71. DOI: 10.1016/j.conb.2012.11.015. View

Bullmore E, Sporns O . The economy of brain network organization. Nat Rev Neurosci. 2012; 13(5):336-49. DOI: 10.1038/nrn3214. View

Stam C . Modern network science of neurological disorders. Nat Rev Neurosci. 2014; 15(10):683-95. DOI: 10.1038/nrn3801. View

Chiang S, Haneef Z . Graph theory findings in the pathophysiology of temporal lobe epilepsy. Clin Neurophysiol. 2014; 125(7):1295-305. PMC: 4281254. DOI: 10.1016/j.clinph.2014.04.004. View

Kramer M, Eden U, Kolaczyk E, Zepeda R, Eskandar E, Cash S . Coalescence and fragmentation of cortical networks during focal seizures. J Neurosci. 2010; 30(30):10076-85. PMC: 2927849. DOI: 10.1523/JNEUROSCI.6309-09.2010. View

10.

Tavakol S, Royer J, Lowe A, Bonilha L, Tracy J, Jackson G . Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks. Epilepsia. 2019; 60(4):593-604. PMC: 6447443. DOI: 10.1111/epi.14688. View

11.

Tan G, Li X, Niu R, Chen D, Wang H, Zhu L . Microstructural features of the cerebral cortex: Implications for predicting epilepsy relapse after drug withdrawal. Brain Res. 2020; 1751:147200. DOI: 10.1016/j.brainres.2020.147200. View

12.

Scheffer I, Berkovic S, Capovilla G, Connolly M, French J, Guilhoto L . ILAE classification of the epilepsies: Position paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017; 58(4):512-521. PMC: 5386840. DOI: 10.1111/epi.13709. View

13.

Sillanpaa M, Schmidt D, Saarinen M, Shinnar S . Remission in epilepsy: How long is enough?. Epilepsia. 2017; 58(5):901-906. DOI: 10.1111/epi.13732. View

14.

Fisher R, Cross J, French J, Higurashi N, Hirsch E, Jansen F . Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology. Epilepsia. 2017; 58(4):522-530. DOI: 10.1111/epi.13670. View

15.

Fisher R, Cross J, Dsouza C, French J, Haut S, Higurashi N . Instruction manual for the ILAE 2017 operational classification of seizure types. Epilepsia. 2017; 58(4):531-542. DOI: 10.1111/epi.13671. View

16.

Ashburner J, Friston K . Unified segmentation. Neuroimage. 2005; 26(3):839-51. DOI: 10.1016/j.neuroimage.2005.02.018. View

17.

Ashburner J . A fast diffeomorphic image registration algorithm. Neuroimage. 2007; 38(1):95-113. DOI: 10.1016/j.neuroimage.2007.07.007. View

18.

Tijms B, Series P, Willshaw D, Lawrie S . Similarity-based extraction of individual networks from gray matter MRI scans. Cereb Cortex. 2011; 22(7):1530-41. DOI: 10.1093/cercor/bhr221. View

19.

van Wijk B, Stam C, Daffertshofer A . Comparing brain networks of different size and connectivity density using graph theory. PLoS One. 2010; 5(10):e13701. PMC: 2965659. DOI: 10.1371/journal.pone.0013701. View

20.

Batalle D, Munoz-Moreno E, Figueras F, Bargallo N, Eixarch E, Gratacos E . Normalization of similarity-based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction. Neuroimage. 2013; 83:901-11. DOI: 10.1016/j.neuroimage.2013.07.045. View