Alteration of the anatomical covariance network after corpus callosotomy in pediatric intractable epilepsy

English version

Autoři: Riyo Ueda ^aff001; Hiroshi Matsuda ^aff003; Noriko Sato ^aff004; Masaki Iwasaki ^aff005; Daichi Sone ^aff003; Eri Takeshita ^aff007; Yuko Shimizu-Motohashi ^aff007; Akihiko Ishiyama ^aff007; Takashi Saito ^aff007; Hirofumi Komaki ^aff007; Eiji Nakagawa ^aff007; Kenji Sugai ^aff007; Masayuki Sasaki ^aff007; Yoshimi Kaga ^aff001; Hiroshige Takeichi ^aff001; Masumi Inagaki ^aff001
Působiště autorů: Department of Developmental Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan ^aff001; Division of Frontier Medicine and Pharmacy, Graduate School of Medical and Pharmaceutical Science, Chiba University, Chiba, Japan ^aff002; Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan ^aff003; Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan ^aff004; Department of Neurosurgery, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan ^aff005; Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, England, United Kingdom ^aff006; Department of Child Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan ^aff007; Computational Engineering Applications Unit, RIKEN, Wako, Japan ^aff008
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222876

Souhrn

Purpose

This study aimed to use graph theoretical analysis of anatomical covariance derived from structural MRI to reveal how the gray matter connectivity pattern is altered after corpus callosotomy (CC).

Materials and methods

We recruited 21 patients with epilepsy who had undergone CC. Enrollment criteria were applied: (1) no lesion identified on brain MRI; (2) no history of other brain surgery; and (3) age not younger than 3 years and not older than 18 years at preoperative MRI evaluation. The most common epilepsy syndrome was Lennox-Gastaut syndrome (11 patients). For voxel-based morphometry, the normalized gray matter images of pre-CC and post-CC patients were analyzed with SPM12 (voxel-level threshold of p<0.05 [familywise error-corrected]). Secondly, the images of both groups were subjected to graph theoretical analysis using the Graph Analysis Toolbox with SPM8. Each group was also compared with 32 age- and sex-matched control patients without brain diseases.

Results

Comparisons between the pre- and post-CC groups revealed a significant reduction in seizure frequency with no change in mean intelligence quotient/developmental quotient levels. There was no relationship among the three groups in global network metrics or in targeted attack. A regional comparison of betweenness centrality revealed decreased connectivity to and from the right middle cingulate gyri and medial side of the right superior frontal gyrus and a partial shift in the distribution of betweenness centrality hubs to the normal location. Significantly lower resilience to random failure was found after versus before CC and versus controls (p = 0.0450 and p = 0.0200, respectively).

Conclusion

Graph theoretical analysis of anatomical covariance derived from structural imaging revealed two neural network effects of resection associated with seizure reduction: the reappearance of a structural network comparable to that in healthy children and reduced connectivity along the median line, including the middle cingulate gyrus.

Klíčová slova:

Network analysis – Central nervous system – Magnetic resonance imaging – Surgical and invasive medical procedures – Neural networks – Network resilience – Covariance – Epilepsy

Zdroje

1. Stigsdotter-Broman L, Olsson I, Flink R, Rydenhag B, Malmgren K. Long-term follow-up after callosotomy—a prospective, population based, observational study. Epilepsia. 2014; 55: 316–321. doi: 10.1111/epi.12488 24372273.

2. Graham D, Tisdall MM, Gill D. Corpus callosotomy outcomes in pediatric patients: A systematic review. Epilepsia. 2016; 57: 1053–1068. doi: 10.1111/epi.13408 27237542.

3. Iwasaki M, Uematsu M, Hino-Fukuyo N, Osawa S, Shimoda Y, Jin K, et al. Clinical profiles for seizure remission and developmental gains after total corpus callosotomy. Brain Dev. 2016; 38: 47–53. doi: 10.1016/j.braindev.2015.04.010 25958823.

4. Otsuki T, Kim HD, Luan G, Inoue Y, Baba H, Oguni H, et al. Surgical versus medical treatment for children with epileptic encephalopathy in infancy and early childhood: Results of an international multicenter cohort study in Far-East Asia (the FACE study). Brain Dev. 2016; 38: 449–460. doi: 10.1016/j.braindev.2015.11.004 26686601.

5. Yonekawa T, Nakagawa E, Takeshita E, Inoue Y, Inagaki M, Kaga M, et al. Effect of corpus callosotomy on attention deficit and behavioral problems in pediatric patients with intractable epilepsy. Epilepsy Behav. 2011; 22: 697–704. doi: 10.1016/j.yebeh.2011.08.027 21978470.

6. Passamonti C, Zamponi N, Foschi N, Trignani R, Luzi M, Cesaroni E, et al. Long-term seizure and behavioral outcomes after corpus callosotomy. Epilepsy Behav. 2014; 41: 23–29. doi: 10.1016/j.yebeh.2014.08.130 25269691.

7. van Diessen E, Diederen SJ, Braun KP, Jansen FE, Stam CJ. Functional and structural brain networks in epilepsy: what have we learned? Epilepsia. 2013; 54: 1855–1865. doi: 10.1111/epi.12350 24032627.

8. Liang JG, Lee D, Youn SE, Kim HD, Kim NY. Electroencephalography Network Effects of Corpus Callosotomy in Patients with Lennox-Gastaut Syndrome. Front Neurol. 2017; 8: 456. doi: 10.3389/fneur.2017.00456 28928710.

9. Liang JG, Kim NY, Ko A, Kim HD, Lee D. Changes in functional brain network topology after successful and unsuccessful corpus callosotomy for Lennox-Gastaut Syndrome. Sci Rep. 2018; 8: 3414. doi: 10.1038/s41598-018-21764-5 29467376.

10. Stam CJ. Modern network science of neurological disorders. Nature Reviews Neuroscience. 2014; 15: 683. doi: 10.1038/nrn3801 25186238.

11. Evans AC. Networks of anatomical covariance. Neuroimage. 2013; 80: 489–504. doi: 10.1016/j.neuroimage.2013.05.054 23711536.

12. Wen W, He Y, Sachdev P. Structural brain networks and neuropsychiatric disorders. Curr Opin Psychiatry. 2011; 24: 219–225. doi: 10.1097/YCO.0b013e32834591f8 21430538.

13. Bernhardt BC, Bonilha L, Gross DW. Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy. Epilepsy Behav. 2015; 50: 162–170. doi: 10.1016/j.yebeh.2015.06.005 26159729.

14. Bernhardt BC, Chen Z, He Y, Evans AC, Bernasconi N. Graph-theoretical analysis reveals disrupted small-world organization of cortical thickness correlation networks in temporal lobe epilepsy. Cereb Cortex. 2011; 21: 2147–2157. doi: 10.1093/cercor/bhq291 21330467.

15. Bonilha L, Tabesh A, Dabbs K, Hsu DA, Stafstrom CE, Hermann BP, et al. Neurodevelopmental alterations of large-scale structural networks in children with new-onset epilepsy. Hum Brain Mapp. 2014; 35: 3661–3672. doi: 10.1002/hbm.22428 24453089.

16. Sone D, Watanabe M, Maikusa N, Sato N, Kimura Y, Enokizono M, et al. Reduced resilience of brain gray matter networks in idiopathic generalized epilepsy: A graph-theoretical analysis. PLoS One. 2019; 14: e0212494. doi: 10.1371/journal.pone.0212494 30768622.

17. Unterberger I, Bauer R, Walser G, Bauer G. Corpus callosum and epilepsies. Seizure. 2016; 37: 55–60. doi: 10.1016/j.seizure.2016.02.012 27010176.

18. Ueda R, Shimizu-Motohashi Y, Sugai K, Takeshita E, Ishiyama A, Saito T, et al. Seizure imitators monitored using video-EEG in children with intellectual disabilities. Epilepsy Behav. 2018; 84: 122–126. doi: 10.1016/j.yebeh.2018.05.006 29791879.

19. Takeda K, Matsuda H, Miyamoto Y, Yamamoto H. Structural brain network analysis of children with localization-related epilepsy. Brain Dev. 2017; 39: 678–686. doi: 10.1016/j.braindev.2017.04.010 28487114.

20. Natsume J, Ogawa C, Fukasawa T, Yamamoto H, Ishihara N, Sakaguchi Y, et al. White Matter Abnormality Correlates with Developmental and Seizure Outcomes in West Syndrome of Unknown Etiology. AJNR Am J Neuroradiol. 2016; 37: 698–705. doi: 10.3174/ajnr.A4589 26585267.

21. Sone D, Matsuda H, Ota M, Maikusa N, Kimura Y, Sumida K, et al. Impaired cerebral blood flow networks in temporal lobe epilepsy with hippocampal sclerosis: A graph theoretical approach. Epilepsy Behav. 2016; 62: 239–245. doi: 10.1016/j.yebeh.2016.07.016 27497065.

22. Hosseini SM, Hoeft F, Kesler SR. GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS One. 2012; 7: e40709. doi: 10.1371/journal.pone.0040709 22808240.

23. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002; 15: 273–289. doi: 10.1006/nimg.2001.0978 11771995.

24. Vaessen MJ, Jansen JF, Vlooswijk MC, Hofman PA, Majoie HJ, Aldenkamp AP, et al. White matter network abnormalities are associated with cognitive decline in chronic epilepsy. Cereb Cortex. 2012; 22: 2139–2147. doi: 10.1093/cercor/bhr298 22038907.

25. Ibrahim GM, Morgan BR, Lee W, Smith ML, Donner EJ, Wang F, et al. Impaired development of intrinsic connectivity networks in children with medically intractable localization-related epilepsy. Hum Brain Mapp. 2014; 35: 5686–5700. doi: 10.1002/hbm.22580 24976288.

26. Warren AEL, Harvey AS, Abbott DF, Vogrin SJ, Bailey C, Davidson A, et al. Cognitive network reorganization following surgical control of seizures in Lennox-Gastaut syndrome. Epilepsia. 2017; 58: e75–e81. doi: 10.1111/epi.13720 28295228.

27. Roland JL, Snyder AZ, Hacker CD, Mitra A, Shimony JS, Limbrick DD, et al. On the role of the corpus callosum in interhemispheric functional connectivity in humans. Proc Natl Acad Sci U S A. 2017; 114: 13278–13283. doi: 10.1073/pnas.1707050114 29183973.

28. Siniatchkin M, Coropceanu D, Moeller F, Boor R, Stephani U. EEG-fMRI reveals activation of brainstem and thalamus in patients with Lennox-Gastaut syndrome. Epilepsia. 2011; 52: 766–774. doi: 10.1111/j.1528-1167.2010.02948.x 21275978.

29. Tyvaert L, Chassagnon S, Sadikot A, LeVan P, Dubeau F, Gotman J. Thalamic nuclei activity in idiopathic generalized epilepsy: an EEG-fMRI study. Neurology. 2009; 73: 2018–2022. doi: 10.1212/WNL.0b013e3181c55d02 19996076.

30. Aghakhani Y, Bagshaw AP, Benar CG, Hawco C, Andermann F, Dubeau F, et al. fMRI activation during spike and wave discharges in idiopathic generalized epilepsy. Brain. 2004; 127: 1127–1144. doi: 10.1093/brain/awh136 15033899.

31. Archer JS, Warren AE, Stagnitti MR, Masterton RA, Abbott DF, Jackson GD. Lennox-Gastaut syndrome and phenotype: secondary network epilepsies. Epilepsia. 2014; 55: 1245–1254. doi: 10.1111/epi.12682 24902608.

32. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci. 2006; 26: 63–72. doi: 10.1523/JNEUROSCI.3874-05.2006 16399673.

33. Santarnecchi E, Rossi S, Rossi A. The smarter, the stronger: intelligence level correlates with brain resilience to systematic insults. Cortex. 2015; 64: 293–309. doi: 10.1016/j.cortex.2014.11.005 25569764.

34. Ibrahim GM, Cassel D, Morgan BR, Smith ML, Otsubo H, Ochi A, et al. Resilience of developing brain networks to interictal epileptiform discharges is associated with cognitive outcome. Brain. 2014; 137: 2690–2702. doi: 10.1093/brain/awu214 25104094.

35. Haneef Z, Levin HS, Chiang S. Brain Graph Topology Changes Associated with Anti-Epileptic Drug Use. Brain Connect. 2015; 5: 284–291. doi: 10.1089/brain.2014.0304 25492633.

36. Iwasaki M, Uematsu M, Sato Y, Nakayama T, Haginoya K, Osawa S, et al. Complete remission of seizures after corpus callosotomy. J Neurosurg Pediatr. 2012; 10: 7–13. doi: 10.3171/2012.3.PEDS11544 22681320.

37. Drakesmith M, Caeyenberghs K, Dutt A, Lewis G, David AS, Jones DK. Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data. Neuroimage. 2015; 118: 313–333. doi: 10.1016/j.neuroimage.2015.05.011 25982515.