Default Mode Network structural alterations in Kocher-Monro trajectory white matter transection: A 3 and 7 tesla simulation modeling approach


Autoři: Saül Pascual-Diaz aff001;  Jose Pineda aff001;  Laura Serra aff001;  Federico Varriano aff001;  Alberto Prats-Galino aff001
Působiště autorů: Laboratory of Surgical Neuroanatomy, University of Barcelona, Barcelona, Spain aff001
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224598

Souhrn

The Kocher-Monro trajectory to the cerebral ventricular system represents one of the most common surgical procedures in the field of neurosurgery. Several studies have analyzed the specific white matter disruption produced during this intervention, which has no reported adverse neurological outcomes. In this study, a graph-theoretical approach was applied to quantify the structural alterations in whole-brain level connectivity. To this end, 132 subjects were randomly selected from the Human Connectome Project dataset and used to create 3 independent 44 subjects groups. Two of the groups underwent a simulated left/right Kocher-Monro trajectory and the third was kept as a control group. For the right Kocher-Monro approach, the nodal analysis revealed decreased strength in the anterior cingulate gyrus of the transected hemisphere. The network-based statistic analysis revealed a set of right lateralized subnetworks with decreased connectivity strength that is consistent with a subset of the Default Mode Network, Salience Network, and Cingulo-Opercular Network. These findings could allow for a better understanding of structural alterations caused by Kocher-Monro approaches that could reveal previously undetected clinical alterations and inform the process of designing safer and less invasive cerebral ventricular approaches.

Klíčová slova:

Central nervous system – Clustering coefficients – Magnetic resonance imaging – Neural networks – Neuroimaging – Surgical and invasive medical procedures – Tractography – Ventriculostomy


Zdroje

1. Muralidharan R. External ventricular drains: Management and complications. Surgical neurology international. 2015;6(Suppl 6):S271–4. doi: 10.4103/2152-7806.157620 26069848

2. Lovasik BP, McCracken DJ, McCracken CE, McDougal ME, Frerich JM, Samuels OB, et al. The Effect of External Ventricular Drain Use in Intracerebral Hemorrhage. World neurosurgery. 2016;94:309–318. doi: 10.1016/j.wneu.2016.07.022 27436212

3. Roitberg BZ, Khan N, Alp MS, Hersonskey T, Charbel FT, Ausman JI. Bedside external ventricular drain placement for the treatment of acute hydrocephalus. British journal of neurosurgery. 2001;15(4):324–327. doi: 10.1080/02688690120072478 11599448

4. Abdoh MG, Bekaert O, Hodel J, Diarra SM, Le Guerinel C, Nseir R, et al. Accuracy of external ventricular drainage catheter placement. Acta neurochirurgica. 2012;154(1):153–9. doi: 10.1007/s00701-011-1136-9 21892637

5. Kakarla UK, Kim LJ, Chang SW, Theodore N, Spetzler RF. Safety and accuracy of bedside external ventricular drain placement. Neurosurgery. 2008;63(1 Suppl 1):ONS162–6; discussion ONS166–7.

6. Ghajar JB. A guide for ventricular catheter placement. Technical note. Journal of neurosurgery. 1985;63(6):985–6. doi: 10.3171/jns.1985.63.6.0985 4056916

7. Schaumann A, Thomale UW. Guided Application of Ventricular Catheters (GAVCA)–multicentre study to compare the ventricular catheter position after use of a catheter guide versus freehand application: study protocol for a randomised trail. Trials. 2013;14(1):428. doi: 10.1186/1745-6215-14-428

8. Srinivasan VM, O’Neill BR, Jho D, Whiting DM, Oh MY. The history of external ventricular drainage. Journal of neurosurgery. 2014;120(1):228–36. doi: 10.3171/2013.6.JNS121577 23889138

9. García S, Rincon-Torroella J, Benet A, Oleaga L, González Sánchez JJ. Assessment of White Matter Transgression During Neuroendoscopic Procedures Using Diffusion Tensor Image Fiber Tracking. World neurosurgery. 2017;99:232–240. doi: 10.1016/j.wneu.2016.11.112 27915065

10. Kwon HG, Jang SH. Cingulum injury by external ventricular drainage procedure: diffusion tensor tractography study. Clinical neuroradiology. 2015;25(1):65–7. doi: 10.1007/s00062-013-0269-z 24221532

11. Horn A, Ostwald D, Reisert M, Blankenburg F. The structural-functional connectome and the default mode network of the human brain. NeuroImage. 2014;102 Pt 1:142–51. doi: 10.1016/j.neuroimage.2013.09.069 24099851

12. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(2):676–82. doi: 10.1073/pnas.98.2.676 11209064

13. Reess TJ, Rus OG, Schmidt R, de Reus MA, Zaudig M, Wagner G, et al. Connectomics-based structural network alterations in obsessive-compulsive disorder. Translational psychiatry. 2016;6(9):e882. doi: 10.1038/tp.2016.163 27598966

14. van den Heuvel MP, Bullmore ET, Sporns O. Comparative Connectomics. Trends in Cognitive Sciences. 2016; 20(5), 345–361. doi: 10.1016/j.tics.2016.03.001 27026480

15. Sporns O, Tononi G, Kötter R. The human connectome: A structural description of the human brain. PLoS computational biology. 2005;1(4):e42. doi: 10.1371/journal.pcbi.0010042 16201007

16. Toga AW, Clark KA, Thompson PM, Shattuck DW, Van Horn JD. Mapping the human connectome. Neurosurgery. 2012;71(1):1–5. doi: 10.1227/NEU.0b013e318258e9ff 22705717

17. Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends in cognitive sciences. 2011;15(10):483–506. doi: 10.1016/j.tics.2011.08.003 21908230

18. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage. 2016;125:1063–1078. doi: 10.1016/j.neuroimage.2015.10.019 26481672

19. Vu ATT, Auerbach E, Lenglet C, Moeller S, Sotiropoulos SNN, Jbabdi S, et al. High resolution whole brain diffusion imaging at 7T for the Human Connectome Project. NeuroImage. 2015;122:318–31. doi: 10.1016/j.neuroimage.2015.08.004 26260428

20. Sotiropoulos SN, Hernández-Fernández M, Vu AT, Andersson JL, Moeller S, Yacoub E, et al. Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project. NeuroImage. 2016;134:396–409. doi: 10.1016/j.neuroimage.2016.04.014 27071694

21. Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, et al. The Human Connectome Project: a data acquisition perspective. NeuroImage. 2012; 62(4), 2222–2231. https://doi.org/10.1016/j.neuroimage.2012.02.018

22. Fischl B. FreeSurfer. NeuroImage. 2012; 62(2), 774–781. doi: 10.1016/j.neuroimage.2012.01.021 22248573

23. Grandhi R, Okonkwo DO. Perioperative Management of Severe Traumatic Brain Injury in Adults. Schmidek and Sweet Operative Neurosurgical Techniques: Indications, Methods, and Results: Sixth Edition. 2012;2:1495–1512. doi: 10.1016/B978-1-4160-6839-6.10132-7

24. Van Rossum G, Drake FL. The Python language reference manual: for Python version 3.2. Network Theory Ltd; 2011. Available from: https://ir.cwi.nl/pub/5008/05008D.pdf.

25. Tournier JD, Calamante F, Connelly A. MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology. 2012;22(1):53–66. doi: 10.1002/ima.22005

26. Tournier JD, Calamante F, Connelly A. Improved probabilistic streamlines tractography by 2 nd order integration over fibre orientation distributions. Ismrm. 2010;88(2003):2010.

27. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cerebral cortex (New York, NY: 1991). 2004;14(1):11–22.

28. Calamante F, Tournier J, Jackson GD, Connelly A. Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping. Neuroimage. 2010;53(4):1233–43. http://dx.doi.org/10.1016/j.neuroimage.2010.07.02420643215

29. Calamante F. Track-weighted imaging methods: extracting information from a streamlines tractogram. MAGMA. 2017 Aug;30(4):317–35. http://www.ncbi.nlm.nih.gov/pubmed/2818102728181027

30. Willats L, Raffelt D, Smith RE, Tournier JD, Connelly A, Calamante F. Quantification of track-weighted imaging (TWI): Characterisation of within-subject reproducibility and between-subject variability. Neuroimage. 2014;87:18–31. http://dx.doi.org/10.1016/j.neuroimage.2013.11.01624246491

31. Atasoy S, Donnelly I, Pearson J. Human brain networks function in connectome-specific harmonic waves. Nature communications. 2016;7:10340. doi: 10.1038/ncomms10340 26792267

32. Collin G, Scholtens LH, Kahn RS, Hillegers MHJ, van den Heuvel MP. Affected Anatomical Rich Club and Structural-Functional Coupling in Young Offspring of Schizophrenia and Bipolar Disorder Patients. Biological psychiatry. 2017;82(10):746–755. doi: 10.1016/j.biopsych.2017.06.013 28734460

33. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR in biomedicine. 2010;23(7):803–20. doi: 10.1002/nbm.1543 20886566

34. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS biology. 2008;6(7):e159. doi: 10.1371/journal.pbio.0060159 18597554

35. Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, et al. Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America. 2009;106(6):2035–40. doi: 10.1073/pnas.0811168106 19188601

36. Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS computational biology. 2007;3(2):e17. doi: 10.1371/journal.pcbi.0030017 17274684

37. Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. NeuroImage. 2010;53(4):1197–207. doi: 10.1016/j.neuroimage.2010.06.041 20600983

38. Civier O, Smith RE, Yeh CH., Connelly A, Calamante F. Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI? NeuroImage. 2019; 194(March): 68–81. doi: 10.1016/j.neuroimage.2019.02.039 30844506

39. Uğurbil K, Xu J, Auerbach EJ, Moeller S, Vu AT, Duarte-Carvajalino JM, et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. NeuroImage. 2013;80:80–104. doi: 10.1016/j.neuroimage.2013.05.012

40. Spreng RN. The Fallacy of a “Task-Negative”Network. Frontiers in Psychology. 2012;3(MAY):1–5.

41. Sethi A, Sarkar S, Dell’Acqua F, Viding E, Catani M, Murphy DGM, et al. Anatomy of the dorsal default-mode network in conduct disorder: Association with callous-unemotional traits. Developmental cognitive neuroscience. 2018;30:87–92. doi: 10.1016/j.dcn.2018.01.004 29413533

42. Brown CA, Jiang Y, Smith CD, Gold BT. Age and Alzheimer’s pathology disrupt default mode network functioning via alterations in white matter microstructure but not hyperintensities. Cortex; a journal devoted to the study of the nervous system and behavior. 2018;104:58–74. doi: 10.1016/j.cortex.2018.04.006

43. Lin P, Yang Y, Gao J, De Pisapia N, Ge S, Wang X, et al. Dynamic Default Mode Network across Different Brain States. Scientific reports. 2017;7(March):46088. doi: 10.1038/srep46088 28382944

44. Buckner RL, Andrews-Hanna JR., Schacter DL. Brain’s Default Network. Annals of the New York Academy of Sciences. 2008; 1124(1); 1–38. doi: 10.1196/annals.1440.011

45. Whitton AE, Webb CA, Dillon DG, Kayser J, Rutherford A, Goer F, et al. Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial. Biol Psychiatry. 2019;85(10):872–80. https://doi.org/10.1016/j.biopsych.2018.12.00730718038

46. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010 Jun;214(5–6):655–67. http://www.ncbi.nlm.nih.gov/pubmed/2051237020512370

47. Sadaghiani S, D’Esposito M. Functional characterization of the cingulo-opercular network in the maintenance of tonic alertness. Cereb Cortex. 2015;25(9):2763–73. https://doi.org/10.1093/cercor/bhu07224770711

48. Glasser MF, Coalson TS, Robinson EC, Hacker CD, Harwell J, Yacoub E, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536(7615):171–178. doi: 10.1038/nature18933 27437579

49. Kong XZ, Mathias SR, Guadalupe T, ENIGMA Laterality Working Group, Glahn DC, Franke B, et al. Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(22):E5154–E5163. doi: 10.1073/pnas.1718418115 29764998

50. Sotiropoulos SN, Zalesky A. Building connectomes using diffusion MRI: why, how and but. NMR in Biomedicine. 2017; p. e3752. doi: 10.1002/nbm.3752 28654718

51. Bassett DS, Brown JA, Deshpande V, Carlson JM, Grafton ST. Conserved and variable architecture of human white matter connectivity. NeuroImage. 2011;54(2):1262–79. doi: 10.1016/j.neuroimage.2010.09.006 20850551

52. Thomas C, Ye FQ, Irfanoglu MO, Modi P, Saleem KS, Leopold DA, et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proceedings of the National Academy of Sciences of the United States of America. 2014;111(46):16574–9. doi: 10.1073/pnas.1405672111 25368179

53. Dauguet J, Peled S, Berezovskii V, Delzescaux T, Warfield SK, Born R, et al. Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain. NeuroImage. 2007;37(2):530–8. doi: 10.1016/j.neuroimage.2007.04.067 17604650

54. O’Leary ST, Kole MK, Hoover Da, Hysell SE, Thomas A, Shaffrey CI. Efficacy of the Ghajar Guide revisited: a prospective study. Journal of neurosurgery. 2000;92(5):801–803. doi: 10.3171/jns.2000.92.5.0801 10794294


Článek vyšel v časopise

PLOS One


2019 Číslo 11