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Carotid artery stenosis and brain connectivity: the role of white matter hyperintensities

  • Michele PorcuEmail author
  • Paolo Garofalo
  • Davide Craboledda
  • Jasjit S. Suri
  • Harman S. Suri
  • Roberto Montisci
  • Roberto Sanfilippo
  • Luca Saba
Functional Neuroradiology

Abstract

Purpose

It is under debate how white matter hyperintensities (WMH) affects the brain connectivity. The objective of this research study is to validate the hypothesis, if and how the WMH influences brain connectivity in a population with carotid artery stenosis (CAS), which are eligible for carotid endarterectomy (CEA). We used resting state functional connectivity (rs-fc) magnetic resonance (MR) to validate our hypothesis, focusing on the effects of the total number of WMH (TNWMH) and of the WMH Burden (WMHB).

Methods

Twenty-three patients (sixteen males and seven females, mean age 74.34 years) with mono or bilateral carotid stenosis eligible for carotid endarterectomy (CEA), underwent an MR examination on a 1.5-T scanner. The protocol included a morphologic T1-3D isotropic, an EPI functional sequence for rs-fc MR analysis, and a 3D isotropic FLAIR sequence. For each patient, the TNWMH and the WMHB were obtained using two online tools—volBrain and lesionBrain. The rs-fc region-of-interest to region-of-interest (ROI-to-ROI) analysis was performed with the CONN toolbox v18a: two different multiple regression analyses including both WMHB and TNWMH as second-level covariates evaluated the individual effects of WMHB (Analysis A) and TNWMH (Analysis B), adopting a p value corrected for false discovery rate (p-FDR) < 0.05 to identify statistically significant values.

Results

Both analyses A and B identified several statistically significant positive and negative correlations associated with WMHB and TNWMH.

Conclusion

WMH influence functional connectivity in patients with carotid artery stenosis eligible for CEA; further, WMHB and TNWMH influence differently functional connectivity.

Keywords

Carotid endarterectomy Graph theory rs-fcMR White matter hyperintensities 

Notes

Funding information

No funding was received for this study.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Béjot Y, Bailly H, Durier J, Giroud M (2016) Epidemiology of stroke in Europe and trends for the 21st century. Presse Med 45(12 Pt 2):e391–e398.  https://doi.org/10.1016/j.lpm.2016.10.003 CrossRefPubMedGoogle Scholar
  2. 2.
    Saba L, Yuan C, Hatsukami TS, Balu N, Qiao Y, DeMarco JK, Saam T, Moody AR, Li D, Matouk CC, Johnson MH, Jäger HR, Mossa-Basha M, Kooi ME, Fan Z, Saloner D, Wintermark M, Mikulis DJ, Wasserman BA, Vessel Wall Imaging Study Group of the American Society of Neuroradiology (2018) Carotid artery wall imaging: perspective and guidelines from the ASNR Vessel Wall Imaging Study Group and Expert Consensus Recommendations of the American Society of Neuroradiology. AJNR Am J Neuroradiol 39(2):E9–E31.  https://doi.org/10.3174/ajnr.A5488 CrossRefPubMedGoogle Scholar
  3. 3.
    Flaherty ML, Kissela B, Khoury JC, Alwell K, Moomaw CJ, Woo D, Khatri P, Ferioli S, Adeoye O, Broderick JP, Kleindorfer D (2012) Carotid artery stenosis as a cause of stroke. Neuroepidemiology 40(1):36–41CrossRefGoogle Scholar
  4. 4.
    Aboyans V, Ricco JB, Bartelink MEL, Björck M, Brodmann M, Cohnert T, Collet JP, Czerny M, De Carlo M, Debus S, Espinola-Klein C, Kahan T, Kownator S, Mazzolai L, Naylor AR, Roffi M, Röther J, Sprynger M, Tendera M, Tepe G, Venermo M, Vlachopoulos C, Desormais I, ESC Scientific Document Group (2017) 2017 ESC guidelines on the diagnosis and treatment of peripheral arterial diseases, in collaboration with the European Society for Vascular Surgery (ESVS): document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteries endorsed by: the European stroke organization (ESO) the task force for the diagnosis and treatment of peripheral arterial diseases of the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS). Eur Heart J.  https://doi.org/10.1093/eurheartj/ehx095 CrossRefGoogle Scholar
  5. 5.
    Wang T, Mei B, Zhang J (2016) Atherosclerotic carotid stenosis and cognitive function. Clin Neurol Neurosurg 146:64–70.  https://doi.org/10.1016/j.clineuro.2016.03.027 ReviewCrossRefPubMedGoogle Scholar
  6. 6.
    Dutra AP (2012) Cognitive function and carotid stenosis: review of the literature. Dement Neuropsychol 6(3):127–130CrossRefGoogle Scholar
  7. 7.
    Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, Lindley RI, O'Brien JT, Barkhof F, Benavente OR, Black SE, Brayne C, Breteler M, Chabriat H, Decarli C, de Leeuw FE, Doubal F, Duering M, Fox NC, Greenberg S, Hachinski V, Kilimann I, Mok V, Oostenbrugge Rv, Pantoni L, Speck O, Stephan BC, Teipel S, Viswanathan A, Werring D, Chen C, Smith C, van Buchem M, Norrving B, Gorelick PB, Dichgans M, STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1) (2013) Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 12(8):822–838.  https://doi.org/10.1016/S1474-4422(13)70124-8 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Debette S, Markus HS (2010) The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341:c3666. Published 2010 Jul 26.  https://doi.org/10.1136/bmj.c3666 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Wardlaw JM, Valdés Hernández MC, Muñoz-Maniega S (2015) What are white matter hyperintensities made of? Relevance to vascular cognitive impairment. J Am Heart Assoc 4(6):001140. Published 2015 Jun 23.  https://doi.org/10.1161/JAHA.114.001140 CrossRefPubMedGoogle Scholar
  10. 10.
    Fernando MS, Simpson JE, Matthews F, Brayne C, Lewis CE, Barber R, Kalaria RN, Forster G, Esteves F, Wharton SB, Shaw PJ, O’Brien JT, Ince PG (2006) White matter lesions in an unselected cohort of the elderly: molecular pathology suggests origin from chronic hypoperfusion injury. Stroke 37:1391–1398CrossRefGoogle Scholar
  11. 11.
    Morris Z, Whiteley WN, Longstreth WT Jr, Weber F, Lee YC, Tsushima Y, Alphs H, Ladd SC, Warlow C, Wardlaw JM, Al-Shahi SR (2009) Incidental findings on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 339:b3016CrossRefGoogle Scholar
  12. 12.
    de Groot JC, de Leeuw FE, Oudkerk M, van Gijn J, Hofman A, Jolles J, Breteler MM (2000) Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann Neurol 47(2):145–151CrossRefGoogle Scholar
  13. 13.
    Saba L, Raz E, Grassi R, Di Paolo PL, Iacomino A, Montisci R, Piga M (2013) Association between the volume of carotid artery plaque and its subcomponents and the volume of white matter lesions in patients selected for endarterectomy. AJR Am J Roentgenol 201(5):W747–W752.  https://doi.org/10.2214/AJR.12.10217 CrossRefPubMedGoogle Scholar
  14. 14.
    Saba L, Sanfilippo R, Porcu M, Lucatelli P, Montisci R, Zaccagna F, Suri JS, Anzidei M, Wintermark M (2017) Relationship between white matter hyperintensities volume and the circle of Willis configurations in patients with carotid artery pathology. Eur J Radiol 89:111–116.  https://doi.org/10.1016/j.ejrad.2017.01.031 CrossRefPubMedGoogle Scholar
  15. 15.
    Ye H, Wang Y, Qiu J, Wu Q, Xu M, Wang J (2018) White matter hyperintensities and their subtypes in patients with carotid artery stenosis: a systematic review and meta-analysis. BMJ Open 8(5):e020830. Published 2018 May 16.  https://doi.org/10.1136/bmjopen-2017-020830 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Saba L, Saam T, Jäger HR, Yuan C, Hatsukami TS, Saloner D, Wasserman BA, Bonati LH, Wintermark M (2019) Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications. Lancet Neurol 18(6):559–572.  https://doi.org/10.1016/S1474-4422(19)30035-3 ReviewCrossRefPubMedGoogle Scholar
  17. 17.
    Kandiah N, Goh O, Mak E, Marmin M, Ng A (2014) Carotid stenosis: a risk factor for cerebral white-matter disease. J Stroke Cerebrovasc Dis 23(1):136–139.  https://doi.org/10.1016/j.jstrokecerebrovasdis.2012.11.007 CrossRefPubMedGoogle Scholar
  18. 18.
    Buchbinder BR (2016) Functional magnetic resonance imaging. Handb Clin Neurol 135:61–92.  https://doi.org/10.1016/B978-0-444-53485-9.00004-0 ReviewCrossRefPubMedGoogle Scholar
  19. 19.
    Reijmer YD, Schultz AP, Leemans A, O'Sullivan MJ, Gurol ME, Sperling R, Greenberg SM, Viswanathan A, Hedden T (2015) Decoupling of structural and functional brain connectivity in older adults with white matter hyperintensities. Neuroimage 117:222–229CrossRefGoogle Scholar
  20. 20.
    De Marco M, Manca R, Mitolo M, Venneri A (2017) White matter hyperintensity load modulates brain morphometry and brain connectivity in healthy adults: a neuroplastic mechanism? Neural Plast 2017:4050536CrossRefGoogle Scholar
  21. 21.
    Benson G, Hildebrandt A, Lange C et al (2018) Functional connectivity in cognitive control networks mitigates the impact of white matter lesions in the elderly. Alzheimers Res Ther 10(1):109. Published 2018 Oct 27.  https://doi.org/10.1186/s13195-018-0434-3 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Donnan GA, Davis SM, Chambers BR, Gates PC (1998) Surgery for prevention of stroke. Lancet 351(9113):1372–1373CrossRefGoogle Scholar
  23. 23.
    Aboyans V, Ricco JB, Bartelink MEL, Björck M, Brodmann M, Cohnert T, Collet JP, Czerny M, De Carlo M, Debus S, Espinola-Klein C, Kahan T, Kownator S, Mazzolai L, Naylor AR, Roffi M, Röther J, Sprynger M, Tendera M, Tepe G, Venermo M, Vlachopoulos C, Desormais I, Reviewers D, Widimsky P, Kolh P, Agewall S, Bueno H, Coca A, De Borst GJ, Delgado V, Dick F, Erol C, Ferrini M, Kakkos S, Katus HA, Knuuti J, Lindholt J, Mattle H, Pieniazek P, Piepoli MF, Scheinert D, Sievert H, Simpson I, Sulzenko J, Tamargo J, Tokgozoglu L, Torbicki A, Tsakountakis N, Tuñón J, de Ceniga MV, Windecker S, Zamorano JL (2018) Editor’s choice - 2017 ESC guidelines on the diagnosis and treatment of peripheral arterial diseases, in collaboration with the European Society for Vascular Surgery (ESVS). Eur J Vasc Endovasc Surg 55(3):305–368.  https://doi.org/10.1016/j.ejvs.2017.07.018 CrossRefPubMedGoogle Scholar
  24. 24.
    van Swieten JC, Koudstaal PJ, Visser MC, Schouten HJ, van Gijn J (1988) Interobserver agreement for the assessment of handicap in stroke patients. Stroke 19(5):604–607CrossRefGoogle Scholar
  25. 25.
    Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198CrossRefGoogle Scholar
  26. 26.
    Magni E, Binetti G, Bianchetti A, Rozzini R, Trabucchi M (1996) Mini-mental state examination: a normative study in Italian elderly population. Eur J Neurol 3(3):198–202.  https://doi.org/10.1111/j.1468-1331.1996.tb00423.x CrossRefPubMedGoogle Scholar
  27. 27.
    Manjón JV, Coupé P (2016) volBrain: an online MRI brain volumetry system. Front Neuroinform 10:30.  https://doi.org/10.3389/fninf.2016.00030 eCollection 2016CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Coupé P, Tourdias T, Linck P, Romero JE, Manjón JV. LesionBrain: an online tool for white matter lesion segmentation. Patched-based techniques in medical imaging - 4th International Workshop, Patch-MI 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings; September 2018.  https://doi.org/10.1007/978-3-030-00500-9_11 CrossRefGoogle Scholar
  29. 29.
    Whitfield-Gabrieli S, Nieto-Castanon A (2012) Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2(3):125–141.  https://doi.org/10.1089/brain.2012.0073 CrossRefPubMedGoogle Scholar
  30. 30.
    Porcu M, Craboledda D, Garofalo P, Barberini L, Sanfilippo R, Zaccagna F, Wintermark M, Montisci R, Saba L (2019) Reorganization of brain networks following carotid endarterectomy: an exploratory study using resting state functional connectivity with a focus on the changes in default mode network connectivity. Eur J Radiol 110:233–241.  https://doi.org/10.1016/j.ejrad.2018.12.007 CrossRefPubMedGoogle Scholar
  31. 31.
    Qi R, Zhang L, Wu S, Zhong J, Zhang Z, Zhong Y, Ni L, Zhang Z, Li K, Jiao Q, Wu X, Fan X, Liu Y, Lu G (2012) Altered resting-state brain activity at functional MR imaging during the progression of hepatic encephalopathy. Radiology 264(1):187–195.  https://doi.org/10.1148/radiol.12111429 CrossRefPubMedGoogle Scholar
  32. 32.
    Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS, Faraone SV, Tsuang MT, Seidman LJ (2006) Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr Res 83(2–3):155–171CrossRefGoogle Scholar
  33. 33.
    Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, Herbert MR, Bent EK, Koneru VK, Dieterich ME, Hodge SM, Rauch SL, Grant PE, Cohen BM, Seidman LJ, Caviness VS, Biederman J (2005) Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am J Psychiatry 162(7):1256–1265CrossRefGoogle Scholar
  34. 34.
    Desikan RS, SÈgonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3):968–980CrossRefGoogle Scholar
  35. 35.
    Goldstein JM, Seidman LJ, Makris N, Ahern T, O'Brien LM, Caviness VS Jr, Kennedy DN, Faraone SV, Tsuang MT (2007) Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol Psychiatry 61(8):935–945CrossRefGoogle Scholar
  36. 36.
    Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15:273–289CrossRefGoogle Scholar
  37. 37.
    Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198.  https://doi.org/10.1038/nrn2575 Review. Erratum in: Nat Rev Neurosci. 2009 Apr;10(4):312CrossRefPubMedGoogle Scholar
  38. 38.
    Bassett DS, Zurn P, Gold JI (2018) On the nature and use of models in network neuroscience. Nat Rev Neurosci 19(9):566–578.  https://doi.org/10.1038/s41583-018-0038-8 ReviewCrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Koelsch S, Skouras S, Lohmann G (2018) The auditory cortex hosts network nodes influential for emotion processing: an fMRI study on music-evoked fear and joy. PLoS One 13(1):e0190057. Published 2018 Jan 31.  https://doi.org/10.1371/journal.pone.0190057 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Stoodley CJ, Valera EM, Schmahmann JD (2012) Functional topography of the cerebellum for motor and cognitive tasks: an fMRI study. Neuroimage 59(2):1560–1570.  https://doi.org/10.1016/j.neuroimage.2011.08.065 CrossRefPubMedGoogle Scholar
  41. 41.
    Salgado S, Kaplitt MG (2015) The nucleus accumbens: a comprehensive review. Stereotact Funct Neurosurg 93(2):75–93CrossRefGoogle Scholar
  42. 42.
    Carta MG, Lecca ME, Saba L et al (2015) Patients with carotid atherosclerosis who underwent or did not undergo carotid endarterectomy: outcome on mood, cognition and quality of life. BMC Psychiatry 15:277. Published 2015 Nov 12.  https://doi.org/10.1186/s12888-015-0663-y CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Naumov NG (2017) Reactive changes to astrocytes in the nucleus accumbens of the forebrain after restriction of blood flow in the basins of both common carotid arteries in rats. Neurosci Behav Physiol 47:7.  https://doi.org/10.1007/s11055-016-0359-x CrossRefGoogle Scholar
  44. 44.
    Utevsky AV, Smith DV, Huettel SA (2014) Precuneus is a functional core of the default-mode network. J Neurosci 34(3):932–940CrossRefGoogle Scholar
  45. 45.
    Cavanna AE, Trimble MR (2006) The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129(Pt 3):564–583 ReviewCrossRefGoogle Scholar
  46. 46.
    Weiner KS, Zilles K (2016) The anatomical and functional specialization of the fusiform gyrus. Neuropsychologia 83:48–62.  https://doi.org/10.1016/j.neuropsychologia.2015.06.033 CrossRefPubMedGoogle Scholar
  47. 47.
    Ding J, Chen K, Chen Y et al (2016) The left fusiform gyrus is a critical region contributing to the core behavioral profile of semantic dementia. Front Hum Neurosci 10:215. Published 2016 May 19.  https://doi.org/10.3389/fnhum.2016.00215 CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Saba L, Pascalis L, Sanfilippo R, Anzidei M, Bura R, Montisci R, Mallarini G (2011) Carotid artery wall thickness and leukoaraiosis: preliminary results using multidetector row CT angiography. AJNR Am J Neuroradiol 32(5):955–961.  https://doi.org/10.3174/ajnr.A2396 CrossRefPubMedGoogle Scholar
  49. 49.
    Saba L, Sanfilippo R, Pascalis L, Montisci R, Mallarini G (2009) Carotid artery abnormalities and leukoaraiosis in elderly patients: evaluation with MDCT. AJR Am J Roentgenol 192(2):W63–W70.  https://doi.org/10.2214/AJR.07.3566 CrossRefPubMedGoogle Scholar
  50. 50.
    Voss HU, Zevin JD, McCandliss BD (2006) Functional MR imaging at 3.0 T versus 1.5 T: a practical review. Neuroimaging Clin N Am 16(2):285–297.  https://doi.org/10.1016/j.nic.2006.02.008 CrossRefPubMedGoogle Scholar
  51. 51.
    Langen CD, Zonneveld HI, White T, Huizinga W, Cremers LGM, de Groot M, Ikram MA, Niessen WJ, Vernooij MW (2017) White matter lesions relate to tract-specific reductions in functional connectivity. Neurobiol Aging 51:97–103.  https://doi.org/10.1016/j.neurobiolaging.2016.12.004 CrossRefPubMedGoogle Scholar
  52. 52.
    Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, Cummings JL, Chertkow H (2005) The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53(4):695–699CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Medical Imaging, AOU of CagliariUniversity of CagliariMonserrato (Cagliari)Italy
  2. 2.Department of Vascular Surgery, AOU of CagliariUniversity of CagliariMonserrato (Cagliari)Italy
  3. 3.Diagnostic and Monitoring DivisionRosevilleUSA
  4. 4.Brown UniversityProvidenceUSA

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