Relations of impaired blood flow and cerebrospinal fluid flow with damage of strategic for cognitive impairment brain regiones in cerebral small vessel disease

Abstract

Introduction. Cerebral small vessel disease (CSVD), associated with age and vascular risk factors, as well as the main cause of vascular and degenerative mixed cognitive impairment (CI). Previously established microstructural predictors of CI (axial diffusion in normal-appearing periventricular white matter of the posterior left frontal lobe, the right midcingulate cortex, and the middle posterior part of the corpus callosum) can be used to calculate an integrative factor, exceeding the threshold value for which indicates the presence of CI. The use of this factor in the diagnosis of CI in CSVD is supported by the fact that leading mechanisms of CSVD are involved in the damage to areas of the brain that are strategic for CI.

The aim of this study was to clarify the link between the known microstructural predictors of CI in CSVD and MRI findings that correspond to the main mechanisms of CSVD.

Materials and methods. Patients (n = 74; including 48 women; average age 60.6 ± 6.9 years) with CSVD and CI of varying severity underwent phase-contrast MRI and voxel-based morphometry (3T) to assess arterial, venous and CSF flow, as well as atrophy.

Results. The established microstructural predictors of CI correlated with measures of arterial and venous blood flow, as well as atrophy. Linear regression models allow us to estimate cognitive impairment (CI) predictors in cerebral small vessel disease (CSVD), based on increased arterial velocity pulse index, CSF flow at the level of the cerebral aqueduct, cerebral aqueduct area and lateral ventricles volume, when there is reduced blood flow in the superior sagittal sinus and the overall arterial blood flow.

Conclusion. The ability to calculate microstructural predictors of CI due to CSVD, based on MRI findings, indicates the validity of using an integrative measure of microstructural predictors of CI as a diagnostic tool of CI in CSVD.

About the authors

Larisa A. Dobrynina

Research Center of Neurology

Author for correspondence.
Email: dobrla@mail.ru
ORCID iD: 0000-0001-9929-2725

D. Sci. (Med.), Head, 3rd Neurological department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Zukhra Sh. Gadzhieva

Research Center of Neurology

Email: zuhradoc@mail.ru
ORCID iD: 0000-0001-7498-4063

Cand. Sci. (Med.), neurologist, 3rd Neurological department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Kamila V. Shamtieva

Research Center of Neurology

Email: dobrla@mail.ru
ORCID iD: 0000-0002-6995-1352

junior researcher, 3rd Neurological department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Elena I. Kremneva

Research Center of Neurology

Email: kremneva@neurology.ru
ORCID iD: 0000-0001-9396-6063

Cand. Sci. (Med.), radiologist, senior researcher, Neuroradiology department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Bulat M. Akhmetzyanov

Research Center of Neurology

Email: dobrla@mail.ru
ORCID iD: 0000-0003-4461-3338

radiologist

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Mariya M. Tsypushtanova

Research Center of Neurology

Email: lagoda.d@neurology.ru
ORCID iD: 0000-0002-4231-3895

neurologist, 3rd Neurological department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Angelina G. Makarova

Research Center of Neurology

Email: angelinagm@mail.ru
ORCID iD: 0000-0001-8862-654X

neurologist, 3rd Neurological department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Victoriya V. Trubitsyna

Research Center of Neurology

Email: pobeda-1994@mail.ru
ORCID iD: 0000-0001-7898-6541

radiologist, Neuroradiology department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

Marina V. Krotenkova

Research Center of Neurology

Email: kattorina@list.ru
ORCID iD: 0000-0003-3820-4554

D. Sci. (Med.), Head, Neuroradiology department

Russian Federation, 125367, Moscow, Volokolamskoye shosse, 80

References

  1. Gorelick P.B., Scuteri A., Black S.E. et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association / American Stroke Association. Stroke. 2011; 42(9): 2672–2713. doi: 10.1161/STR.0b013e3182299496
  2. Deramecourt V., Slade J.Y., Oakley A.E. et al. Staging and natural history of cerebrovascular pathology in dementia. Neurology. 2012; 78(14): 1043–1050. doi: 10.1212/WNL.0b013e31824e8e7f
  3. Wardlaw J.M., Smith C., Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 2013; 12(5): 483–497. doi: 10.1016/S1474-4422(13)70060-7
  4. Livingston G., Sommerlad A., Orgeta V. et al. Dementia prevention, intervention, and care. Lancet. 2017; 390(10113): 2673–2734. doi: 10.1016/S0140-6736(17)31363-6
  5. Azarpazhooh M.R., Avan A., Cipriano L.E. et al. Concomitant vascular and neurodegenerative pathologies double the risk of dementia. Alzheimers Dement. 2018; 14(2): 148–156. doi: 10.1016/j.jalz.2017.07.755
  6. Smith E.E., Beaudin A.E. New insights into cerebral small vessel disease and vascular cognitive impairment from MRI. Curr. Opin. Neurol. 2018; 31(1): 36–43. doi: 10.1097/WCO.0000000000000513
  7. Wardlaw J.M., Smith E.E., Biessels G.J. et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013; 12(8): 822–838. doi: 10.1016/S1474-4422(13)70124-8
  8. Schmidt R., Berghold A., Jokinen H. et al. White matter lesion progression in LADIS: frequency, clinical effects, and sample size calculations. Stroke. 2012; 43(10): 2643–2647. doi: 10.1161/STROKEAHA.112.662593
  9. Pantoni L., Fierini F., Poggesi A., LADIS Study Group. Impact of cerebral white matter changes on functionality in older adults: An overview of the LADIS Study results and future directions. Geriatr. Gerontol. Int. 2015; 15(Suppl 1): 10–16. doi: 10.1111/ggi.12665. PMID: 26671152.
  10. Добрынина Л.А., Гаджиева З.Ш., Калашникова Л.А. и др. Нейропсихологический профиль и факторы сосудистого риска у больных с церебральной микроангиопатией. Анналы клинической и экспериментальной неврологии. 2018; 12(4): 5–15. Dobrynina L.A., Gadzhieva Z.Sh., Kalashnikova L.A. et al. Neuropsychological profile and vascular risk factors in patients with cerebral microangiopathy. Annals of clinical and experimental neurology. 2018; 12(4): 5–15. (In Russ.) doi: 10.25692/ACEN.2018.4.1
  11. Pasi M., van Uden I.W., Tuladhar A.M. et al. White matter microstructural damage on diffusion tensor imaging in cerebral small vessel disease: clinical consequences. Stroke. 2016; 47(6): 1679–1684. doi: 10.1161/STROKEAHA.115.012065
  12. Raja R., Rosenberg G., Caprihan A. Review of diffusion MRI studies in chronic white matter diseases. Neurosci. Lett. 2019; 694: 198–207. doi: 10.1016/j.neulet.2018.12.007
  13. Lawrence A.J., Brookes R.L., Zeestraten E.A. et al. Pattern and rate of cognitive decline in cerebral small vessel disease: a prospective study. PLoS One. 2015; 10(8): e0135523. doi: 10.1371/journal.pone.0135523
  14. Benjamin P., Zeestraten E., Lambert C. et al. Progression of MRI markers in cerebral small vessel disease: Sample size considerations for clinical trials. J. Cereb. Blood Flow Metab. 2016; 36(1): 228–240. doi: 10.1038/jcbfm.2015.113
  15. Гнедовская Е.В., Добрынина Л.А., Кротенкова М.В., Сергеева А.Н. МРТ в оценке прогрессирования церебральной микроангиопатии. Анналы клинической и экспериментальной неврологии. 2018; 12(1): 61–68. Gnedovskaya E.V., Dobrynina L.A., Krotenkova M.V., Sergeeva A.N. MRI in the assessment of cerebral small vessel disease. Annals of clinical and experimental neurology. 2018; 12(1): 61–68. (In Russ.) doi: 10.25692/ACEN.2018.1.9
  16. Pasi M., Salvadori E., Poggesi A. et al. White matter microstructural damage in small vessel disease is associated with Montreal cognitive assessment but not with Mini Mental State Examination performances: vascular mild cognitive impairment Tuscany study. Stroke. 2015; 46(1): 262–264. doi: 10.1161/STROKEAHA.114.007553
  17. O’Sullivan M., Morris R.G., Huckstep B. et al. Diffusion tensor MRI correlates with executive dysfunction in patients with ischaemic leukoaraiosis. J. Neurol. Neurosurg. Psychiatry. 2004; 75(3): 441–447. doi: 10.1136/jnnp.2003.014910
  18. Nitkunan A., Barrick T.R., Charlton R.A. et al. Multimodal MRI in cerebral small vessel disease: its relationship with cognition and sensitivity to change over time. Stroke. 2008; 39(7): 1999–2005. doi: 10.1161/STROKEAHA.107.507475
  19. Tuladhar A.M., van Norden A.G., de Laat K.F. et al. White matter integrity in small vessel disease is related to cognition. Neuroimage Clin. 2015; 7: 518–524. doi: 10.1016/j.nicl.2015.02.003
  20. Williams O.A., Zeestraten E.A., Benjamin P. et al. Diffusion tensor image segmentation of the cerebrum provides a single measure of cerebral small vessel disease severity related to cognitive change. Neuroimage Clin. 2017; 16: 330–342. doi: 10.1016/j.nicl.2017.08.016
  21. Williams O.A., Zeestraten E.A., Benjamin P. et al. Predicting dementia in cerebral small vessel disease using an automatic diffusion tensor image segmentation technique. Stroke. 2019; 50(10): 2775–2782. doi: 10.1161/STROKEAHA.119.025843
  22. Dobrynina L.A., Gadzhieva Z.S., Shamtieva K.V. et al. Microstructural predictors of cognitive impairment in cerebral small vessel disease and the conditions of their formation. Diagnostics (Basel). 2020; 10(9): 720. doi: 10.3390/diagnostics10090720
  23. Добрынина Л.А., Гаджиева З.Ш., Шамтиева К.В. и др. Предикторы и интегративный показатель тяжести когнитивных расстройств при церебральной микроангиопатии (болезни мелких сосудов). Журнал неврологии и психиатрии им. С.С. Корсакова. 2022; 122(4): 52–60. Dobrynina L.A., Gadzhieva Z.S., Shamtieva K.V. et al. Predictors and integrative index of severity of cognitive disorders in cerebral microangiopathy. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova. 2022; 122(4): 52–60. (In Russ.). doi: 10.17116/jnevro202212204152
  24. Kim K.W., MacFall J.R., Payne M.E. Classification of white matter lesions on magnetic resonance imaging in elderly persons. Biol. Psychiatry. 2008; 64(4): 273–280. doi: 10.1016/j.biopsych.2008.03.024
  25. Medrano Martorell S., Cuadrado Blázquez M., García Figueredo D. et al. Hyperintense punctiform images in the white matter: a diagnostic approach. Radiologia. 2012; 54(4): 321–335. doi: 10.1016/j.rx.2011.09.015
  26. Gouw A.A., Seewann A., van der Flier W.M. et al. Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations. J. Neurol. Neurosurg. Psychiatry. 2011; 82(2): 126–135. doi: 10.1136/jnnp.2009.204685
  27. Гулевская Т.С., Моргунов В.А. Патологическая анатомия нарушений мозгового кровообращения при атеросклерозе и артериальной гипертонии. М.; 2009. 295 с. Gulevskaya T.S., Morgunov V.A. Pathological anatomy of cerebral blood flow disorders in atherosclerosis and arterial hypertension. Мoscow; 2009. (In Russ.)
  28. Ганнушкина И.В., Лебедева Н.В. Гипертоническая энцефалопатия. М.: Медицина, 1987. 224 с. Gannushkina I.V., Lebedeva N.V. Hypertensive encephalopathy. Мoscow; 1987. (In Russ.)
  29. Kemper T.L., Blatt G.J., Killiany R.J., Moss M.B. Neuropathology of progressive cognitive decline in chronically hypertensive rhesus monkeys. Acta Neuropathol. 2001; 101(2): 145–153. doi: 10.1007/s004010000278
  30. Bateman G.A., Levi C.R., Schofield P. et al. The venous manifestations of pulse wave encephalopathy: windkessel dysfunction in normal aging and senile dementia. Neuroradiology. 2008; 50(6): 491–497. doi: 10.1007/s00234-008-0374-x
  31. Добрынина Л.А., Ахметзянов Б.М., Гаджиева З.Ш. и др. Роль нарушений артериального, венозного кровотока и ликворотока в развитии когнитивных расстройств при церебральной микроангиопатии. Анналы клинической и экспериментальной неврологии. 2019; 13(2): 19–31. Dobrynina L.A., Akhmetzyanov B.M., Gadzhieva Z.Sh. et al. The role of arterial and venous blood flow and cerebrospinal fluid flow disturbances in the development of cognitive impairments in cerebral microangiopathy. Annals of clinical and experimental neurology. 2019; 13(2): 19–31. (In Russ.) doi: 10.25692/ACEN.2019.2.3
  32. Добрынина Л.А., Гаджиева З.Ш., Ахметзянов Б.М. и др. Роль нарушений артериального, венозного кровотока и ликворотока в формировании когнитивных расстройств при возрастзависимой церебральной микроангиопатии. Журнал неврологии и психиатрии им. С.С. Корсакова. Спецвыпуски. 2019; 119(12-2): 81–88. Dobrynina L.A., Gadzhieva Z.Sh., Akhmetzyanov B.M. et al. The role of arterial, venous blood and cerebrospinal fluid flow disturbances in forming cognitive impairment types in age-related cerebral microangiophathy. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova. 2019; 119(12-2): 81–88. (In Russ.) doi: 10.17116/jnevro201911912281
  33. Nasreddine Z.S., Phillips N.A., Bédirian V. et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 2005; 53(4): 695–699. doi: 10.1111/j.1532-5415.2005.53221.x
  34. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition: DSM-5. American Psychiatric Association, Arlington (USA): American Psychiatric Publishing; 2013. 991 p.
  35. Albert M.S., DeKosky S.T., Dickson D. et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging — Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011; 7(3): 270–279. doi: 10.1016/j.jalz.2011.03.008
  36. McKhann G.M., Knopman D.S., Chertkow H. et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging —Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011; 7(3): 263–269. doi: 10.1016/j.jalz.2011.03.005
  37. Whelton P.K., Carey R.M., Aronow W.S. et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Circulation. 2018; 138(17): e426–e483. doi: 10.1161/CIR.0000000000000597
  38. Balédent O., Henry-Feugeas M.C., Idy-Peretti I. Cerebrospinal fluid dyna- mics and relation with blood flow: a magnetic resonance study with semiautomated cerebrospinal fluid segmentation. Invest. Radiol. 2001; 36(7): 368–377. doi: 10.1097/00004424-200107000-00003
  39. Bateman G.A., Levi C.R., Schofield P. et al. Quantitative measurement of cerebral haemodynamics in early vascular dementia and Alzheimer’s disease. J. Clin. Neurosci. 2006; 13(5): 563–568. doi: 10.1016/j.jocn.2005.04.017
  40. Богомякова О.Б., Станкевич Ю.А., Месропян Н.А., Шрайбман Л.А., Тулупов А.А. Применение фазовоконтрастной магнитно-резонансной томографии в количественной оценке ликвородинамики у пациентов с сообщающейся гидроцефалией. Вестник рентгенологии и радиологии. 2016; 97(1): 20–27. Bogomyakova O.B., Stankevich Yu.A., Mesropyan N.A. et al. Use of phase-contrast magnetic resonance imaging to quantify cerebrospinal fluid dynamics in patients with communicating hydrocephalus. Vestnik rentgenologii i radiologii. 2016; 97(1): 20–27. (In Russ.) doi: 10.20862/0042-4676-2016-97-1-20-27
  41. Ashburner J., Friston K.J. Voxel-based morphometry — the methods. Neuroimage. 2000; 11(6 Pt 1): 805–821. doi: 10.1006/nimg.2000.0582
  42. Schmidt P., Wink L. LST: a lesion segmentation tool for SPM. Manual/Docu- mentation for Version 3.0.0 October 2019.
  43. Winklewski P.J., Sabisz A., Naumczyk P. et al. Understanding the physiopathology behind axial and radial diffusivity changes — what do we know? Front. Neurol. 2018; 9: 92. doi: 10.3389/fneur.2018.00092
  44. Lawrence A.J., Patel B., Morris R.G. et al. Mechanisms of cognitive impairment in cerebral small vessel disease: multimodal MRI results from the St George’s cognition and neuroimaging in stroke (SCANS) study. PLoS One. 2013; 8(4): e61014. doi: 10.1371/journal.pone.0061014
  45. Shim Y.S., Yang D.W., Roe C.M. et al. Pathological correlates of white matter hyperintensities on magnetic resonance imaging. Dement. Geriatr. Cogn. Disord. 2015; 39(1–2): 92–104. doi: 10.1159/000366411
  46. Bateman G.A. Pulse-wave encephalopathy: a comparative study of the hydrodynamics of leukoaraiosis and normal-pressure hydrocephalus. Neuroradiology. 2002; 44(9): 740–748. doi: 10.1007/s00234-002-0812-0
  47. Schmidt R., Schmidt H., Haybaeck J. et al. Heterogeneity in age-related white matter changes. Acta Neuropathol. 2011; 122(2): 171–185. doi: 10.1007/s00401-011-0851-x
  48. Castejón O.J. Ultrastructural pathology of oligodendroglial cells in traumatic and hydrocephalic human brain edema: a review. Ultrastruct. Pathol. 2015; 39(6): 359–368. doi: 10.3109/01913123.2012.750408
  49. Castejón O.J., Arismendi G.J. Nerve cell death types in the edematous human cerebral cortex. J. Submicrosc. Cytol. Pathol. 2006; 38(1): 21–36.
  50. Verheggen I.C.M., Van Boxtel M.P.J., Verhey F.R.J. et al. Interaction between blood-brain barrier and glymphatic system in solute clearance. Neurosci. Biobehav. Rev. 2018; 90: 26–33. doi: 10.1016/j.neubiorev.2018.03.028
  51. Rasmussen M.K., Mestre H., Nedergaard M. The glymphatic pathway in neurological disorders. Lancet Neurol. 2018; 17(11): 1016–1024. doi: 10.1016/S1474-4422(18)30318-1
  52. Ryberg C., Rostrup E., Paulson O.B. et al. Corpus callosum atrophy as a predictor of age-related cognitive and motor impairment: a 3-year follow-up of the LADIS study cohort. J. Neurol. Sci. 2011; 307(1–2): 100–105. doi: 10.1016/j.jns.2011.05.002
  53. Habes M., Sotiras A., Erus G. et al. White matter lesions: Spatial heterogeneity, links to risk factors, cognition, genetics, and atrophy. Neurology. 2018; 91(10): e964–e975. doi: 10.1212/WNL.0000000000006116
  54. Marnane M., Al-Jawadi O.O., Mortazavi S. et al. Periventricular hyperintensities are associated with elevated cerebral amyloid. Neurology. 2016; 86(6): 535–543. doi: 10.1212/WNL.0000000000002352

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2022 Dobrynina L.A., Gadzhieva Z.S., Shamtieva K.V., Kremneva E.I., Akhmetzyanov B.M., Tsypushtanova M.M., Makarova A.G., Trubitsyna V.V., Krotenkova M.V.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies