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Neuroimaging of Small Vessel Disease in Late-Life Depression

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Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 1192))

Abstract

Cerebral small vessel disease is associated with late-life depression, cognitive impairment, executive dysfunction, distress, and loss of life for older adults. Late-life depression is becoming a substantial public health burden, and a considerable number of older adults presenting to primary care have significant clinical depression. Even though white matter hyperintensities are linked with small vessel disease, white matter hyperintensities are nonspecific to small vessel disease and can co-occur with other brain diseases. Advanced neuroimaging techniques at the ultrahigh field magnetic resonance imaging are enabling improved characterization, identification of cerebral small vessel disease and are elucidating some of the mechanisms that associate small vessel disease with late-life depression.

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References

  1. De Leeuw FE, De Groot JC, Achten E, Oudkerk M, Ramos LMP, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam scan study. J Neurol Neurosurg Psychiatry. 2001;70(1):9–14.

    Google Scholar 

  2. Kuo H-K, Lipsitz LA. Cerebral white matter changes and geriatric syndromes: is there a link? J Gerontol Ser A Biol Sci Med Sci. 2004;59(8):M818–26. http://www.ncbi.nlm.nih.gov/pubmed/15345732.

    Article  Google Scholar 

  3. Herrmann LL, Le Masurier M, Ebmeier KP. White matter hyperintensities in late life depression: a systematic review. J Neurol Neurosurg Psychiatry. 2007;79(6):619–24. http://jnnp.bmj.com/cgi/doi/10.1136/jnnp.2007.124651.

    Article  Google Scholar 

  4. Silbert LC, Nelson C, Howieson DB, Moore MM, Kaye JA. Impact of white matter hyperintensity volume progression on rate of cognitive and motor decline. Neurology. 2008;71(2):108–13. http://www.neurology.org/cgi/doi/10.1212/01.wnl.0000316799.86917.37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Steffens DC, Tupler LA, Ranga K, Krishnan R. Magnetic resonance imaging signal hypointensity and iron content of putamen nuclei in elderly depressed patients. Psychiatry Res Neuroimaging. 1998;83(2):95–103. https://linkinghub.elsevier.com/retrieve/pii/S0925492798000328.

    Article  CAS  PubMed  Google Scholar 

  6. Sabayan B, Westendorp RG, Grond J van der, Stott DJ, Sattar N, van Osch MJP, et al. Markers of endothelial dysfunction and cerebral blood flow in older adults. Neurobiol Aging. 2014;35(2):373–7. https://linkinghub.elsevier.com/retrieve/pii/S0197458013003540.

    Article  CAS  PubMed  Google Scholar 

  7. Beason-Held LL, Moghekar A, Zonderman AB, Kraut MA, Resnick SM. Longitudinal changes in cerebral blood flow in the older hypertensive brain. Stroke. 2007;38(6):1766–73. https://www.ahajournals.org/doi/10.1161/STROKEAHA.106.477109.

    Article  PubMed  Google Scholar 

  8. Debette S, Seshadri S, Beiser A, Au R, Himali JJ, Palumbo C, et al. Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology. 2011;77(5):461–8. http://www.neurology.org/cgi/doi/10.1212/WNL.0b013e318227b227.

  9. Jefferson AL, Himali JJ, Beiser AS, Au R, Massaro JM, Seshadri S, et al. Cardiac index is associated with brain aging. Circulation. 2010;122(7):690–7. https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.109.905091.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Vidal J-S, Sigurdsson S, Jonsdottir MK, Eiriksdottir G, Thorgeirsson G, Kjartansson O, et al. Coronary artery calcium, brain function and structure. Stroke. 2010;41(5):891–7. https://www.ahajournals.org/doi/10.1161/STROKEAHA.110.579581.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Swan GE, DeCarli C, Miller BL, Reed T, Wolf PA, Jack LM, et al. Association of midlife blood pressure to late-life cognitive decline and brain morphology. Neurology. 1998;51(4):986–93. http://www.neurology.org/cgi/doi/10.1212/WNL.51.4.986.

    Article  CAS  PubMed  Google Scholar 

  12. Strassburger TL, Lee H-C, Daly EM, Szczepanik J, Krasuski JS, Mentis MJ, et al. Interactive effects of age and hypertension on volumes of brain structures. Stroke. 1997;28(7):1410–7. https://www.ahajournals.org/doi/10.1161/01.STR.28.7.1410.

    Article  CAS  PubMed  Google Scholar 

  13. Taylor WD, Aizenstein HJ, Alexopoulos GS. The vascular depression hypothesis: mechanisms linking vascular disease with depression. Mol Psychiatry. 2013;18(9):963–74. http://www.nature.com/articles/mp201320.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Blazer DG. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003;58(3):249–65. http://www.ncbi.nlm.nih.gov/pubmed/12634292.

  15. Alexopoulos GS. Depression in the elderly. Lancet. 2005;365(9475):1961–70. https://linkinghub.elsevier.com/retrieve/pii/S0140673605666652.

    Article  Google Scholar 

  16. Gallegos-Carrillo K, García-Peña C, Mudgal J, Romero X, Durán-Arenas L, Salmerón J. Role of depressive symptoms and comorbid chronic disease on health-related quality of life among community-dwelling older adults. J Psychosom Res. 2009;66(2):127–35. https://linkinghub.elsevier.com/retrieve/pii/S0022399908003632.

    Article  PubMed  Google Scholar 

  17. Lebowitz BD, Pearson JL, Schneider LS, Reynolds CF, Alexopoulos GS, Bruce ML, et al. Diagnosis and treatment of depression in late life. Consensus statement update. JAMA. 1997;278(14):1186–90. http://www.ncbi.nlm.nih.gov/pubmed/9326481.

    Article  CAS  PubMed  Google Scholar 

  18. Vu NQ, Aizenstein HJ. Depression in the elderly. Curr Opin Neurol. 2013;26(6):656–61. http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00019052-201312000-00011.

  19. Suzuki A, Kondo T, Mihara K, Yasui-Furukori N, Otani K, Furukori H, et al. Association between Taq I A dopamine D2 receptor polymorphism and therapeutic response to bromperidol: a preliminary report. Eur Arch Psychiatry Clin Neurosci. 2001;251(2):57–9. http://link.springer.com/10.1007/s004060170053.

    Article  CAS  PubMed  Google Scholar 

  20. Sheline YI, Pieper CF, Barch DM, Welsh-Boehmer K, McKinstry RC, MacFall JR, et al. Support for the vascular depression hypothesis in late-life depression. Arch Gen Psychiatry. 2010;67(3):277. http://archpsyc.jamanetwork.com/article.aspx?doi=10.1001/archgenpsychiatry.2009.204.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sheline YI, Price JL, Vaishnavi SN, Mintun MA, Barch DM, Epstein AA, et al. Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. Am J Psychiatry. 2008;165(4):524–32. http://psychiatryonline.org/doi/abs/10.1176/appi.ajp.2007.07010175.

    Article  PubMed  Google Scholar 

  22. Gouw AA, van der Flier WM, Fazekas F, van Straaten ECW, Pantoni L, Poggesi A, et al. Progression of white matter hyperintensities and incidence of new lacunes over a 3-year period. Stroke. 2008;39(5):1414–20. https://www.ahajournals.org/doi/10.1161/STROKEAHA.107.498535.

    Article  PubMed  Google Scholar 

  23. Quarantelli M, Lanzillo R, Del Vecchio W, Mollica C, Prinster A, Iadicicco L, et al. Modifications of brain tissue volumes in facioscapulohumeral dystrophy. Neuroimage. 2006;32(3):1237–42. https://linkinghub.elsevier.com/retrieve/pii/S105381190600526X.

    Article  PubMed  Google Scholar 

  24. Tupler LA, Krishnan KRR, McDonald WM, Dombeck CB, D’Souza S, Steffens DC. Anatomic location and laterality of MRI signal hyperintensities in late-life depression. J Psychosom Res. 2002;53(2):665–76. https://linkinghub.elsevier.com/retrieve/pii/S0022399902004257.

    Article  PubMed  Google Scholar 

  25. Nebes RD, Vora IJ, Meltzer CC, Fukui MB, Williams RL, Kamboh MI, et al. Relationship of deep white matter hyperintensities and apolipoprotein e genotype to depressive symptoms in older adults without clinical depression. Am J Psychiatry. 2001;158(6):878–84. http://psychiatryonline.org/doi/abs/10.1176/appi.ajp.158.6.878.

    Article  CAS  PubMed  Google Scholar 

  26. Taylor WD, Steffens DC, MacFall JR, McQuoid DR, Payne ME, Provenzale JM, et al. White matter hyperintensity progression and late-life depression outcomes. Arch Gen Psychiatry. 2003;60(11):1090. http://archpsyc.jamanetwork.com/article.aspx?doi=10.1001/archpsyc.60.11.1090.

    Article  PubMed  Google Scholar 

  27. Khalaf A, Edelman K, Tudorascu D, Andreescu C, Reynolds CF, Aizenstein H. White matter hyperintensity accumulation during treatment of late-life depression. Neuropsychopharmacology. 2015;40(13):3027–35. http://www.nature.com/articles/npp2015158.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Aizenstein HJ, Khalaf A, Walker SE, Andreescu C. Magnetic resonance imaging predictors of treatment response in late-life depression. J Geriatr Psychiatry Neurol. 2014;27(1):24–32.

    Article  PubMed  Google Scholar 

  29. R.A.Ochs. FDA approval of Magnetom Terra. 2017. https://www.accessdata.fda.gov/cdrh_docs/pdf17/K170840.pdf.

  30. Polimeni JR, Uludağ K. Neuroimaging with ultra-high field MRI: present and future. Neuroimage. 2018;168(February):1–6.

    Article  PubMed  Google Scholar 

  31. Ibrahim T, Hue Y, Gilbert R, Boada F. Tic Tac Toe: highly-coupled, load insensitive Tx/Rx array and a quadrature coil without lumped capacitors. Proceedings of the 16th scientific meeting, international society for magnetic resonance in medicine, Toronto, January 2008. p. 438. https://cds.ismrm.org/ismrm-2008/files/00438.pdf.

  32. Pohmann R, Speck O, Scheffler K. Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 tesla using current receive coil arrays. Magn Reson Med. 2016;75(2):801–9. http://doi.wiley.com/10.1002/mrm.25677.

    Article  PubMed  Google Scholar 

  33. Peters AM, Brookes MJ, Hoogenraad FG, Gowland PA, Francis ST, Morris PG, et al. T2* measurements in human brain at 1.5, 3 and 7 T. Magn Reson Imaging. 2007;25(6):748–53. https://linkinghub.elsevier.com/retrieve/pii/S0730725X07001701.

    Article  PubMed  Google Scholar 

  34. Rooney WD, Johnson G, Li X, Cohen ER, Kim S-G, Ugurbil K, et al. Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo. Magn Reson Med. 2007;57(2):308–18. http://doi.wiley.com/10.1002/mrm.21122.

    Article  CAS  PubMed  Google Scholar 

  35. Park C-A, Kang C-K, Kim Y-B, Cho Z-H. Advances in MR angiography with 7 T MRI: From microvascular imaging to functional angiography. Neuroimage. 2018;168(January 2017):269–78. https://doi.org/10.1016/j.neuroimage.2017.01.019.

    Article  PubMed  Google Scholar 

  36. van der Zwaag W, Francis S, Head K, Peters A, Gowland P, Morris P, et al. fMRI at 1.5, 3 and 7 T: characterising BOLD signal changes. Neuroimage. 2009;47(4):1425–34. http://dx.doi.org/10.1016/j.neuroimage.2009.05.015.

    Article  PubMed  Google Scholar 

  37. Yacoub E, Shmuel A, Pfeuffer J, Van De Moortele P-F, Adriany G, Andersen P, et al. Imaging brain function in humans at 7 Tesla. Magn Reson Med. 2001;45(4):588–94. http://doi.wiley.com/10.1002/mrm.1080.

    Article  CAS  PubMed  Google Scholar 

  38. Zwanenburg JJM, Hendrikse J, Visser F, Takahara T, Luijten PR. Fluid attenuated inversion recovery (FLAIR) MRI at 7.0 Tesla: comparison with 1.5 and 3.0 Tesla. Eur Radiol. 2010;20(4):915–22.

    Article  PubMed  PubMed Central  Google Scholar 

  39. van Kalleveen IML, Koning W, Boer VO, Luijten PR, Zwanenburg JJM, Klomp DWJ. Adiabatic turbo spin echo in human applications at 7 T. Magn Reson Med. 2012;68(2):580–7. http://doi.wiley.com/10.1002/mrm.23264.

  40. Ibrahim TS, Abduljalil AM, Baertlein BA, Jin JM, Chen J, Prock T, et al. B1 field homogeneity and SAR calculations for the birdcage coil. Phys Med Biol. 2001;46:609–19.

    Article  CAS  PubMed  Google Scholar 

  41. Ibrahim TS, Abduljalil AM, Baertlein BA, Lee R, Robitaille P-M-L. Analysis of B 1 field profiles and SAR values for multi-strut transverse electromagnetic RF coils in high field MRI applications. Phys Med Biol. 2001;46(10):2545–55. http://stacks.iop.org/0031-9155/46/i=10/a=303?key=crossref.47bbda7bd3ed5b0905ee30465694aa42.

    Article  CAS  PubMed  Google Scholar 

  42. Santini T, Zhao Y, Wood S, Krishnamurthy N, Kim J, Farhat N, et al. In-vivo and numerical analysis of the eigenmodes produced by a multi-level Tic-Tac-Toe head transmit array for 7 Tesla MRI. Lundberg P, editor. PLoS One. 2018;13(11):e0206127. http://dx.plos.org/10.1371/journal.pone.0206127.

  43. Krishnamurthy N, Santini T, Wood S, Kim J, Zhao T, Aizenstein HJ, et al. Computational and experimental evaluation of the Tic-Tac-Toe RF coil for 7 Tesla MRI. Orzada S, editor. PLoS One. 2019;14(1):e0209663. http://dx.plos.org/10.1371/journal.pone.0209663.

  44. Winkler SA, Schmitt F, Landes H, de Bever J, Wade T, Alejski A, et al. Gradient and shim technologies for ultra high field MRI. Neuroimage. 2018;168:59–70. https://linkinghub.elsevier.com/retrieve/pii/S1053811916306498.

    Article  PubMed  Google Scholar 

  45. Stockmann JP, Wald LL. In vivo B 0 field shimming methods for MRI at 7 T. Neuroimage. 2018;168:71–87. https://linkinghub.elsevier.com/retrieve/pii/S1053811917304822.

    Article  PubMed  Google Scholar 

  46. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12:822–38.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Vermeer SE, Longstreth WT, Koudstaal PJ. Silent brain infarcts: a systematic review. Lancet Neurol. 2007;6(7):611–9. https://linkinghub.elsevier.com/retrieve/pii/S1474442207701709.

    Article  PubMed  Google Scholar 

  48. Snowdon DA. Brain infarction and the clinical expression of alzheimer disease-reply. J Am Med Assoc. 1997;278(2):114. http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.1997.03550020046024.

    Article  Google Scholar 

  49. Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MMB. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med. 2003;348(13):1215–22. http://www.nejm.org/doi/abs/10.1056/NEJMoa022066.

    Article  PubMed  Google Scholar 

  50. Schrag M, McAuley G, Pomakian J, Jiffry A, Tung S, Mueller C, et al. Correlation of hypointensities in susceptibility-weighted images to tissue histology in dementia patients with cerebral amyloid angiopathy: a postmortem MRI study. Acta Neuropathol. 2010;119(3):291–302. http://link.springer.com/10.1007/s00401-009-0615-z.

    Article  Google Scholar 

  51. Patankar TF, Baldwin R, Mitra D, Jeffries S, Sutcliffe C, Burns A, et al. Virchow–Robin space dilatation may predict resistance to antidepressant monotherapy in elderly patients with depression. J Affect Disord. 2007;97(1–3):265–70. https://linkinghub.elsevier.com/retrieve/pii/S0165032706002916.

    Article  CAS  PubMed  Google Scholar 

  52. Potter GM, Marlborough FJ, Wardlaw JM. Wide variation in definition, detection, and description of lacunar lesions on imaging. Stroke. 2011;42(2):359–66. https://www.ahajournals.org/doi/10.1161/STROKEAHA.110.594754.

    Article  PubMed  Google Scholar 

  53. Benjamin P, Trippier S, Lawrence AJ, Lambert C, Zeestraten E, Williams OA, et al. Lacunar infarcts, but not perivascular spaces, are predictors of cognitive decline in cerebral small-vessel disease. Stroke. 2018;49(3):586–93. https://www.ahajournals.org/doi/10.1161/STROKEAHA.117.017526.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Madai VI, von Samson-Himmelstjerna FC, Bauer M, Stengl KL, Mutke MA, Tovar-Martinez E, et al. Ultrahigh-field MRI in human ischemic Stroke—a 7 Tesla study. Herholz K, editor. PLoS One. 2012;7(5):e37631. http://dx.plos.org/10.1371/journal.pone.0037631.

  55. Zong X, Park SH, Shen D, Lin W. Visualization of perivascular spaces in the human brain at 7 T: sequence optimization and morphology characterization. Neuroimage. 2016;125:895–902. https://linkinghub.elsevier.com/retrieve/pii/S1053811915010022.

    Article  PubMed  Google Scholar 

  56. Bouvy WH, Zwanenburg JJM, Reinink R, Wisse LEM, Luijten PR, Kappelle LJ, et al. Perivascular spaces on 7 Tesla brain MRI are related to markers of small vessel disease but not to age or cardiovascular risk factors. J Cereb Blood Flow Metab. 2016;36(10):1708–17. http://journals.sagepub.com/doi/10.1177/0271678X16648970.

    Article  Google Scholar 

  57. De Cocker LJ, Lindenholz A, Zwanenburg JJ, van der Kolk AG, Zwartbol M, Luijten PR, et al. Clinical vascular imaging in the brain at 7 T. Neuroimage. 2018;168(November 2016):452–8. https://doi.org/10.1016/j.neuroimage.2016.11.044.

    Article  PubMed  Google Scholar 

  58. Shoamanesh A, Preis SR, Beiser AS, Vasan RS, Benjamin EJ, Kase CS, et al. Inflammatory biomarkers, cerebral microbleeds, and small vessel disease: Framingham heart study. Neurology. 2015;84(8):825–32. http://www.neurology.org/cgi/doi/10.1212/WNL.0000000000001279.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Theysohn JM, Kraff O, Maderwald S, Barth M, Ladd SC, Forsting M, et al. 7 tesla MRI of microbleeds and white matter lesions as seen in vascular dementia. J Magn Reson Imaging. 2011;33(4):782–91. http://www.ncbi.nlm.nih.gov/pubmed/21448941.

    Article  PubMed  Google Scholar 

  60. Stehling C, Wersching H, Kloska SP, Kirchhof P, Ring J, Nassenstein I, et al. Detection of asymptomatic cerebral microbleeds. Acad Radiol. 2008;15(7):895–900. https://linkinghub.elsevier.com/retrieve/pii/S1076633208000469.

    Article  PubMed  Google Scholar 

  61. Nandigam RNK, Viswanathan A, Delgado P, Skehan ME, Smith EE, Rosand J, et al. MR imaging detection of cerebral microbleeds: effect of susceptibility-weighted imaging, section thickness, and field strength. Am J Neuroradiol. 2009;30(2):338–43. http://www.ajnr.org/lookup/doi/10.3174/ajnr.A1355.

    Article  PubMed  Google Scholar 

  62. de Bresser J, Brundel M, Conijn MM, van Dillen JJ, Geerlings MI, Viergever MA, et al. Visual cerebral microbleed detection on 7 T MR imaging: reliability and effects of image processing. Am J Neuroradiol. 2013;34(6):E61–4. http://www.ajnr.org/lookup/doi/10.3174/ajnr.A2960.

  63. White L, Petrovitch H, Hardman J, Nelson J, Davis DG, Ross GW, et al. Cerebrovascular pathology and dementia in autopsied honolulu-asia aging study participants. Ann N Y Acad Sci. 2002;977(1):9–23. http://doi.wiley.com/10.1111/j.1749-6632.2002.tb04794.x.

    Article  PubMed  Google Scholar 

  64. Brundel M, de Bresser J, van Dillen JJ, Kappelle LJ, Biessels GJ. Cerebral microinfarcts: a systematic review of neuropathological studies. J Cereb Blood Flow Metab. 2012;32(3):425–36. http://journals.sagepub.com/doi/10.1038/jcbfm.2011.200.

    Article  Google Scholar 

  65. Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–38. https://linkinghub.elsevier.com/retrieve/pii/S1474442213701248.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Smith EE, Schneider JA, Wardlaw JM, Greenberg SM. Cerebral microinfarcts: the invisible lesions. Lancet Neurol. 2012;11(3):272–82. https://linkinghub.elsevier.com/retrieve/pii/S1474442211703076.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Summers PM, Hartmann DA, Hui ES, Nie X, Deardorff RL, McKinnon ET, et al. Functional deficits induced by cortical microinfarcts. J Cereb Blood Flow Metab. 2017;37(11):3599–614. http://journals.sagepub.com/doi/10.1177/0271678X16685573.

    Article  Google Scholar 

  68. Coban H, Tung S, Yoo B, Vinters H V., Hinman JD. Molecular disorganization of axons adjacent to human cortical microinfarcts. Front Neurol. 2017;8. http://journal.frontiersin.org/article/10.3389/fneur.2017.00405/full.

  69. van Veluw SJ, Zwanenburg JJM, Engelen-Lee J, Spliet WGM, Hendrikse J, Luijten PR, et al. In vivo detection of cerebral cortical microinfarcts with high-resolution 7 T MRI. J Cereb Blood Flow Metab. 2013;33(3):322–9. http://journals.sagepub.com/doi/10.1038/jcbfm.2012.196.

  70. van Veluw SJ, Zwanenburg JJ, Rozemuller AJ, Luijten PR, Spliet WG, Biessels GJ. The spectrum of MR detectable cortical microinfarcts: a classification study with 7-Tesla postmortem MRI and histopathology. J Cereb Blood Flow Metab. 2015;35(4):676–83. http://journals.sagepub.com/doi/10.1038/jcbfm.2014.258.

    Article  Google Scholar 

  71. van Veluw SJ, Shih AY, Smith EE, Chen C, Schneider JA, Wardlaw JM, et al. Detection, risk factors, and functional consequences of cerebral microinfarcts. Lancet Neurol. 2017;16(9):730–40. http://dx.doi.org/10.1016/S1474-4422(17)30196-5.

  72. Shi Y, Wardlaw JM. Update on cerebral small vessel disease: a dynamic whole-brain disease. BMJ. 2016;1(3):83–92. http://svn.bmj.com/cgi/doi/10.1136/svn-2016-000035.

    Google Scholar 

  73. Wardlaw JM, Smith C, Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 2013;12(5):483–97. https://linkinghub.elsevier.com/retrieve/pii/S1474442213700607.

    Article  PubMed  Google Scholar 

  74. Heverhagen JT, Bourekas E, Sammet S, Knopp M V., Schmalbrock P. Time-of-flight magnetic resonance angiography at 7 Tesla. Invest Radiol. 2008;43(8):568–73. https://insights.ovid.com/crossref?an=00004424-200808000-00004.

    Article  PubMed  Google Scholar 

  75. Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol. 2010;9(7):689–701. https://linkinghub.elsevier.com/retrieve/pii/S1474442210701046.

    Article  PubMed  Google Scholar 

  76. Cho Z-H, Kang C-K, Han J-Y, Kim S-H, Kim K-N, Hong S-M, et al. Observation of the lenticulostriate arteries in the human brain in vivo using 7.0T MR Angiography. Stroke. 2008;39(5):1604–6. https://www.ahajournals.org/doi/10.1161/STROKEAHA.107.508002.

    Article  PubMed  Google Scholar 

  77. Kang C-K, Park C-A, Kim K-N, Hong S-M, Park C-W, Kim Y-B, et al. Non-invasive visualization of basilar artery perforators with 7 T MR angiography. J Magn Reson Imaging. 2010;32(3):544–50. http://doi.wiley.com/10.1002/jmri.22250.

    Article  PubMed  Google Scholar 

  78. Harteveld AA, De Cocker LJL, Dieleman N, van der Kolk AG, Zwanenburg JJM, Robe PA, et al. High-resolution postcontrast time-of-flight MR angiography of intracranial perforators at 7.0 Tesla. Paul F, editor. PLoS One. 2015;10(3):e0121051. https://dx.plos.org/10.1371/journal.pone.0121051.

  79. Cho Z-H, Kang C-K, Park C-A, Hong S-M, Kim S-H, Oh S-T, et al. Microvascular functional MR angiography with ultra-high-field 7 t MRI: comparison with BOLD fMRI. Int J Imaging Syst Technol. 2012;22(1):18–22. http://doi.wiley.com/10.1002/ima.22008.

    Article  Google Scholar 

  80. Moody DM, Brown WR, Challa VR, Ghazi-Birry HS, Reboussin DM. Cerebral microvascular alterations in aging, leukoaraiosis, and Alzheimer’s disease. Ann N Y Acad Sci. 1997;826(1 Cerebrovascul):103–16. http://doi.wiley.com/10.1111/j.1749-6632.1997.tb48464.x.

    Article  CAS  PubMed  Google Scholar 

  81. Shaaban CE, Aizenstein HJ, Jorgensen DR, MacCloud RL, Meckes NA, Erickson KI, et al. In vivo imaging of venous side cerebral small-vessel disease in older adults: an MRI method at 7 T. Am J Neuroradiol. 2017;38(10):1923–8. http://www.ajnr.org/lookup/doi/10.3174/ajnr.A5327.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Mittal S, Wu Z, Neelavalli J, Haacke EM. Susceptibility-weighted imaging: technical aspects and clinical applications, Part 2. Am J Neuroradiol. 2009;30(2):232–52. http://www.ajnr.org/lookup/doi/10.3174/ajnr.A1461.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Haacke EM, Mittal S, Wu Z, Neelavalli J, Cheng Y-CN. Susceptibility-weighted imaging: technical aspects and clinical applications, Part 1. Am J Neuroradiol. 2009;30(1):19–30. http://www.ajnr.org/lookup/doi/10.3174/ajnr.A1400.

    Article  PubMed  Google Scholar 

  84. Sinnecker T, Bozin I, Dörr J, Pfueller CF, Harms L, Niendorf T, et al. Periventricular venous density in multiple sclerosis is inversely associated with T2 lesion count: a 7 Tesla MRI study. Mult Scler J. 2013;19(3):316–25. http://journals.sagepub.com/doi/10.1177/1352458512451941.

    Article  PubMed  Google Scholar 

  85. Novelli EM, Elizabeth Sarles C, Jay Aizenstein H, Ibrahim TS, Butters MA, Connelly Ritter A, et al. Brain venular pattern by 7 T MRI correlates with memory and haemoglobin in sickle cell anaemia. Psychiatry Res Neuroimaging. 2015;233(1):18–22. https://linkinghub.elsevier.com/retrieve/pii/S0925492715000931.

    Article  PubMed  Google Scholar 

  86. De Guio F, Vignaud A, Ropele S, Duering M, Duchesnay E, Chabriat H, et al. Loss of venous integrity in cerebral small vessel disease. Stroke. 2014;45(7):2124–6. https://www.ahajournals.org/doi/10.1161/STROKEAHA.114.005726.

    Article  PubMed  Google Scholar 

  87. Kuijf HJ, Bouvy WH, Zwanenburg JJM, Razoux Schultz TB, Viergever MA, Vincken KL, et al. Quantification of deep medullary veins at 7 T brain MRI. Eur Radiol. 2016;26(10):3412–8. http://link.springer.com/10.1007/s00330-016-4220-y.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Hartmann DA, Hyacinth HI, Liao F-F, Shih AY. Does pathology of small venules contribute to cerebral microinfarcts and dementia? J Neurochem. 2018;144(5):517–26. http://doi.wiley.com/10.1111/jnc.14228.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  89. Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology. 1993;43(9):1683–9. http://www.ncbi.nlm.nih.gov/pubmed/8414012.

    Article  CAS  PubMed  Google Scholar 

  90. Moody DM, Brown WR, Challa VR, Anderson RL. Periventricular venous collagenosis: association with leukoaraiosis. Radiology. 1995;194(2):469–76. http://pubs.rsna.org/doi/10.1148/radiology.194.2.7824728.

    Article  CAS  PubMed  Google Scholar 

  91. Webb AJS, Simoni M, Mazzucco S, Kuker W, Schulz U, Rothwell PM. Increased cerebral arterial pulsatility in patients with leukoaraiosis. Stroke. 2012;43(10):2631–6. https://www.ahajournals.org/doi/10.1161/STROKEAHA.112.655837.

    Article  PubMed  Google Scholar 

  92. Shi Y, Thrippleton MJ, Marshall I, Wardlaw JM. Intracranial pulsatility in patients with cerebral small vessel disease: a systematic review. Clin Sci. 2018;132(1):157–71. http://www.clinsci.org/cgi/doi/10.1042/CS20171280.

    Article  PubMed  Google Scholar 

  93. Blair GW, Hernandez MV, Thrippleton MJ, Doubal FN, Wardlaw JM. Advanced neuroimaging of cerebral small vessel disease. Curr Treat Options Cardiovasc Med. 2017;19(7):56. http://link.springer.com/10.1007/s11936-017-0555-1.

  94. Furuta A, Ishii N, Nishihara Y, Horie A. Medullary arteries in aging and dementia. Stroke. 1991;22(4):442–6. https://www.ahajournals.org/doi/10.1161/01.STR.22.4.442.

    Article  CAS  PubMed  Google Scholar 

  95. Miao Q, Paloneva T, Tuominen S, Pöyhönen M, Tuisku S, Viitanen M, et al. Fibrosis and stenosis of the long penetrating cerebral arteries: the cause of the white matter pathology in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Brain Pathol. 2006;14(4):358–64. http://doi.wiley.com/10.1111/j.1750-3639.2004.tb00078.x.

    Article  PubMed Central  Google Scholar 

  96. Geurts L, Biessels GJ, Luijten P, Zwanenburg J. Better and faster velocity pulsatility assessment in cerebral white matter perforating arteries with 7 T quantitative flow MRI through improved slice profile, acquisition scheme, and postprocessing. Magn Reson Med. 2018;79(3):1473–82. http://doi.wiley.com/10.1002/mrm.26821.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Geurts LJ, Zwanenburg JJM, Klijn CJM, Luijten PR, Biessels GJ. Higher pulsatility in cerebral perforating arteries in patients with small vessel disease related stroke, a 7 T MRI study. Stroke. 2019;50(1):62–8. https://www.ahajournals.org/doi/10.1161/STROKEAHA.118.022516.

    Article  Google Scholar 

  98. Zwanenburg JJM, van Osch MJP. Targeting cerebral small vessel disease with MRI. Stroke. 2017;48(11):3175–82. https://www.ahajournals.org/doi/10.1161/STROKEAHA.117.016996.

    Article  PubMed  Google Scholar 

  99. Hall CN, Reynell C, Gesslein B, Hamilton NB, Mishra A, Sutherland BA, et al. Capillary pericytes regulate cerebral blood flow in health and disease. Nature. 2014;508(7494):55–60. http://www.nature.com/articles/nature13165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Toth P, Tarantini S, Csiszar A, Ungvari Z. Functional vascular contributions to cognitive impairment and dementia: mechanisms and consequences of cerebral autoregulatory dysfunction, endothelial impairment, and neurovascular uncoupling in aging. Am J Physiol Circ Physiol. 2017;312(1):H1–20. http://www.physiology.org/doi/10.1152/ajpheart.00581.2016.

    Article  PubMed  Google Scholar 

  101. Ogawa S, Lee T-M, Nayak AS, Glynn P. Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magn Reson Med. 1990;14(1):68–78. http://doi.wiley.com/10.1002/mrm.1910140108.

    Article  CAS  PubMed  Google Scholar 

  102. Blair GW, Doubal FN, Thrippleton MJ, Marshall I, Wardlaw JM. Magnetic resonance imaging for assessment of cerebrovascular reactivity in cerebral small vessel disease: a systematic review. J Cereb Blood Flow Metab. 2016;36(5):833–41. http://journals.sagepub.com/doi/10.1177/0271678X16631756.

    Article  Google Scholar 

  103. Liu P, Li Y, Pinho M, Park DC, Welch BG, Lu H. Cerebrovascular reactivity mapping without gas challenges. Neuroimage. 2017;146:320–6. https://linkinghub.elsevier.com/retrieve/pii/S1053811916306723.

    Article  PubMed  Google Scholar 

  104. Huber L, Ivanov D, Krieger SN, Streicher MN, Mildner T, Poser BA, et al. Slab-selective, BOLD-corrected VASO at 7 tesla provides measures of cerebral blood volume reactivity with high signal-to-noise ratio. Magn Reson Med. 2014;72(1):137–48. http://doi.wiley.com/10.1002/mrm.24916.

    Article  PubMed  Google Scholar 

  105. Heye AK, Culling RD, Valdés Hernández MDC, Thrippleton MJ, Wardlaw JM. Assessment of blood–brain barrier disruption using dynamic contrast-enhanced MRI: a systematic review. NeuroImage Clin. 2014;6:262–74. https://linkinghub.elsevier.com/retrieve/pii/S2213158214001387.

    Article  Google Scholar 

  106. Wardlaw JM, Makin SJ, Valdés Hernández MC, Armitage PA, Heye AK, Chappell FM, et al. Blood-brain barrier failure as a core mechanism in cerebral small vessel disease and dementia: evidence from a cohort study. Alzheimer’s Dement. 2017;13(6):634–43. https://linkinghub.elsevier.com/retrieve/pii/S1552526016300401.

    Article  Google Scholar 

  107. van Nieuwenhuizen KM, Hendrikse J, Klijn CJM. New microbleed after blood–brain barrier leakage in intracerebral haemorrhage. BMJ Case Rep. 2017;bcr-2016-218794. http://casereports.bmj.com/lookup/doi/10.1136/bcr-2016-218794.

  108. Hiscox L V., Johnson CL, Barnhill E, McGarry MDJ, Huston J, van Beek EJR, et al. Magnetic resonance elastography (MRE) of the human brain: technique, findings and clinical applications. Phys Med Biol. 2016;61(24):R401–37. http://stacks.iop.org/0031-9155/61/i=24/a=R401?key=crossref.ecc79a2b0aeac0f13673535f9385f27a.

    Article  PubMed  Google Scholar 

  109. Sack I, Beierbach B, Wuerfel J, Klatt D, Hamhaber U, Papazoglou S, et al. The impact of aging and gender on brain viscoelasticity. Neuroimage. 2009;46(3):652–7. https://linkinghub.elsevier.com/retrieve/pii/S1053811909002237.

    Article  PubMed  Google Scholar 

  110. Sarvazyan AP, Skovoroda AR, Emelianov SY, Fowlkes JB, Pipe JG, Adler RS, et al. Biophysical bases of elasticity imaging. In 1995. p. 223–40. http://link.springer.com/10.1007/978-1-4615-1943-0_23.

  111. Desmidt T, Brizard B, Dujardin P-A, Ternifi R, Réméniéras J-P, Patat F, et al. Brain tissue pulsatility is increased in midlife depression: a comparative study using ultrasound tissue pulsatility imaging. Neuropsychopharmacology. 2017;42(13):2575–82. http://www.nature.com/articles/npp2017113.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Johnson CL, Telzer EH. Magnetic resonance elastography for examining developmental changes in the mechanical properties of the brain. Dev Cogn Neurosci. 2018;33:176–81. https://linkinghub.elsevier.com/retrieve/pii/S1878929317300373.

    Article  PubMed  Google Scholar 

  113. Fehlner A, Hirsch S, Weygandt M, Christophel T, Barnhill E, Kadobianskyi M, et al. Increasing the spatial resolution and sensitivity of magnetic resonance elastography by correcting for subject motion and susceptibility-induced image distortions. J Magn Reson Imaging. 2017;46(1):134–41. http://doi.wiley.com/10.1002/jmri.25516.

    Article  PubMed  Google Scholar 

  114. Toga AW, Clark KA, Thompson PM, Shattuck DW, Van Horn JD. Mapping the human connectome. Neurosurgery. 2012;71(1):1–5. https://academic.oup.com/neurosurgery/article-lookup/doi/10.1227/NEU.0b013e318258e9ff.

    Article  PubMed  Google Scholar 

  115. Schmidt R, Ropele S, Ferro J, Madureira S, Verdelho A, Petrovic K, et al. Diffusion-weighted imaging and cognition in the leukoariosis and disability in the elderly study. Stroke. 2010;41(5). https://www.ahajournals.org/doi/10.1161/STROKEAHA.109.576629.

  116. Tuladhar AM, van Dijk E, Zwiers MP, van Norden AGW, de Laat KF, Shumskaya E, et al. Structural network connectivity and cognition in cerebral small vessel disease. Hum Brain Mapp. 2016;37(1):300–10. http://doi.wiley.com/10.1002/hbm.23032.

    Article  PubMed  PubMed Central  Google Scholar 

  117. Tuladhar AM, Lawrence A, Norris DG, Barrick TR, Markus HS, de Leeuw F-E. Disruption of rich club organisation in cerebral small vessel disease. Hum Brain Mapp. 2017;38(4):1751–66. http://doi.wiley.com/10.1002/hbm.23479.

    Article  PubMed  PubMed Central  Google Scholar 

  118. Lawrence AJ, Tozer DJ, Stamatakis EA, Markus HS. A comparison of functional and tractography based networks in cerebral small vessel disease. NeuroImage Clin. 2018;18(February):425–32. https://doi.org/10.1016/j.nicl.2018.02.013.

    Article  Google Scholar 

  119. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med. 1995;34(4):537–41. http://doi.wiley.com/10.1002/mrm.1910340409.

    Article  CAS  PubMed  Google Scholar 

  120. Cole. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci. 2010; http://journal.frontiersin.org/article/10.3389/fnsys.2010.00008/abstract.

  121. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, et al. Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci. 2009;106(31):13040–5. http://www.pnas.org/cgi/doi/10.1073/pnas.0905267106.

    Article  CAS  Google Scholar 

  122. Fornito A, Zalesky A, Bullmore ET. Fundamentals of brain network analysis. Elsevier; 2016. https://linkinghub.elsevier.com/retrieve/pii/C2012006036X.

  123. Craddock RC, Holtzheimer PE, Hu XP, Mayberg HS. Disease state prediction from resting state functional connectivity. Magn Reson Med. 2009;62(6):1619–28. http://doi.wiley.com/10.1002/mrm.22159.

    Article  PubMed  PubMed Central  Google Scholar 

  124. Venkataraman A, Kubicki M, Westin C-F, Golland P. Robust feature selection in resting-state fMRI connectivity based on population studies. In: 2010 IEEE computer society conference on computer vision and pattern recognition—workshops. IEEE; 2010. p. 63–70. http://ieeexplore.ieee.org/document/5543446/.

  125. Yu Y, Shen H, Zhang H, Zeng L-L, Xue Z, Hu D. Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings. Biomed Eng Online. 2013;12(1):10. http://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-12-10.

    Article  PubMed  PubMed Central  Google Scholar 

  126. Eloyan A, Muschelli J, Nebel MB, Liu H, Han F, Zhao T, et al. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging. Front Syst Neurosci. 2012;6. http://journal.frontiersin.org/article/10.3389/fnsys.2012.00061/abstract.

  127. Dai D, Wang J, Hua J, He H. Classification of ADHD children through multimodal magnetic resonance imaging. Front Syst Neurosci. 2012;6. http://journal.frontiersin.org/article/10.3389/fnsys.2012.00063/abstract.

  128. Li W, Douglas Ward B, Liu X, Chen G, Jones JL, Antuono PG, et al. Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment. J Neurol Neurosurg Psychiatry. 2015;86(10):1097–105. http://jnnp.bmj.com/lookup/doi/10.1136/jnnp-2014-309180.

    Article  Google Scholar 

  129. Reijmer YD, Fotiadis P, Piantoni G, Boulouis G, Kelly KE, Gurol ME, et al. Small vessel disease and cognitive impairment: the relevance of central network connections. Hum Brain Mapp. 2016;37(7):2446–54. http://doi.wiley.com/10.1002/hbm.23186.

    Article  PubMed  PubMed Central  Google Scholar 

  130. Brinker T, Stopa E, Morrison J, Klinge P. A new look at cerebrospinal fluid circulation. Fluids Barriers CNS. 2014;11(1):10. http://fluidsbarrierscns.biomedcentral.com/articles/10.1186/2045-8118-11-10.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Moody DM, Bell MA, Challa VR. Features of the cerebral vascular pattern that predict vulnerability to perfusion or oxygenation deficiency: an anatomic study. Am J Neuroradiol. 1990;11:431–9. https://cds.ismrm.org/ismrm-2008/files/00438.pdf.

    CAS  PubMed  PubMed Central  Google Scholar 

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Farhat, N.S., Theiss, R., Santini, T., Ibrahim, T.S., Aizenstein, H.J. (2019). Neuroimaging of Small Vessel Disease in Late-Life Depression. In: Kim, YK. (eds) Frontiers in Psychiatry. Advances in Experimental Medicine and Biology, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-32-9721-0_5

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