Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Brain Iron Imaging in Aging and Cognitive Disorders: MRI Approaches

노화 및 인지기능장애에서 뇌 철 영상 기법: 자기공명영상을 이용한 접근

  • Jinhee Jang (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Junghwa Kang (Division of Biomedical Engineering, Hankuk University of Foreign Studies) ;
  • Yoonho Nam (Division of Biomedical Engineering, Hankuk University of Foreign Studies)
  • 장진희 (가톨릭대학교 의과대학 서울성모병원 영상의학과) ;
  • 강정화 (한국외국어대학교 바이오메디컬공학부) ;
  • 남윤호 (한국외국어대학교 바이오메디컬공학부)
  • Received : 2022.03.29
  • Accepted : 2022.05.16
  • Published : 2022.05.01
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Abstract

Iron has a vital role in the human body, including the central nervous system. Increased deposition of iron in the brain has been reported in aging and important neurodegenerative diseases. Owing to the unique magnetic resonance properties of iron, MRI has great potential for in vivo assessment of iron deposition, distribution, and non-invasive quantification. In this paper, we will review the MRI methods for iron assessment and their changes in aging and neurodegenerative diseases, focusing on Alzheimer's disease. In addition, we will summarize the limitations of current approaches and introduce new areas and MRI methods for iron imaging that are expected in the future.

철은 중추신경계 및 인체에 필수적인 성분으로 노화 및 다양한 퇴행성 뇌질환에서 뇌의 철 침착이 증가된다. 철은 MRI에서 독특한 특성을 가지고 있어 인체의 철 침착과 분포를 비침 습적으로 평가 및 정량화가 가능하다. 이 종설에서는 철 영상을 위한 MRI 기법에 대하여 알아보고, 노화 및 알츠하이머병을 포함한 퇴행성 뇌질환에서 변화를 고찰해 보고자 한다. 또한 현재 접근법의 제한점과 앞으로 기대되는 새로운 접근도 확인해 보고자 한다.

Keywords

Acknowledgement

This work was supported by National Research Foundation of Korea funded by the Korea government (MSIT) (NRF-2020R1C1C1012320) and Hankuk University of Foreign Studies Research Fund (20211238001).

References

  1. Rouault TA. Iron metabolism in the CNS: implications for neurodegenerative diseases. Nat Rev Neurosci 2013;14:551-564 https://doi.org/10.1038/nrn3453
  2. Ropele S, Langkammer C. Iron quantification with susceptibility. NMR Biomed 2017;30:e3534
  3. Wang Y, Spincemaille P, Liu Z, Dimov A, Deh K, Li J, et al. Clinical quantitative susceptibility mapping (QSM): biometal imaging and its emerging roles in patient care. J Magn Reson Imaging 2017;46:951-971 https://doi.org/10.1002/jmri.25693
  4. Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med 2004; 52:612-618 https://doi.org/10.1002/mrm.20198
  5. Jeon BU, Yu IK, Kim TK, Kim HY, Hwang S. Susceptibility-weighted imaging as a distinctive imaging technique for providing complementary information for precise diagnosis of neurologic disorder. J Korean Soc Radiol 2021;82:99-115 https://doi.org/10.3348/jksr.2020.0054
  6. Connor JR, Menzies SL. Cellular management of iron in the brain. J Neurol Sci 1995;134 Suppl:33-44 https://doi.org/10.1016/0022-510X(95)00206-H
  7. Todorich B, Pasquini JM, Garcia CI, Paez PM, Connor JR. Oligodendrocytes and myelination: the role of iron. Glia 2009;57:467-478 https://doi.org/10.1002/glia.20784
  8. Langkammer C, Schweser F, Krebs N, Deistung A, Goessler W, Scheurer E, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 2012;62:1593-1599 https://doi.org/10.1016/j.neuroimage.2012.05.049
  9. Lee S, Nam Y, Jang J, Na GH, Kim DG, Shin NY, et al. Deep gray matter iron measurement in patients with liver cirrhosis using quantitative susceptibility mapping: relationship with pallidal T1 hyperintensity. J Magn Reson Imaging 2018;47:1342-1349 https://doi.org/10.1002/jmri.25841
  10. Nam Y, Gho SM, Kim DH, Kim EY, Lee J. Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI). J Magn Reson Imaging 2017;46:528-536 https://doi.org/10.1002/jmri.25553
  11. Bae YJ, Kim JM, Sohn CH, Choi JH, Choi BS, Song YS, et al. Imaging the substantia nigra in Parkinson disease and other Parkinsonian syndromes. Radiology 2021;300:260-278 https://doi.org/10.1148/radiol.2021203341
  12. Duyn JH, Schenck J. Contributions to magnetic susceptibility of brain tissue. NMR Biomed 2017;30:e3546
  13. Treit S, Naji N, Seres P, Rickard J, Stolz E, Wilman AH, et al. R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90years. Hum Brain Mapp 2021;42:4597-4610 https://doi.org/10.1002/hbm.25569
  14. Chavhan GB, Babyn PS, Thomas B, Shroff MM, Haacke EM. Principles, techniques, and applications of T2*-based MR imaging and its special applications. Radiographics 2009;29:1433-1449 https://doi.org/10.1148/rg.295095034
  15. Roh K, Kang H, Kim I. Clinical applications of neuroimaging with susceptibility weighted imaging. J Korean Soc Magn Reson Med 2014;18:290-302 https://doi.org/10.13104/jksmrm.2014.18.4.290
  16. Haller S, Haacke EM, Thurnher MM, Barkhof F. Susceptibility-weighted imaging: technical essentials and clinical neurologic applications. Radiology 2021;299:3-26 https://doi.org/10.1148/radiol.2021203071
  17. Alsop DC, Detre JA, Golay X, Gunther M, Hendrikse J, Hernandez-Garcia L, et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 2015;73:102-116 https://doi.org/10.1002/mrm.25197
  18. Jung W, Yoon J, Ji S, Choi JY, Kim JM, Nam Y, et al. Exploring linearity of deep neural network trained QSM: QSMnet. Neuroimage 2020;211:116619
  19. Mrak RE, Griffin ST, Graham DI. Aging-associated changes in human brain. J Neuropathol Exp Neurol 1997;56:1269-1275 https://doi.org/10.1097/00005072-199712000-00001
  20. Bilgic B, Pfefferbaum A, Rohlfing T, Sullivan EV, Adalsteinsson E. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage 2012;59:2625-2635 https://doi.org/10.1016/j.neuroimage.2011.08.077
  21. Harder SL, Hopp KM, Ward H, Neglio H, Gitlin J, Kido D. Mineralization of the deep gray matter with age: a retrospective review with susceptibility-weighted MR imaging. AJNR Am J Neuroradiol 2008;29:176-183 https://doi.org/10.3174/ajnr.A0770
  22. Hallgren B, Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem 1958;3:41-51 https://doi.org/10.1111/j.1471-4159.1958.tb12607.x
  23. Jang J, Nam Y, Jung SW, Riew TR, Kim SH, Kim IB. Paradoxical paramagnetic calcifications in the globus pallidus: an ex vivo MR investigation and histological validation study. NMR Biomed 2021;34:e4571
  24. Kim H, Jang J, Kang J, Jang S, Nam Y, Choi Y, et al. Clinical implications of focal mineral deposition in the globus pallidus on CT and quantitative susceptibility mapping of MRI. Korean J Radiol 2022 Jun [Epub]. https://doi.org/10.3348/kjr.2022.0003
  25. Shoamanesh A, Kwok CS, Benavente O. Cerebral microbleeds: histopathological correlation of neuroimaging. Cerebrovasc Dis 2011;32:528-534 https://doi.org/10.1159/000331466
  26. Shams S, Martola J, Cavallin L, Granberg T, Shams M, Aspelin P, et al. SWI or T2* : which MRI sequence to use in the detection of cerebral microbleeds? The Karolinska imaging dementia study. AJNR Am J Neuroradiol 2015;36:1089-1095 https://doi.org/10.3174/ajnr.A4248
  27. Poels MM, Vernooij MW, Ikram MA, Hofman A, Krestin GP, van der Lugt A, et al. Prevalence and risk factors of cerebral microbleeds: an update of the Rotterdam scan study. Stroke 2010;41(10 Suppl):S103-S106 https://doi.org/10.1161/STROKEAHA.110.595181
  28. Ding J, Sigurdsson S, Garcia M, Phillips CL, Eiriksdottir G, Gudnason V, et al. Risk factors associated with incident cerebral microbleeds according to location in older people: the age, gene/environment susceptibility (AGES)-Reykjavik Study. JAMA Neurol 2015;72:682-688 https://doi.org/10.1001/jamaneurol.2015.0174
  29. 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;12:822-838 https://doi.org/10.1016/S1474-4422(13)70124-8
  30. Ding J, Sigurdsson S, Jonsson PV, Eiriksdottir G, Meirelles O, Kjartansson O, et al. Space and location of cerebral microbleeds, cognitive decline, and dementia in the community. Neurology 2017;88:2089-2097 https://doi.org/10.1212/WNL.0000000000003983
  31. Lee SH, Bae HJ, Kwon SJ, Kim H, Kim YH, Yoon BW, et al. Cerebral microbleeds are regionally associated with intracerebral hemorrhage. Neurology 2004;62:72-76 https://doi.org/10.1212/01.WNL.0000101463.50798.0D
  32. Byun H, Jang J, Choi HS, Jung SL, Ahn KJ, Kim BS. Associations between morphological characteristics of intracranial arteries and atherosclerosis risk factors in subjects with less than 50% intracranial arterial stenosis. Investig Magn Reson Imaging 2018;22:150-157 https://doi.org/10.13104/imri.2018.22.3.150
  33. Jack CR Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013;12:207-216 https://doi.org/10.1016/S1474-4422(12)70291-0
  34. Park M, Moon WJ. Structural MR imaging in the diagnosis of Alzheimer's disease and other neurodegenerative dementia: current imaging approach and future perspectives. Korean J Radiol 2016;17:827-845 https://doi.org/10.3348/kjr.2016.17.6.827
  35. Lopes KO, Sparks DL, Streit WJ. Microglial dystrophy in the aged and Alzheimer's disease brain is associated with ferritin immunoreactivity. Glia 2008;56:1048-1060 https://doi.org/10.1002/glia.20678
  36. Hametner S, Wimmer I, Haider L, Pfeifenbring S, Bruck W, Lassmann H. Iron and neurodegeneration in the multiple sclerosis brain. Ann Neurol 2013;74:848-861 https://doi.org/10.1002/ana.23974
  37. Stephenson E, Nathoo N, Mahjoub Y, Dunn JF, Yong VW. Iron in multiple sclerosis: roles in neurodegeneration and repair. Nat Rev Neurol 2014;10:459-468 https://doi.org/10.1038/nrneurol.2014.118
  38. Bush AI. The metal theory of Alzheimer's disease. J Alzheimers Dis 2013;33 Suppl 1:S277-S281 https://doi.org/10.3233/JAD-2012-129011
  39. Zhu WZ, Zhong WD, Wang W, Zhan CJ, Wang CY, Qi JP, et al. Quantitative MR phase-corrected imaging to investigate increased brain iron deposition of patients with Alzheimer disease. Radiology 2009;253:497-504 https://doi.org/10.1148/radiol.2532082324
  40. Acosta-Cabronero J, Williams GB, Cardenas-Blanco A, Arnold RJ, Lupson V, Nestor PJ. In vivo quantitative susceptibility mapping (QSM) in Alzheimer's disease. PLoS One 2013;8:e81093
  41. Kim HG, Park S, Rhee HY, Lee KM, Ryu CW, Rhee SJ, et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer's disease. Neuroimage Clin 2017;16:429-438 https://doi.org/10.1016/j.nicl.2017.08.019
  42. Moon Y, Han SH, Moon WJ. Patterns of brain iron accumulation in vascular dementia and Alzheimer's dementia using quantitative susceptibility mapping imaging. J Alzheimers Dis 2016;51:737-745 https://doi.org/10.3233/JAD-151037
  43. Zeineh MM, Chen Y, Kitzler HH, Hammond R, Vogel H, Rutt BK. Activated iron-containing microglia in the human hippocampus identified by magnetic resonance imaging in Alzheimer disease. Neurobiol Aging 2015;36:2483-2500 https://doi.org/10.1016/j.neurobiolaging.2015.05.022
  44. Ayton S, Fazlollahi A, Bourgeat P, Raniga P, Ng A, Lim YY, et al. Cerebral quantitative susceptibility mapping predicts amyloid-β-related cognitive decline. Brain 2017;140:2112-2119 https://doi.org/10.1093/brain/awx137
  45. Wang M, Hu HY, Wang ZT, Ou YN, Qu Y, Ma YH, et al. Association of cerebral microbleeds with risks of cognitive impairment and dementia: a systematic review and meta-analysis of prospective studies. Brain Disorders 2021;2:100010
  46. Akoudad S, Wolters FJ, Viswanathan A, de Bruijn RF, van der Lugt A, Hofman A, et al. Association of cerebral microbleeds with cognitive decline and dementia. JAMA Neurol 2016;73:934-943 https://doi.org/10.1001/jamaneurol.2016.1017
  47. Ravanfar P, Loi SM, Syeda WT, Van Rheenen TE, Bush AI, Desmond P, et al. Systematic review: quantitative susceptibility mapping (QSM) of brain iron profile in neurodegenerative diseases. Front Neurosci 2021;15: 618435
  48. Choi Y, Jang J, Kim J, Nam Y, Shin NY, Ahn KJ, et al. MRI and quantitative magnetic susceptibility maps of the brain after serial administration of gadobutrol: a longitudinal follow-up study. Radiology 2020;297:143-150 https://doi.org/10.1148/radiol.2020192579
  49. Shin HG, Lee J, Yun YH, Yoo SH, Jang J, Oh SH, et al. χ-separation: magnetic susceptibility source separation toward iron and myelin mapping in the brain. Neuroimage 2021;240:118371
  50. Hametner S, Endmayr V, Deistung A, Palmrich P, Prihoda M, Haimburger E, et al. The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation - A biochemical and histological validation study. Neuroimage 2018;179:117-133 https://doi.org/10.1016/j.neuroimage.2018.06.007
  51. Gong NJ, Dibb R, Bulk M, van der Weerd L, Liu C. Imaging beta amyloid aggregation and iron accumulation in Alzheimer's disease using quantitative susceptibility mapping MRI. Neuroimage 2019;191:176-185 https://doi.org/10.1016/j.neuroimage.2019.02.019
  52. Hametner S, Dal Bianco A, Trattnig S, Lassmann H. Iron related changes in MS lesions and their validity to characterize MS lesion types and dynamics with ultra-high field magnetic resonance imaging. Brain Pathol 2018;28:743-749 https://doi.org/10.1111/bpa.12643
  53. Wehrli FW, Fan AP, Rodgers ZB, Englund EK, Langham MC. Susceptibility-based time-resolved whole-organ and regional tissue oximetry. NMR Biomed 2017;30:e3495
  54. Lee H, Englund EK, Wehrli FW. Interleaved quantitative BOLD: combining extravascular R2'- and intravascular R2-measurements for estimation of deoxygenated blood volume and hemoglobin oxygen saturation. Neuroimage 2018;174:420-431 https://doi.org/10.1016/j.neuroimage.2018.03.043
  55. Lin Z, Sur S, Soldan A, Pettigrew C, Miller M, Oishi K, et al. Brain oxygen extraction by using MRI in older individuals: relationship to apolipoprotein E genotype and amyloid burden. Radiology 2019;292:140-148 https://doi.org/10.1148/radiol.2019182726
  56. Jang J, Oh SH, Nam Y, Lee K, Choi HS, Jung SL, et al. Prognostic value of phase information of 2D T2*-weighted gradient echo brain imaging in cardiac arrest survivors: a preliminary study. Resuscitation 2019;140:142-149 https://doi.org/10.1016/j.resuscitation.2019.05.026