Neuroscience Bulletin

, Volume 35, Issue 2, pp 229–243 | Cite as

Post-Mortem MRI and Histopathology in Neurologic Disease: A Translational Approach

  • Laura E. JonkmanEmail author
  • Boyd Kenkhuis
  • Jeroen J. G. Geurts
  • Wilma D. J. van de Berg


In this review, combined post-mortem brain magnetic resonance imaging (MRI) and histology studies are highlighted, illustrating the relevance of translational approaches to define novel MRI signatures of neuropathological lesions in neuroinflammatory and neurodegenerative disorders. Initial studies combining post-mortem MRI and histology have validated various MRI sequences, assessing their sensitivity and specificity as diagnostic biomarkers in neurologic disease. More recent studies have focused on defining new radiological (bio)markers and implementing them in the clinical (research) setting. By combining neurological and neuroanatomical expertise with radiological development and pathological validation, a cycle emerges that allows for the discovery of novel MRI biomarkers to be implemented in vivo. Examples of this cycle are presented for multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, and traumatic brain injury. Some applications have been shown to be successful, while others require further validation. In conclusion, there is much to explore with post-mortem MRI and histology studies, which can eventually be of high relevance for clinical practice.


MRI Histology Biomarkers Multiple sclerosis Alzheimer’s disease Parkinson’s disease 


Conflict of interest

L. E. Jonkman is financially supported by a grant from the Alzheimer’s Association (AARF-18-566459). B. Kenkhuis has no conflict of interest. J. J. G. Geurts is an editor of MS Journal. He serves on the editorial boards of Neurology and Frontiers of Neurology and is President of the Netherlands Organization for Health Research and Innovation. He has served as a consultant for Merck-Serono, Biogen, Novartis, Genzyme, and Teva Pharmaceuticals. W. D. J. van de Berg is financially supported by grants from Amsterdam Neuroscience, ZonMW Memorabel, ZonMW Technology Hotel, Stichting Parkinson Fonds, Alzheimer Netherlands-LECMA, Roche Pharma, Lysosomal Therapeutics, and Cross-beta Sciences. She is a consultant for CHDR Leiden and Lysosomal Therapeutics.


  1. 1.
    Josephs KA, Dickson DW, Tosakulwong N, Weigand SD, Murray ME, Petrucelli L, et al. Rates of hippocampal atrophy and presence of post-mortem TDP-43 in patients with Alzheimer’s disease: a longitudinal retrospective study. Lancet Neurol 2017 16: 917–924.CrossRefGoogle Scholar
  2. 2.
    Kantarci K, Murray ME, Schwarz CG, Reid RI, Przybelski SA, Lesnick T, et al. White-matter integrity on DTI and the pathologic staging of Alzheimer’s disease. Neurobiol Aging 2017, 56: 172–179.CrossRefGoogle Scholar
  3. 3.
    Nedelska Z, Ferman TJ, Boeve BF, Przybelski SA, Lesnick TG, Murray ME, et al. Pattern of brain atrophy rates in autopsy-confirmed dementia with Lewy bodies. Neurobiol Aging 2015, 36: 452–461.CrossRefGoogle Scholar
  4. 4.
    Raman MR, Preboske GM, Przybelski SA, Gunter JL, Senjem ML, Vemuri P, et al. Antemortem MRI findings associated with microinfarcts at autopsy. Neurology 2014, 82: 1951–1958.CrossRefGoogle Scholar
  5. 5.
    Petzold A, Tozer DJ, Schmierer K. Axonal damage in the making: neurofilament phosphorylation, proton mobility and magnetisation transfer in multiple sclerosis normal appearing white matter. Exp Neurol 2011, 232: 234–349.CrossRefGoogle Scholar
  6. 6.
    Seehaus AK, Roebroeck A, Chiry O, Kim DS, Ronen I, Bratzke H, et al. Histological validation of DW-MRI tractography in human postmortem tissue. Cereb Cortex 2013, 23: 442–450.CrossRefGoogle Scholar
  7. 7.
    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.CrossRefGoogle Scholar
  8. 8.
    Meijer FJA, Goraj B. Brain MRI in Parkinson’s disease. Front Biosci (Elite Ed) 2014, 6: 360–369.CrossRefGoogle Scholar
  9. 9.
    Bell JE, Alafuzoff I, Al-Sarraj S, Arzberger T, Bogdanovic N, Budka H, et al. Management of a twenty-first century brain bank: experience in the BrainNet Europe consortium. Acta Neuropathol 2008, 115: 497–507.CrossRefGoogle Scholar
  10. 10.
    Samarasekera N, Salman RAS, Huitinga I, Klioueva N, McLean CA, Kretzschmar H, et al. Brain banking for neurological disorders. Lancet Neurol 2013, 12: 1096–1105.Google Scholar
  11. 11.
    Beach TG, Adler CH, Sue LI, Serrano G, Shill HA, Walker DG, et al. Arizona study of aging and neurodegenerative disorders and brain and body donation program. Neuropathology 2015, 35: 354–389.CrossRefGoogle Scholar
  12. 12.
    Jonkman LE, Geurts JJG. Postmortem magnetic resonance imaging. Handb Clin Neurol 2018, 150: 335–354. CrossRefGoogle Scholar
  13. 13.
    Seewann A, Kooi EJ, Roosendaal SD, Barkhof F, van der Valk P, Geurts JJG. Translating pathology in multiple sclerosis: the combination of postmortem imaging, histopathology and clinical findings. Acta Neurol Scand 2009, 119: 349–355.CrossRefGoogle Scholar
  14. 14.
    Lassmann H. Recent neuropathological findings in MS-implications for diagnosis and therapy. J Neurol 2004, 251 Suppl: IV2–IV5.Google Scholar
  15. 15.
    Lucchinetti C, Brück W, Noseworthy J. Multiple sclerosis: recent developments in neuropathology, pathogenesis, magnetic resonance imaging studies and treatment. Curr Opin Neurol 2001, 14: 259–269.CrossRefGoogle Scholar
  16. 16.
    Matthews PM, Arnold DL. Magnetic resonance imaging of multiple sclerosis: new insights linking pathology to clinical evolution. Curr Opin Neurol 2001, 14: 279–287.CrossRefGoogle Scholar
  17. 17.
    Miller DH, Grossman RI, Reingold SC, McFarland HF. The role of magnetic resonance techniques in understanding and managing multiple sclerosis. Brain 1998, 121: 3–24.CrossRefGoogle Scholar
  18. 18.
    Barkhof F, Bruck W, De Groot CJA, Bergers E, Hulshof S, Geurts J, et al. Remyelinated lesions in multiple sclerosis: magnetic resonance image appearance. Arch Neurol 2003, 60: 1073–1081.CrossRefGoogle Scholar
  19. 19.
    Zhang Y, Jonkman L, Klauser A, Barkhof F, Yong VW, Metz LM, et al. Multi-scale MRI spectrum detects differences in myelin integrity between MS lesion types. Mult Scler 2016, 22: 1569–1577.CrossRefGoogle Scholar
  20. 20.
    Yao B, Bagnato F, Matsuura E, Merkle H, Gelderen P van, Cantor FK, et al. Chronic multiple sclerosis lesions: characterization with high-field-strength MR imaging. Radiology 2012, 262: 206–215.CrossRefGoogle Scholar
  21. 21.
    Wegner C, Esiri MM, Chance SA, Palace J, Matthews PM. Neocortical neuronal, synaptic, and glial loss in multiple sclerosis. Neurology 2006, 67: 960–967.CrossRefGoogle Scholar
  22. 22.
    Peterson JW, Bö L, Mörk S, Chang A, Trapp BD. Transected neurites, apoptotic neurons, and reduced inflammation in cortical multiple sclerosis lesions. Ann Neurol 2001, 50: 389–400.CrossRefGoogle Scholar
  23. 23.
    Calabrese M, Rinaldi F, Seppi D, Favaretto A, Squarcina L, Mattisi I, et al. Cortical diffusion-tensor imaging abnormalities in multiple sclerosis: a 3-year longitudinal study. Radiology 2011, 261: 891–898.CrossRefGoogle Scholar
  24. 24.
    Roosendaal SD, Moraal B, Vrenken H, Castelijns JA, Pouwels PJW, Barkhof F, et al. In vivo MR imaging of hippocampal lesions in multiple sclerosis. J Magn Reson Imaging 2008, 27: 726–731.CrossRefGoogle Scholar
  25. 25.
    Roosendaal SD, Moraal B, Pouwels PJW, Vrenken H, Castelijns JA, Barkhof F, et al. Accumulation of cortical lesions in MS: relation with cognitive impairment. Mult Scler 2009, 15: 708–714.CrossRefGoogle Scholar
  26. 26.
    Kidd D, Barkhof F, McConnell R, Algra PR, Allen IV, Revesz T. Cortical lesions in multiple sclerosis. Brain 1999, 122: 17–26.CrossRefGoogle Scholar
  27. 27.
    Geurts JJG, Bö L, Pouwels PJW, Castelijns JA, Polman CH, Barkhof F. Cortical lesions in multiple sclerosis: combined postmortem MR imaging and histopathology. AJNR Am J Neuroradiol 2005, 26: 572–527.Google Scholar
  28. 28.
    Seewann A, Kooi EJ, Roosendaal SD, Pouwels PJW, Wattjes MP, van der Valk P, et al. Postmortem verification of MS cortical lesion detection with 3D DIR. Neurology 2012, 78: 302–308.CrossRefGoogle Scholar
  29. 29.
    Simon B, Schmidt S, Lukas C, Gieseke J, Träber F, Knol DL, et al. Improved in vivo detection of cortical lesions in multiple sclerosis using double inversion recovery MR imaging at 3 Tesla. Eur Radiol 2010, 20: 1675–1683.CrossRefGoogle Scholar
  30. 30.
    Geurts JJG, Roosendaal SD, Calabrese M, Ciccarelli O, Agosta F, Chard DT, et al. Consensus recommendations for MS cortical lesion scoring using double inversion recovery MRI. Neurology 2011, 76: 418–424.CrossRefGoogle Scholar
  31. 31.
    Kilsdonk ID, Jonkman LE, Klaver R, van Veluw SJ, Zwanenburg JJM, Kuijer JPA, et al. Increased cortical grey matter lesion detection in multiple sclerosis with 7 T MRI: a post-mortem verification study. Brain 2016, 139: 1472–1481.CrossRefGoogle Scholar
  32. 32.
    Jonkman LE, Klaver R, Fleysher L, Inglese M, Geurts JJG. Ultra-high-field MRI visualization of cortical multiple sclerosis lesions with T2 and T2*: a postmortem MRI and histopathology study. AJNR Am J Neuroradiol 2015, 36: 2062–2067.CrossRefGoogle Scholar
  33. 33.
    Pitt D, Boster A, Pei W, Wohleb E, Jasne A, Zachariah CR, et al. Imaging cortical lesions in multiple sclerosis with ultra-high-field magnetic resonance imaging. Arch Neurol 2010, 67: 812–818.CrossRefGoogle Scholar
  34. 34.
    Beach TG, Monsell SE, Phillips LE, Kukull W. Accuracy of the clinical diagnosis of Alzheimer disease at national institute on aging Alzheimer disease centers, 2005–2010. J Neuropathol Exp Neurol 2012, 71: 266–273.CrossRefGoogle Scholar
  35. 35.
    Apostolova LG, Zarow C, Biado K, Hurtz S, Boccardi M, Somme J, et al. Relationship between hippocampal atrophy and neuropathology markers: a 7T MRI validation study of the EADC-ADNI harmonized hippocampal segmentation protocol. Alzheimer’s Dement 2015, 11: 139–150.CrossRefGoogle Scholar
  36. 36.
    Apostolova LG, Mosconi L, Thompson PM, Green AE, Hwang KS, Ramirez A, et al. Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiol Aging 2010, 31: 1077–1088.CrossRefGoogle Scholar
  37. 37.
    De Leon MJ, George AE, Golomb J, Tarshish C, Convit A, Kluger A, et al. Frequency of hippocampal formation atrophy in normal aging and Alzheimer’s disease. Neurobiol Aging 1997, 18: 1–11.CrossRefGoogle Scholar
  38. 38.
    Apostolova LG, Dinov ID, Dutton RA, Hayashi KM, Toga AW, Cummings JL, et al. 3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and Alzheimer’s disease. Brain 2006, 129: 2867–2873.CrossRefGoogle Scholar
  39. 39.
    Apostolova LG, Dutton RA, Dinov ID, Hayashi KM, Toga AW, Cummings JL, et al. Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps. Arch Neurol 2006, 63: 693.CrossRefGoogle Scholar
  40. 40.
    Jack CR, Dickson DW, Parisi JE, Xu YC, Cha RH, O’Brien PC, et al. Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology 2002, 58: 750–757.CrossRefGoogle Scholar
  41. 41.
    Giuliano A, Donatelli G, Cosottini M, Tosetti M, Retico A, Fantacci ME. Hippocampal subfields at ultra high field MRI: an overview of segmentation and measurement methods. Hippocampus 2017, 27: 481–494.CrossRefGoogle Scholar
  42. 42.
    Benveniste H, Einstein G, Kim KR, Hulette C, Johnson GA. Detection of neuritic plaques in Alzheimer’s disease by magnetic resonance microscopy. Proc Natl Acad Sci U S A 1999, 96: 14079–14084.CrossRefGoogle Scholar
  43. 43.
    Meadowcroft MD, Connor JR, Smith MB, Yang QX. MRI and histological analysis of beta-amyloid plaques in both human Alzheimer’s disease and APP/PS1 transgenic mice. J Magn Reson Imaging 2009, 29: 997–1007.CrossRefGoogle Scholar
  44. 44.
    Nabuurs RJA, Hegeman I, Natté R, van Duinen SG, van Buchem MA, van der Weerd L, et al. High-field MRI of single histological slices using an inductively coupled, self-resonant microcoil: application to ex vivo samples of patients with Alzheimer’s disease. NMR Biomed 2011, 24: 351–357.Google Scholar
  45. 45.
    Nabuurs RJA, Natté R, de Ronde FM, Hegeman-Kleinn I, Dijkstra J, van Duinen SG, et al. MR microscopy of human Amyloid-β deposits: characterization of parenchymal amyloid, diffuse plaques, and vascular amyloid. J Alzheimer’s Dis 2013, 34: 1037–1049.CrossRefGoogle Scholar
  46. 46.
    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.CrossRefGoogle Scholar
  47. 47.
    Bulk M, Abdelmoula WM, Nabuurs RJA, van der Graaf LM, Mulders CWH, Mulder AA, et al. Postmortem MRI and histology demonstrate differential iron accumulation and cortical myelin organization in early- and late-onset Alzheimer’s disease. Neurobiol Aging 2018, 62: 231–242.CrossRefGoogle Scholar
  48. 48.
    Kenkhuis B, Jonkman LE, Bulk M, Buijs M, Boon BDC, Bouwman FH, et al. 7T MRI allows detection of disturbed cortical lamination in medial temporal lobe in patients with Alzheimer’s disease. Neuroimage Clin 2019. Scholar
  49. 49.
    Attems J, Jellinger KA. The overlap between vascular disease and Alzheimer’s disease—lessons from pathology. BMC Med 2014, 12: 206.CrossRefGoogle Scholar
  50. 50.
    Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010, 9: 689–701.CrossRefGoogle Scholar
  51. 51.
    Kövari E, Herrmann FR, Hof PR, Bouras C. The relationship between cerebral amyloid angiopathy and cortical microinfarcts in brain ageing and Alzheimer’s disease. Neuropathol Appl Neurobiol 2013, 39: 498–509.CrossRefGoogle Scholar
  52. 52.
    Soontornniyomkij V, Lynch MD, Mermash S, Pomakian J, Badkoobehi H, Clare R, et al. Cerebral microinfarcts associated with severe cerebral β-amyloid angiopathy. Brain Pathol 2010, 20: 459–467.CrossRefGoogle Scholar
  53. 53.
    Niwa A, Ii Y, Shindo A, Matsuo K, Ishikawa H, Taniguchi A, et al. Comparative analysis of cortical microinfarcts and microbleeds using 3.0-Tesla postmortem magnetic resonance images and histopathology. J Alzheimer’s Dis 2017, 59: 951–959.Google Scholar
  54. 54.
    De Reuck JL, Deramecourt V, Auger F, Durieux N, Cordonnier C, Devos D, et al. The significance of cortical cerebellar microbleeds and microinfarcts in neurodegenerative and cerebrovascular diseases. Cerebrovasc Dis 2015, 39: 138–143.CrossRefGoogle Scholar
  55. 55.
    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: 1683–1689.CrossRefGoogle Scholar
  56. 56.
    Mortamais M, Artero S, Ritchie K. White matter hyperintensities as early and independent predictors of Alzheimer’s disease risk. J Alzheimer’s Dis 2014, 42: S393–S400.CrossRefGoogle Scholar
  57. 57.
    Benedictus MR, Prins ND, Goos JDC, Scheltens P, Barkhof F, van der Flier WM. Microbleeds, mortality, and stroke in Alzheimer Disease. JAMA Neurol 2015, 72: 539.CrossRefGoogle Scholar
  58. 58.
    Fazekas F, Kleinert R, Roob G, Kleinert G, Kapeller P, Schmidt R, et al. Histopathologic analysis of foci of signal loss on gradient-echo T2*-weighted MR images in patients with spontaneous intracerebral hemorrhage: evidence of microangiopathy-related microbleeds. AJNR Am J Neuroradiol 1999, 20: 637–642.Google Scholar
  59. 59.
    Lauer A, van Veluw SJ, William CM, Charidimou A, Roongpiboonsopit D, Vashkevich A, et al. Microbleeds on MRI are associated with microinfarcts on autopsy in cerebral amyloid angiopathy. Neurology 2016, 87: 1488–1492.CrossRefGoogle Scholar
  60. 60.
    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: 425–436.CrossRefGoogle Scholar
  61. 61.
    van Veluw SJ, Zwanenburg JJ, Engelen-Lee J, Spliet WG, Hendrikse J, Luijten PR, et al. In vivo detection of cerebral cortical microinfarcts with high-resolution 7T MRI. J Cereb Blood Flow Metab 2013, 33: 322–329.CrossRefGoogle Scholar
  62. 62.
    Hilal S, Sikking E, Shaik MA, Chan QL, van Veluw SJ, Vrooman H, et al. Cortical cerebral microinfarcts on 3T MRI. Neurology 2016, 87: 1583–1590.CrossRefGoogle Scholar
  63. 63.
    Ferro DA, van Veluw SJ, Koek HL, Exalto LG, Biessels GJ, Utrecht Vascular Cognitive Impairment (VCI) study group. Cortical cerebral microinfarcts on 3 Tesla MRI in patients with vascular cognitive impairment. J Alzheimer’s Dis 2017, 60: 1443–1450.Google Scholar
  64. 64.
    Braak H, Del Tredici K, Rüb U, de Vos RAI, Jansen Steur ENH, Braak E. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol Aging 24: 197–211.Google Scholar
  65. 65.
    Massey LA, Yousry TA. Anatomy of the substantia nigra and subthalamic nucleus on MR imaging. Neuroimaging Clin N Am 2010, 20: 7–27.CrossRefGoogle Scholar
  66. 66.
    Dormont D, Ricciardi KG, Tandé D, Parain K, Menuel C, Galanaud D, et al. Is the subthalamic nucleus hypointense on T2-weighted images? A correlation study using MR imaging and stereotactic atlas data. AJNR Am J Neuroradiol 2004, 5: 1516–1523.Google Scholar
  67. 67.
    Rijkers K, Temel Y, Visser-Vandewalle V, Vanormelingen L, Vandersteen M, Adriaensens P, et al. The microanatomical environment of the subthalamic nucleus. J Neurosurg 2007, 107: 198–201.CrossRefGoogle Scholar
  68. 68.
    Massey LA, Miranda MA, Zrinzo L, Al-Helli O, Parkes HG, Thornton JS, et al. High resolution MR anatomy of the subthalamic nucleus: imaging at 9.4T with histological validation. Neuroimage 2012, 59: 2035–2044.Google Scholar
  69. 69.
    Al-Helli O, Thomas DL, Massey L, Foltynie T, Limousin P, Holton JL, et al. Deep brain stimulation of the subthalamic nucleus: histological verification and 9.4-T MRI correlation. Acta Neurochir (Wien) 2015, 157: 2143–2147.Google Scholar
  70. 70.
    Ewert S, Plettig P, Li N, Chakravarty MM, Collins DL, Herrington TM, et al. Toward defining deep brain stimulation targets in MNI space: a subcortical atlas based on multimodal MRI, histology and structural connectivity. Neuroimage 2018, 170: 271–282.CrossRefGoogle Scholar
  71. 71.
    McClelland S, Vonsattel JP, Garcia RE, Amaya MD, Winfield LM, Pullman SL, et al. Relationship of clinical efficacy to postmortem-determined anatomic subthalamic stimulation in Parkinson syndrome. Clin Neuropathol 2007, 26: 267–275.CrossRefGoogle Scholar
  72. 72.
    Vedam-Mai V, Yachnis A, Ullman M, Javedan SP, Okun MS. Postmortem observation of collagenous lead tip region fibrosis as a rare complication of DBS. Mov Disord 2012, 27: 565–569.CrossRefGoogle Scholar
  73. 73.
    Sun DA, Yu H, Spooner J, Tatsas AD, Davis T, Abel TW, et al. Postmortem analysis following 71 months of deep brain stimulation of the subthalamic nucleus for Parkinson disease. J Neurosurg 2008, 109: 325–329.CrossRefGoogle Scholar
  74. 74.
    Kitao S, Matsusue E, Fujii S, Miyoshi F, Kaminou T, Kato S, et al. Correlation between pathology and neuromelanin MR imaging in Parkinson’s disease and dementia with Lewy bodies. Neuroradiology 2013, 55: 947–953.CrossRefGoogle Scholar
  75. 75.
    Damier P, Hirsch EC, Agid Y, Graybiel AM. The substantia nigra of the human brain. II. Patterns of loss of dopamine-containing neurons in Parkinson’s disease. Brain 1999, 122 (Pt 8):1437–1448.Google Scholar
  76. 76.
    Blazejewska AI, Schwarz ST, Pitiot A, Stephenson MC, Lowe J, Bajaj N, et al. Visualization of nigrosome 1 and its loss in PD: pathoanatomical correlation and in vivo 7 T MRI. Neurology 2013, 81: 534–540.CrossRefGoogle Scholar
  77. 77.
    Massey LA, Miranda MA, Al-Helli O, Parkes HG, Thornton JS, So P-W, et al. 9.4 T MR microscopy of the substantia nigra with pathological validation in controls and disease. Neuroimage Clin 2017, 13: 154–163.Google Scholar
  78. 78.
    Damier P, Hirsch EC, Agid Y, Graybiel AM. The substantia nigra of the human brain. Brain 1999, 122: 1437–1448.CrossRefGoogle Scholar
  79. 79.
    Schwarz ST, Mougin O, Xing Y, Blazejewska A, Bajaj N, Auer DP, et al. Parkinson’s disease related signal change in the nigrosomes 1–5 and the substantia nigra using T2* weighted 7T MRI. Neuroimage Clin 2018, 19: 683–689.CrossRefGoogle Scholar
  80. 80.
    Zarow C, Lyness SA, Mortimer JA, Chui HC. Neuronal loss is greater in the locus coeruleus than nucleus basalis and substantia nigra in Alzheimer and Parkinson diseases. Arch Neurol 2003, 60: 337–341.CrossRefGoogle Scholar
  81. 81.
    Vazey EM, Aston-Jones G. The emerging role of norepinephrine in cognitive dysfunctions of Parkinson’s disease. Front Behav Neurosci 2012, 6: 48.CrossRefGoogle Scholar
  82. 82.
    Keren NI, Taheri S, Vazey EM, Morgan PS, Granholm ACE, Aston-Jones GS, et al. Histologic validation of locus coeruleus MRI contrast in post-mortem tissue. Neuroimage 2015, 113: 235–245.Google Scholar
  83. 83.
    Birkl C, Langkammer C, Golob-Schwarzl N, Leoni M, Haybaeck J, Goessler W, et al. Effects of formalin fixation and temperature on MR relaxation times in the human brain. NMR Biomed 2016, 29: 458–465.CrossRefGoogle Scholar
  84. 84.
    Shatil AS, Uddin MN, Matsuda KM, Figley CR. Quantitative Ex Vivo MRI changes due to progressive formalin fixation in whole human brain specimens: longitudinal characterization of diffusion, relaxometry, and myelin water fraction measurements at 3T. Front Med 2018, 5: 31.CrossRefGoogle Scholar
  85. 85.
    Priovoulos N, Jacobs HIL, Ivanov D, Uludağ K, Verhey FRJ, Poser BA. High-resolution in vivo imaging of human locus coeruleus by magnetization transfer MRI at 3T and 7T. Neuroimage 2018, 168: 427–436.CrossRefGoogle Scholar
  86. 86.
    Jones NR, Blumbergs PC, Brown CJ, McLean AJ, Manavis J, Perrett LV, et al. Correlation of postmortem MRI and CT appearances with neuropathology in brain trauma: a comparison of two methods. J Clin Neurosci 1998, 5: 73–79.CrossRefGoogle Scholar
  87. 87.
    Hesselink JR, Dowd CF, Healy ME, Hajek P, Baker LL, Luerssen TG. MR imaging of brain contusions: a comparative study with CT. AJR Am J Roentgenol 1988, 150: 1133–1142.CrossRefGoogle Scholar
  88. 88.
    Vilela P, Rowley HA. Brain ischemia: CT and MRI techniques in acute ischemic stroke. Eur J Radiol 2017, 96: 162–172.CrossRefGoogle Scholar
  89. 89.
    Milidonis X, Marshall I, Macleod MR, Sena ES. Magnetic resonance imaging in experimental stroke and comparison with histology. Stroke 2015, 46: 843–851.CrossRefGoogle Scholar
  90. 90.
    Knight MJ, McGarry BL, Rogers HJ, Jokivarsi KT, Gröhn OH, Kauppinen RA. A spatiotemporal theory for MRI T2 relaxation time and apparent diffusion coefficient in the brain during acute ischaemia: application and validation in a rat acute stroke model. J Cereb Blood Flow Metab 2016, 36: 1232–1243.CrossRefGoogle Scholar
  91. 91.
    Holleran L, Kim JH, Gangolli M, Stein T, Alvarez V, McKee A, et al. Axonal disruption in white matter underlying cortical sulcus tau pathology in chronic traumatic encephalopathy. Acta Neuropathol 2017, 133: 367–380.CrossRefGoogle Scholar
  92. 92.
    Ruprecht R, Scheurer E, Lenz C. Systematic review on the characterization of chronic traumatic encephalopathy by MRI and MRS. J Magn Reson Imaging 2019, 49: 212–228.CrossRefGoogle Scholar
  93. 93.
    Yelnik J, Bardinet E, Dormont D, Malandain G, Ourselin S, Tandé D, et al. A three-dimensional, histological and deformable atlas of the human basal ganglia. I. Atlas construction based on immunohistochemical and MRI data. Neuroimage 2007, 34: 618–638.Google Scholar
  94. 94.
    Chakravarty MM, Bertrand G, Hodge CP, Sadikot AF, Collins DL. The creation of a brain atlas for image guided neurosurgery using serial histological data. Neuroimage 2006, 30: 359–376.CrossRefGoogle Scholar
  95. 95.
    Cardinale F, Chinnici G, Bramerio M, Mai R, Sartori I, Cossu M, et al. Validation of FreeSurfer-estimated brain cortical thickness: comparison with histologic measurements. Neuroinformatics 2014, 12: 535–542.CrossRefGoogle Scholar
  96. 96.
    Morel A, Magnin M, Jeanmonod D. Multiarchitectonic and stereotactic atlas of the human thalamus. J Comp Neurol 1997, 387: 588–630.CrossRefGoogle Scholar
  97. 97.
    Gaugler JE, Ascher-Svanum H, Roth DL, Fafowora T, Siderowf A, Beach TG. Characteristics of patients misdiagnosed with Alzheimer’s disease and their medication use: an analysis of the NACC-UDS database. BMC Geriatr 2013, 13: 137.CrossRefGoogle Scholar
  98. 98.
    Rizzo G, Copetti M, Arcuti S, Martino D, Fontana A, Logroscino G. Accuracy of clinical diagnosis of Parkinson disease. Neurology 2016, 86: 566–576.CrossRefGoogle Scholar
  99. 99.
    Yan F, He N, Lin H, Li R. Iron deposition quantification: applications in the brain and liver. J Magn Reson Imaging 2018, 48: 301–317.CrossRefGoogle Scholar
  100. 100.
    Yoo Y, Tang LYW, Brosch T, Li DKB, Kolind S, Vavasour I, et al. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. Neuroimage Clin 2018, 17: 169–178.CrossRefGoogle Scholar
  101. 101.
    Ambastha AK, Leong TY, Alzheimer’s Disease Neuroimaging Initiative. A deep learning approach to neuroanatomical characterisation of Alzheimer’s disease. Stud Health Technol Inform 2017, 245: 1249.Google Scholar
  102. 102.
    Jonkman LE, Fleysher L, Steenwijk MD, Koeleman JA, de Snoo TP, Barkhof F, et al. Ultra-high field MTR and qR2* differentiates subpial cortical lesions from normal-appearing gray matter in multiple sclerosis. Mult Scler 2016, 22: 1306–1314.CrossRefGoogle Scholar
  103. 103.
    Derakhshan M, Caramanos Z, Narayanan S, Arnold DL, Louis Collins D. Surface-based analysis reveals regions of reduced cortical magnetization transfer ratio in patients with multiple sclerosis: a proposed method for imaging subpial demyelination. Hum Brain Mapp 2014, 35: 3402–3413.CrossRefGoogle Scholar
  104. 104.
    Oguz I, Yaxley R, Budin F, Hoogstoel M, Lee J, Maltbie E, et al. Comparison of magnetic resonance imaging in live vs. post mortem rat brains. PLoS One 2013, 8: e71027.Google Scholar
  105. 105.
    Scheurer E, Lovblad KO, Kreis R, Maier SE, Boesch C, Dirnhofer R, et al. Forensic application of postmortem diffusion-weighted and diffusion tensor MR imaging of the human brain in situ. Am J Neuroradiol 2011, 32: 1518–1524.CrossRefGoogle Scholar
  106. 106.
    Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 1991, 82: 239–259.CrossRefGoogle Scholar
  107. 107.
    Ikonomovic MD, Klunk WE, Abrahamson EE, Mathis CA, Price JC, Tsopelas ND, et al. Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer’s disease. Brain 2008, 131(Pt 6): 1630–1645.CrossRefGoogle Scholar

Copyright information

© Shanghai Institutes for Biological Sciences, CAS 2019

Authors and Affiliations

  • Laura E. Jonkman
    • 1
    Email author
  • Boyd Kenkhuis
    • 2
    • 3
  • Jeroen J. G. Geurts
    • 1
  • Wilma D. J. van de Berg
    • 1
  1. 1.Department of Anatomy and Neurosciences, Amsterdam NeuroscienceAmsterdam UMC, Vrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Department of Human GeneticsLeiden University Medical CenterLeidenThe Netherlands
  3. 3.Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands

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