Advertisement

Identifying patients with neuronal intranuclear inclusion disease in Singapore using characteristic diffusion-weighted MR images

  • Wai-Yung Yu
  • Zheyu Xu
  • Hwei-Yee Lee
  • Aya Tokumaru
  • Jeanne M. M. Tan
  • Adeline Ng
  • Shigeo Murayama
  • C. C. Tchoyoson LimEmail author
Diagnostic Neuroradiology
  • 18 Downloads

Abstract

Purpose

Adult-onset neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder described mainly in the Japanese population, with characteristic DWI abnormalities at the junction between gray and white matter. We identify possible cases of NIID in the picture archive and communication system (PACS) of a tertiary neurological referral hospital in Singapore and describe their radiological features.

Methods

The neuroradiology imaging database was reviewed using keyword search of radiological reports to identify patients who had “subcortical U fibre” abnormalities on DWI. MRI were retrospectively reviewed, and those fulfilling inclusion criteria were invited for skin biopsy to detect nuclear inclusions by light and electron microscopy.

Results

Twelve Chinese patients (nine female; median age 70.5 years) were enrolled. Seven patients were being assessed for dementia and five for other neurological indications. In all patients, DWI showed distinctive subcortical high signal with increased average apparent diffusion coefficient (ADC), involving frontal, parietal, and temporal more than occipital lobes; the corpus callosum and external capsule were affected in some patients. On T2-weighted images, cerebral and cerebellar atrophy and white matter hyperintensity of Fazekas grade 2 and above were seen in all patients. Three patients underwent skin biopsy; all were positive for intranuclear hyaline inclusion bodies on either p62 staining or electron microscopy, which are pathognomonic for NIID.

Conclusion

Previously undiagnosed patients with NIID can be identified by searching for abnormalities at the junction between gray and white matter on DWI in PACS and subsequently confirmed by skin biopsy. Radiologists should recognize the distinctive neuroimaging pattern of this dementing disease.

Keywords

Neuronal intranuclear inclusion disease (NIID) Dementia MRI DWI Picture archive and communication systems (PACS) 

Notes

Acknowledgments

The authors wish to thank Junko Takahashi-Fujikasaki for invaluable help with slide preparation and Qianhui Cheng for administrative support.

Funding

This work was supported by the NNI Health Research Endowment Fund, which has no involvement in study design, data collection/analysis/interpretation, report writing, or publication decisions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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

Informed consent

This study was approved by the Institutional Review Board. Informed consent was obtained from all participants or their legal representatives.

References

  1. 1.
    Takahashi-Fujigasaki J (2003) Neuronal intranuclear hyaline inclusion disease. Neuropathology. 23:351–359CrossRefGoogle Scholar
  2. 2.
    Woulfe JM (2007) Abnormalities of the nucleus and nuclear inclusions in neurodegenerative disease: a work in progress. Neuropathol Appl Neurobiol 33:2–42.  https://doi.org/10.1111/j.1365-2990.2006.00819.x Google Scholar
  3. 3.
    Takahashi-Fujigasaki J (2015) Neuronal intranuclear hyaline inclusion disease (NIHID): an update. Brain and Nerve 67:199–204Google Scholar
  4. 4.
    Sasaki T, Hideyama T, Saito Y, Shimizu J, Maekawa R, Shiio Y (2015) Neuronal intranuclear inclusion disease presenting with recurrent cerebral infarct-like lesions. Neurol Clin Neurosci 3:185–187.  https://doi.org/10.1111/ncn3.178 CrossRefGoogle Scholar
  5. 5.
    Toyota T, Huang Z, Nohara S, Okada K, Kakeda S, Korogi Y, Nakayama T, Sone J, Sobue G, Adachi H (2015) Neuronal intranuclear inclusion disease manifesting with new-onset epilepsy in the elderly. Neurol Clin Neurosci 3:238–240.  https://doi.org/10.1111/ncn3.12016 CrossRefGoogle Scholar
  6. 6.
    Sone J, Mori K, Inagaki T, Katsumata R, Takagi S, Yokoi S, Araki K, Kato T, Nakamura T, Koike H, Takashima H, Hashiguchi A, Kohno Y, Kurashige T, Kuriyama M, Takiyama Y, Tsuchiya M, Kitagawa N, Kawamoto M, Yoshimura H, Suto Y, Nakayasu H, Uehara N, Sugiyama H, Takahashi M, Kokubun N, Konno T, Katsuno M, Tanaka F, Iwasaki Y, Yoshida M, Sobue G (2016) Clinicopathological features of adult-onset neuronal intranuclear inclusion disease. Brain 139:3170–3186.  https://doi.org/10.1093/brain/aww249 CrossRefPubMedCentralGoogle Scholar
  7. 7.
    Araki K, Sone J, Fujioka Y, Masuda M, Ohdake R, Tanaka Y, Nakamura T, Watanabe H, Sobue G (2016) Memory loss and frontal cognitive dysfunction in a patient with adult-onset neuronal Intranuclear inclusion disease. Intern Med 55:2281–2284.  https://doi.org/10.2169/internalmedicine.55.5544 CrossRefGoogle Scholar
  8. 8.
    Yokoi S, Yasui K, Hasegawa Y, Niwa K, Noguchi Y, Tsuzuki T, Mimuro M, Sone J, Watanabe H, Katsuno M, Yoshida M, Sobue G (2016) Pathological background of subcortical hyperintensities on diffusion-weighted images in a case of neuronal intranuclear inclusion disease. Clin Neuropathol 35:375–380.  https://doi.org/10.5414/NP300961 CrossRefGoogle Scholar
  9. 9.
    Yoshimoto T, Takamatsu K, Kurashige T et al (2017) Adult-onset neuronal intranuclear inclusion disease in two female siblings. Brain Nerve 69:267–274.  https://doi.org/10.11477/mf.1416200737 Google Scholar
  10. 10.
    Aiba Y, Sakakibara R, Abe F, Higuchi T, Tokuyama W, Hiruta N, Tateno F, Tsuyusaki Y, Kishi M, Tateno H, Ogata T (2016) Neuronal intranuclear inclusion disease with leukoencephalopathy and light motor-sensory and autonomic neuropathy diagnosed by skin biopsy. J Neurol Sci 368:263–265.  https://doi.org/10.1016/j.jns.2016.07.042 CrossRefGoogle Scholar
  11. 11.
    Takahashi-Fujigasaki J, Nakano Y, Uchino A, Murayama S (2016) Adult-onset neuronal intranuclear hyaline inclusion disease is not rare in older adults. Geriatr Gerontol Int 16:51–56.  https://doi.org/10.1111/ggi.12725 CrossRefGoogle Scholar
  12. 12.
    Morimoto S, Hatsuta H, Komiya T, Kanemaru K, Tokumaru AM, Murayama S (2017) Simultaneous skin-nerve-muscle biopsy and abnormal mitochondrial inclusions in intranuclear hyaline inclusion body disease. J Neurol Sci 372:447–449.  https://doi.org/10.1016/j.jns.2016.10.042 CrossRefGoogle Scholar
  13. 13.
    Yokoi S, Yasui K (2011) An autopsy case of intranuclear inclusion body disease with leukoencephalopathy. Japanese Soc Neuropathol - Abstr 52nd Annu Meet 31:333Google Scholar
  14. 14.
    Sugiyama A, Sato N, Kimura Y, Maekawa T, Enokizono M, Saito Y, Takahashi Y, Matsuda H, Kuwabara S (2017) MR imaging features of the cerebellum in adult-onset neuronal intranuclear inclusion disease: 8 cases. AJNR Am J Neuroradiol 38:2100–2104.  https://doi.org/10.3174/ajnr.A5336 CrossRefGoogle Scholar
  15. 15.
    Abe K, Fujita M (2017) Over 10 years MRI observation of a patient with neuronal intranuclear inclusion disease. BMJ Case Rep 2017.  https://doi.org/10.1136/bcr-2016-218790
  16. 16.
    Matsuda H, Asada T, Midori Tokumaru A (2017) Neuroimaging diagnosis for Alzheimer’s disease and other dementias. Springer Japan, TokyoCrossRefGoogle Scholar
  17. 17.
    Sone J, Tanaka F, Koike H, Inukai A, Katsuno M, Yoshida M, Watanabe H, Sobue G (2011) Skin biopsy is useful for the antemortem diagnosis of neuronal intranuclear inclusion disease. Neurology 76:1372–1376.  https://doi.org/10.1212/WNL.0b013e3182166e13 CrossRefGoogle Scholar
  18. 18.
    Sone J, Kitagawa N, Sugawara E, Iguchi M, Nakamura R, Koike H, Iwasaki Y, Yoshida M, Takahashi T, Chiba S, Katsuno M, Tanaka F, Sobue G (2013) Neuronal intranuclear inclusion disease cases with leukoencephalopathy diagnosed via skin biopsy. J Neurol Neurosurg Psychiatry 85:3–6.  https://doi.org/10.1136/jnnp-2013-306084 Google Scholar
  19. 19.
    Kohno Y, Ishii A, Terada M, Kobayahi M, Hiroki M, Tamaoka A NH (2013) A case of neuronal intranuclear hyaline inclusion disease presenting polyneuropathy, episodic vomitting, neurogenic bladder dysfunction and leukoencephalopathy. In: The Japanese Society of Neuropathology. p 369Google Scholar
  20. 20.
    Osaki Y, Shimatani Y, Fujita K, Murakami N, Sato K, Terasawa Y, Izumi Y, Kaji R, Sumikura H MS (2013) A case of neuronal intranuclear hyaline inclusion disease suggested by diffusion-weighted MRI and confirmed by skin biopsy. In: The Japanese Society of Neuropathology. p 370Google Scholar
  21. 21.
    Sone J, Kitagawa N, Iwasaki Y, Yoshida M, Tanaka F SG (2013) Diagnosis of neuronal intranuclear inclusion disease with skin biopsy. In: The Japanese Society of Neuropathology. p 370Google Scholar
  22. 22.
    Tokumaru A, Sakurai K, Imabayashi E, Hasegawa S, Murayama S TM (2013) MRI findings of neuronal intranuclear hyaline inclusion disease (NIHID)- histopathologic correlation. In: The Japanese Society of Neuropathology. p 368Google Scholar
  23. 23.
    Kitagawa N, Sone J, Sobue G, Kuroda M, Sakurai M (2014) Neuronal intranuclear inclusion disease presenting with resting tremor. Case Rep Neurol 6:176–180.  https://doi.org/10.1159/000363687 CrossRefPubMedCentralGoogle Scholar
  24. 24.
    Tokumaru AM (2011) Usefulness of MRI for diagnosing dementia. Nihon Rinsho 69(Suppl 8):494–508Google Scholar
  25. 25.
    Martin-Macintosh EL, Broski SM, Johnson GB, Hunt CH, Cullen EL, Peller PJ (2016) Multimodality imaging of neurodegenerative processes: part 1, the basics and common dementias. AJR Am J Roentgenol 207:871–882.  https://doi.org/10.2214/AJR.14.12842 CrossRefGoogle Scholar
  26. 26.
    Patro SN, Glikstein R, Hanagandi P, Chakraborty S (2015) Role of neuroimaging in multidisciplinary approach towards non-Alzheimer’s dementia. Insights Imaging 6:531–544.  https://doi.org/10.1007/s13244-015-0421-1 CrossRefPubMedCentralGoogle Scholar
  27. 27.
    Kansagra AP, Yu JPJ, Chatterjee AR, Lenchik L, Chow DS, Prater AB, Yeh J, Doshi AM, Hawkins CM, Heilbrun ME, Smith SE, Oselkin M, Gupta P, Ali S (2016) Big data and the future of radiology informatics. Acad Radiol 23:30–42.  https://doi.org/10.1016/j.acra.2015.10.004 CrossRefGoogle Scholar
  28. 28.
    Yang GL, Tan YF, Loh SC, Lim TCC (2007) Neuroradiology imaging database: using picture archive and communication systems for brain tumour research. Singap Med J 48:342–346Google Scholar
  29. 29.
    Morris JC (1997) Clinical dementia rating: a reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. Int Psychogeriatrics 9:173–176.  https://doi.org/10.1017/s1041610297004870 CrossRefGoogle Scholar
  30. 30.
    Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA (1987) MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol 149:351–356.  https://doi.org/10.2214/ajr.149.2.351 CrossRefGoogle Scholar
  31. 31.
    Maddalena A, Richards CS, McGinniss MJ et al (2001) Technical standards and guidelines for fragile X: the first of a series of disease-specific supplements to the Standards and Guidelines for Clinical Genetics Laboratories of the American College of Medical Genetics. Quality Assurance Subcommittee of the L. Genet Med 3:200–205.  https://doi.org/10.1097/00125817-200105000-00010 CrossRefPubMedCentralGoogle Scholar
  32. 32.
    Yadav N, Raja P, Shetty SS, Jitender S, Prasad C, Kamble NL, Mahadevan A, M N (2019) Neuronal intranuclear inclusion disease: a rare etiology for rapidly progressive dementia. Alzheimer Dis Assoc Disord 00:1–3.  https://doi.org/10.1097/WAD.0000000000000312 CrossRefGoogle Scholar
  33. 33.
    Suthiphosuwan S, Sasikumar S, Munoz DG (2019) MRI diagnosis of neuronal intranuclear inclusion disease leukoencephalopathy. Neurol Clin Pract.  https://doi.org/10.1212/CPJ.0000000000000664
  34. 34.
    Liu Y, Lu J, Li K, Zhao H, Feng Y, Zhang Z, Hu L, Li G, Shao Y, Wang Y (2019) A multimodal imaging features of the brain in adult-onset neuronal intranuclear inclusion disease. Neurol Sci 40:1495–1497.  https://doi.org/10.1007/s10072-019-03742-5 CrossRefGoogle Scholar
  35. 35.
    Chen L, Wu L, Li S, Huang Q, Xiong J, Hong D, Zeng X (2018) A long time radiological follow-up of neuronal intranuclear inclusion disease: two case reports. Medicine (Baltimore) 97:e13544.  https://doi.org/10.1097/MD.0000000000013544 CrossRefGoogle Scholar
  36. 36.
    Cupidi C, Dijkstra AA, Melhem S, Vernooij MW, Severijnen LA, Hukema RK, Rozemuller AJM, Neumann M, van Swieten JC, Seelaar H (2019) Refining the spectrum of neuronal intranuclear inclusion disease: a case report. J Neuropathol Exp Neurol 78:665–670.  https://doi.org/10.1093/jnen/nlz043 CrossRefGoogle Scholar
  37. 37.
    Valdés Hernández MDC, Chappell FM, Muñoz Maniega S, Dickie DA, Royle NA, Morris Z, Anblagan D, Sakka E, Armitage PA, Bastin ME, Deary IJ, Wardlaw JM (2017) Metric to quantify white matter damage on brain magnetic resonance images. Neuroradiology 59:951–962.  https://doi.org/10.1007/s00234-017-1892-1 CrossRefPubMedCentralGoogle Scholar
  38. 38.
    Patay Z (2005) Diffusion-weighted MR imaging in leukodystrophies. Eur Radiol 15:2284–2303.  https://doi.org/10.1007/s00330-005-2846-2 CrossRefGoogle Scholar
  39. 39.
    Sacher M, Fatterpekar GM, Edelstein S, Sansaricq C, Naidich TP (2005) MRI findings in an atypical case of Kearns-Sayre syndrome: a case report. Neuroradiology 47:241–244.  https://doi.org/10.1007/s00234-004-1314-z CrossRefGoogle Scholar
  40. 40.
    Yang E, Prabhu SP (2014) Imaging manifestations of the leukodystrophies, inherited disorders of white matter. Radiol Clin N Am 52:279–319.  https://doi.org/10.1016/j.rcl.2013.11.008 CrossRefGoogle Scholar
  41. 41.
    Sedel F, Tourbah A, Fontaine B, Lubetzki C, Baumann N, Saudubray JM, Lyon-Caen O (2008) Leukoencephalopathies associated with inborn errors of metabolism in adults. J Inherit Metab Dis 31:295–307.  https://doi.org/10.1007/s10545-008-0778-0 CrossRefGoogle Scholar
  42. 42.
    van der Lei HDW, Steenweg ME, Bugiani M, Pouwels PJW, Vent IM, Barkhof F, van Wieringen WN, van der Knaap MS (2012) Restricted diffusion in vanishing white matter. JAMA Neurol 69:723–727.  https://doi.org/10.1001/archneurol.2011.1658 Google Scholar
  43. 43.
    Gelpi E, Botta-Orfila T, Bodi L, Marti S, Kovacs G, Grau-Rivera O, Lozano M, Sánchez-Valle R, Muñoz E, Valldeoriola F, Pagonabarraga J, Tartaglia GG, Milà M (2017) Neuronal intranuclear (hyaline) inclusion disease and fragile X-associated tremor/ataxia syndrome: a morphological and molecular dilemma. Brain 140:e51.  https://doi.org/10.1093/brain/awx156 CrossRefGoogle Scholar
  44. 44.
    Sone J, Nakamura T, Koike H, Katsuno M, Tanaka F, Iwasaki Y, Yoshida M, Sobue G (2017) Reply: neuronal intranuclear (hyaline) inclusion disease and fragile X-associated tremor/ataxia syndrome: a morphological and molecular dilemma. Brain 140:e52.  https://doi.org/10.1093/brain/awx158 CrossRefGoogle Scholar
  45. 45.
    Padilha IG, Nunes RH, Scortegagna FA, Pedroso JL, Marussi VH, Rodrigues Gonçalves MR, Barsottini OGP, da Rocha AJ (2018) MR imaging features of adult-onset neuronal intranuclear inclusion disease may be indistinguishable from fragile X-associated tremor/ataxia syndrome. AJNR Am J Neuroradiol 39:E100–E101.  https://doi.org/10.3174/ajnr.A5729 CrossRefGoogle Scholar
  46. 46.
    Sugiyama A, Sato N (2018) Reply. AJNR Am J Neuroradiol 39:E102.  https://doi.org/10.3174/ajnr.A5757 CrossRefGoogle Scholar
  47. 47.
    Leehey MA (2009) Fragile X-associated tremor/ataxia syndrome: clinical phenotype, diagnosis, and treatment. J Investig Med 57:830–836.  https://doi.org/10.2310/JIM.0b013e3181af59c4 CrossRefPubMedCentralGoogle Scholar
  48. 48.
    Brown SSG, Stanfield AC (2015) Fragile X premutation carriers: a systematic review of neuroimaging findings. J Neurol Sci 352:19–28.  https://doi.org/10.1016/j.jns.2015.03.031 CrossRefGoogle Scholar
  49. 49.
    Brunberg JA, Jacquemont S, Hagerman RJ, Berry-Kravis EM, Grigsby J, Leehey MA, Tassone F, Brown WT, Greco CM, Hagerman PJ (2002) Fragile X premutation carriers: characteristic MR imaging findings of adult male patients with progressive cerebellar and cognitive dysfunction. AJNR Am J Neuroradiol 23:1757–1766Google Scholar
  50. 50.
    Dmytriw AA, Sawlani V, Shankar J (2017) Diffusion-weighted imaging of the brain: beyond stroke. Can Assoc Radiol J 68:131–146.  https://doi.org/10.1016/j.carj.2016.10.001 CrossRefGoogle Scholar
  51. 51.
    Demaerel P, Heiner L, Robberecht W, Sciot R, Wilms G (1999) Diffusion-weighted MRI in sporadic Creutzfeldt-Jakob disease. Neurology 52:205 LP–205205.  https://doi.org/10.1212/WNL.52.1.205 CrossRefGoogle Scholar
  52. 52.
    Kandiah N, Tan K, Pan a BS et al (2008) Creutzfeldt-Jakob disease: which diffusion-weighted imaging abnormality is associated with periodic EEG complexes? J Neurol 255:1411–1414.  https://doi.org/10.1007/s00415-008-0934-3 CrossRefGoogle Scholar
  53. 53.
    Zerr I, Kallenberg K, Summers DM, Romero C, Taratuto A, Heinemann U, Breithaupt M, Varges D, Meissner B, Ladogana A, Schuur M, Haik S, Collins SJ, Jansen GH, Stokin GB, Pimentel J, Hewer E, Collie D, Smith P, Roberts H, Brandel JP, van Duijn C, Pocchiari M, Begue C, Cras P, Will RG, Sanchez-Juan P (2009) Updated clinical diagnostic criteria for sporadic Creutzfeldt-Jakob disease. Brain 132:2659–2668.  https://doi.org/10.1093/brain/awp191 CrossRefPubMedCentralGoogle Scholar
  54. 54.
    Chandrasekhar V, Lin J, Morère O, et al (2015) A practical guide to CNNs and Fisher vectors for image instance retrievalGoogle Scholar
  55. 55.
    Faria AV, Oishi K, Yoshida S, Hillis A, Miller MI, Mori S (2015) Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis. NeuroImage Clin 7:367–376.  https://doi.org/10.1016/j.nicl.2015.01.008 CrossRefPubMedCentralGoogle Scholar
  56. 56.
    Dinh TA, Silander T, Su B, Gong T, Pang BC, Lim CC, Lee CK, Tan CL, Leong TY (2013) Unsupervised medical image classification by combining case-based classifiers. Stud Health Technol Inform 192:739–743Google Scholar
  57. 57.
    Cheng LTE, Zheng J, Savova GK, Erickson BJ (2010) Discerning tumor status from unstructured MRI reports-completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging 23:119–132.  https://doi.org/10.1007/s10278-009-9215-7 CrossRefGoogle Scholar
  58. 58.
    Shrot S, Salhov M, Dvorski N, Konen E, Averbuch A, Hoffmann C (2019) Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using machine learning scheme. Neuroradiology 61:757–765.  https://doi.org/10.1007/s00234-019-02195-z CrossRefGoogle Scholar
  59. 59.
    Zeynalova A, Kocak B, Durmaz ES, Comunoglu N, Ozcan K, Ozcan G, Turk O, Tanriover N, Kocer N, Kizilkilic O, Islak C (2019) Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI. Neuroradiology 61:767–774.  https://doi.org/10.1007/s00234-019-02211-2 CrossRefGoogle Scholar
  60. 60.
    Ker J, Wang L, Rao J, Lim T (2018) Deep learning applications in medical image analysis. IEEE Access 3536:1.  https://doi.org/10.1109/ACCESS.2017.2788044 Google Scholar
  61. 61.
    Liew CJY, Krishnaswamy P, Cheng LT-E, Tan CH, Poh ACC, Lim TCC (2019) Artificial Intelligence and Radiology in Singapore: championing a new age of augmented imaging for unsurpassed patient care. Ann Acad Med Singapore 48:16–24Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of NeuroradiologyNational Neuroscience InstituteSingaporeRepublic of Singapore
  2. 2.Duke-NUS Graduate Medical SchoolSingaporeSingapore
  3. 3.Department of NeurologyNational Neuroscience InstituteSingaporeSingapore
  4. 4.Department of PathologyTan Tock Seng HospitalSingaporeSingapore
  5. 5.Department of Diagnostic RadiologyTokyo Metropolitan Geriatric CentreTokyoJapan
  6. 6.Department of Neurology & Neuropathology and the Brain Bank for Aging ResearchTokyo Metropolitan Geriatric Centre and Institute of GerontologyTokyoJapan

Personalised recommendations