Brain hypometabolism in rare genetic neurodegenerative disease: Niemann-Pick disease type C, spinocerebellar ataxia and Huntington disease assessed by FDG PET

Document Type : Case Series

Authors

1 Department of Nuclear Medicine, the Royal Melbourne Hospital, Melbourne, Australia

2 Neuropsychiatry, the Royal Melbourne Hospital, Melbourne, Australia

3 Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Australia

4 Florey Institute of Neuroscience and Mental Health, Melbourne, Australia

Abstract

Brain metabolic imaging using 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) with contemporaneous low-dose CT may be used to assess neurodegenerative diseases. In contrast to oncology whole-body FDG PET, qualitative assessment alone in brain FDG PET is subjective and vulnerable to visual interference due to high physiologic background activity. Therefore, mild changes in brain metabolism may be visually undetectable by qualitative interpretation alone, resulting in diagnostic inaccuracy. To overcome this, some institutions may employ an objective comparison to a normal reference database. To date, there is limited literature describing brain metabolic changes in rare genetic neurodegenerative diseases such as Niemann-Pick disease Type C, spinocerebellar ataxia and Huntington disease. In this case series, we illustrate the typical FDG PET findings in the cortex and deep grey matter for these rare diseases, utilising normal database comparison including three dimensional Stereotactic Surface Projection (3D-SSP) mapping. These comparisons can generate 3D-SSP maps where metabolic changes may be expressed in standard deviations from normal (z-score) and visually depicted in a scale of colours to improve diagnostic accuracy.

Keywords

References

1. Kim J, Cho SG, Song M, Kang SR, Kwon SY, Choi KH, et al. Usefulness of 3-dimensional stereotactic surface projection FDG PET images for the diagnosis of dementia. Medicine (Baltimore). 2016; 95:e5622.
2. Patterson MC, Clayton C, Gissen P, Anheim M, Bauer P, Bonnot O, et al. Recommendations for the detection and diagnosis of Niemann-Pick disease type C. Neurol Clin Pract. 2017; 7: 499–511.
3. Geberhiwot T, Moro A, Dardis A, Ramaswami U, Sirrs S, Pineda Marfa M, et al. Consensus clinical management guidelines for Niemann-Pick disease type C. Geberhiwot T, Moro A, Dardis A et al. Orphanet J Rare Dis. 2018;13:50.
4. Walterfang M, Patenaude B, Abel LA, Kluenemann H, Bowman EA, Fahey MC, et al. Subcortical volumetric reductions in adult Niemann-Pick disease type C: a cross-sectional study. AJNR Am J Neuroradiol. 2013; 34:1334-40.
5. Huang JY, Peng SF, Yang CC, Yen KY, Tzen KY, Yen RF. Neuroimaging findings in a brain with Niemann-Pick type C disease. J Formos Med Assoc. 2011; 110:537-42.
6. [Ruano L1, Melo C, Silva MC, Coutinho P. The global epidemiology of hereditary ataxia and spastic paraplegia: a systematic review of prevalence studies. Neuroepidemiology. 2014; 42:174-83.
7. Kim Y, Kondo M, Sunami Y, Kawata A, Komori T, Oyanagi K, et al. MRI Findings in Spinocerebellar Ataxia. J Neurol Disord Stroke. 2014; 2:1072.
8. Klaes A, Reckziegel E, Franca M, Rezende T, Vedolin L, Jardim L et al. MR imaging in Spinocerebellar Ataxias: A Systematic Review. AJNR Am J Neuroradiol. 2016; 37:1405-12.
9. Meira A, Arruda W, Ono S, Neto A, Raskin S, Camargo C, et al. Neuroradiological Findings in the Spinocerebellar Ataxias. Tremor Other Hyperkinet Mov (NY). 2019; 9.
10. Meles S, Kok J, De Jong B, Renken R, de Vries J, Spikman J, et al. The cerebral metabolic topography of spinocerebellar ataxia type 3. Neuroimage Clin. 2018; 19: 90-7.
11. [Pringsheim T, Wiltshire K, Day L, Dykeman J, Steeves T, Jette N. The incidence and prevalence of Huntington's disease: a systematic review and meta-analysis. Mov Disord. 2012; 27: 1083-91.
12. Niccolini F, Politis M. Neuroimaging in Huntington's disease. World J Radiol. 2014; 6:301–12.
13. Ho V, Chuang H, Rovira M, Koo B. Juvenile Huntington disease: CT and MR features.AJNR Am J Neuroradiol. 1995; 16:1405-12.
Asia Oceania Journal of Nuclear Medicine and Biology
Volume 9, Issue 2
July 2021
Pages 167-172

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