Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T02:25:42.426Z Has data issue: false hasContentIssue false

Chapter 4 - Imaging

Published online by Cambridge University Press:  21 June 2019

Rob Butler
Affiliation:
Waitemata DHB and North Shore Hospital, Auckland
Cornelius Katona
Affiliation:
Helen Bamber Foundation
Get access

Summary

Exactly 21 years have passed since John Besson’s chapter ‘Imaging’ in the previous edition of these seminars. There has been an amazing proliferation of imaging methods, but very little change in the clinical imaging protocols available to the average UK clinician. X-ray computed tomography (CT) still seems to be the mainstay of assessment in the standard psychiatric memory clinic. Magnetic resonance imaging (MRI) tends to be available, but only as a ‘special treat’, often mediated by neurologists, and emission tomography, such as single photon emission computerised tomography (SPECT) and positron emission tomography (PET), is only used in highly specialised cases outside a few academic centres. Apart from generic NHS austerity, ‘health without mental health’, and institutional ageism, what could be the reasons for this?

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

McKhann, G. M., Knopman, D. S., Chertkow, H., et al. (2011) ‘The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease’. Alzheimers Dement 7(3): 263–9.Google Scholar
Wardlaw, J., for METACOHORTS Consortium (2016) ‘METACOHORTS for the study of vascular disease and its contribution to cognitive decline and neurodegeneration: An initiative of the Joint Programme for Neurodegenerative Disease research’. Alzheimers Dement 12(12): 1235–49.Google Scholar
Suri, S., Valkanova, V., Heise, V., Sexton, C. E., Ebmeier, K. P. (2018) ‘Neuroimaging’. In Oxford Textbook of Old Age Psychiatry, edited by Dening, T., Thomas, A., Stewart, R., Taylor, J.-P.. 3rd ed. Oxford: Oxford University Press.Google Scholar
Ebmeier, K. P., Filippini, N., Mackay, C. E., Suri, S., Valkanova, V. (2016) ‘Functional brain imaging and connectivity in dementia’. In Dementia, edited by Ames, D., Burn, A., O’Brien, J.. 5th ed. London: Taylor & Francis, pp. 107–18.Google Scholar
National Institute for Health and Care Excellence (2018) ‘Dementia: Assessment, management and support for people living with dementia and their carers’. NICE guideline [NG97], June, www.nice.org.uk/guidance/ng97.Google Scholar
Frisoni, G. B., Boccardi, M., Barkhof, F., et al. (2017) ‘Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers’. Lancet Neurol 16(8): 661–76.Google Scholar
Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2009) ‘MRI and CSF biomarkers in normal, MCI, and AD subjects: Diagnostic discrimination and cognitive correlations’. Neurology 73(4): 287–93.Google Scholar
Vemuri, P., Gunter, J. L., Senjem, M. L., et al. (2008) ‘Alzheimer’s disease diagnosis in individual subjects using structural MR images: Validation studies’. Neuroimage 39(3): 1186–97.Google Scholar
Valkanova, V., Ebmeier, K. P. (2014) ‘Neuroimaging in dementia’. Maturitas 79(2): 202–8.Google Scholar
Scheltens, P., Leys, D., Barkhof, F., et al. (1992) ‘Atrophy of medial temporal lobes on MRI in “probable” Alzheimer’s disease and normal ageing: Diagnostic value and neuropsychological correlates’. J Neurol Neurosurg Psychiatry 55(10): 967–72.Google Scholar
Herholz, K. (2011) ‘Perfusion SPECT and FDG-PET’. Int Psychogeriatr 23 Suppl 2: S25–31.CrossRefGoogle ScholarPubMed
Hall, B., Mak, E., Cervenka, S., et al. (2017) ‘In vivo tau PET imaging in dementia: Pathophysiology, radiotracer quantification, and a systematic review of clinical findings’. Ageing Res Rev 36: 5063.Google Scholar
Donaghy, P., Thomas, A. J., O’Brien, J. T. (2015) ‘Amyloid PET Imaging in Lewy body disorders’. Am J Geriatr Psychiatry 23(1): 2337.Google Scholar
Mathis, C. A., Lopresti, B. J., Ikonomovic, M. D., Klun, W. E. (2017) ‘Small-molecule PET Tracers for Imaging Proteinopathies’. Semin Nucl Med 47(5): 553–75.Google Scholar
Jack, C. R. Jr., Wiste, H. J., Weigand, S. D., et al. (2013) ‘Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity’. Neurology 81(20): 1732–40.Google Scholar
Prestia, A., Caroli, A., van der Flier, W. M., et al. (2013) ‘Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease’. Neurology 80(11): 1048–56.Google Scholar
Smailagic, N., Vacante, M., Hyde, C., et al. (2015) ‘(1)(8)F-FDG PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI)’. Cochrane Database Syst Rev 1: Cd010632.Google Scholar
Zhang, S., Smailagic, N., Hyde, C., et al. (2014) ‘(11)C-PIB-PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI)’. Cochrane Database Syst Rev 7: Cd010386.Google Scholar
Wardlaw, J. M., Smith, E. E., Biessels, G. J., et al. (2013) ‘Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration’. Lancet Neurol 12(8): 822–38.Google Scholar
Perneczky, R., Tene, O., Attems, J., et al. (2016) ‘Is the time ripe for new diagnostic criteria of cognitive impairment due to cerebrovascular disease? Consensus report of the International Congress on Vascular Dementia working group’. BMC Med 14(1): 162.Google Scholar
Narayanan, L., Murray, A. D. (2016) ‘What can imaging tell us about cognitive impairment and dementia?World J Radiol 8(3): 240–54.CrossRefGoogle ScholarPubMed
Wahlund, L. O., Barkhof, F., Fazekas, F., et al. (2001) ‘A new rating scale for age-related white matter changes applicable to MRI and CT’. Stroke 32(6): 1318–22.Google Scholar
Suri, S., Topiwala, A., Mackay, C. E., et al. (2014) ‘Using structural and diffusion magnetic resonance imaging to differentiate the dementias’. Curr Neurol Neurosci Rep 14(9): 475.Google Scholar
McKeith, I. G., Boeve, B. F., Dickson, D. W., et al. (2017) ‘Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium’. Neurology 89(1): 88100.CrossRefGoogle ScholarPubMed
Broski, S. M., Hunt, C. H., Johnson, G. B., et al. (2014) ‘Structural and functional imaging in parkinsonian syndromes’. Radiographics 34(5): 1273–92.CrossRefGoogle ScholarPubMed
Rascovsky, K., Hodges, J. R., Knopman, D., et al. (2011) ‘Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia’. Brain 134(Pt 9): 2456–77.CrossRefGoogle ScholarPubMed
Gorno-Tempini, M. L., Hillis, A. E., Weintraub, S., et al. (2011) ‘Classification of primary progressive aphasia and its variants’. Neurology 76(11): 1006–14.Google Scholar
Meeter, L. H., Kaat, L. D., Rohrer, J. D., van Swieten, J. C. (2017) ‘Imaging and fluid biomarkers in frontotemporal dementia’. Nat Rev Neurol 13(7): 406–19.Google Scholar
Alexopoulos, G. S. (2006) ‘The vascular depression hypothesis: 10 years later’. Biol Psychiatry 60(12): 1304–5.Google Scholar
Aizenstein, H. J., Baskys, A., Boldrini, M., et al. (2016) ‘Vascular depression consensus report – A critical update’. BMC Med 14(1): 161.Google Scholar
Wen, M. C., Steffens, D. C., Chen, M. K., Zainal, N. H. (2014) ‘Diffusion tensor imaging studies in late-life depression: Systematic review and meta-analysis’. Int J Geriatr Psychiatry 29(12): 1173–84.Google Scholar
Filippini, N., Zsoldos, E., Haapakoski, R., et al. (2014) ‘Study protocol: The Whitehall II imaging sub-study’. BMC Psychiatry 14: 159.Google Scholar
Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., et al. (2017) ‘Image processing and quality control for the first 10,000 brain imaging datasets from UK Biobank’. Neuroimage 166: 400–24.Google Scholar
Smith, S. M., Beckmann, C. F., Andersson, J., et al. (2013) ‘Resting-state fMRI in the Human Connectome Project’. Neuroimage 80: 144–68.Google Scholar
Walhovd, K. B., Fjell, A. M., Westerhausen, R., et al. (2017) ‘Healthy minds from 0–100 years: Optimising the use of European brain imaging cohorts (“Lifebrain”)’. Eur Psychiatry 47: 7687.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×