Neuroimaging in Dementia
Open accessAbstract
Although the diagnosis of dementia still is primarily based on clinical criteria, neuroimaging is playing an increasingly important role. This is in large part due to advances in techniques that can assist with discriminating between different syndromes. Magnetic resonance imaging remains at the core of differential diagnosis, with specific patterns of cortical and subcortical changes having diagnostic significance. Recent developments in molecular PET imaging techniques have opened the door for not only antemortem but early, even preclinical, diagnosis of underlying pathology. This is vital, as treatment trials are underway for pharmacological agents with specific molecular targets, and numerous failed trials suggest that earlier treatment is needed. This article provides an overview of classic neuroimaging findings as well as new and cutting-edge research techniques that assist with clinical diagnosis of a range of dementia syndromes, with an emphasis on studies using pathologically proven cases.
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Positron emission tomography has shortcomings for visualizing Aβ plaques in the brain, including radioligand use, limited spatial resolution, and higher cost.
“Positron emission tomography has been employed to visualize Aβ plaques in the brain for the diagnosis of AD, 3 however it has obvious shortcomings, such as radioligand use, limited spatial resolution and higher cost.4”
From Amyloid Plaque Imaging with a Targeted MRI Contrast Agent in a Transgenic Mouse Model of Alzheimer’s Disease (2022) · cites this paper as [3]
In “Alzheimer’s Disease > Other Imaging Modalities in AD” (page not provided), the paper says amyloid PET detects cortical β-amyloid and identifies high cost and the need for a specialized PET center as limitations. It does not identify radioligand use or limited spatial resolution as shortcomings. (LLM, conf=0.88)
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Santos‐Santos, M., Mandelli, M. L., Binney, R. J., Ogar, J. M., Wilson, S. M., Henry, M. L., Hubbard, H. I., Meese, M., Attygalle, S., Rosenberg, L. M., et al. (2016) · JAMA Neurology · doi:10.1001/jamaneurol.2016.0412 · Cited by 162 · see citing works
Self-citationTowards a nosology for frontotemporal lobar degenerations—A meta-analysis involving 267 subjects
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Pattern of brain atrophy rates in autopsy-confirmed dementia with Lewy bodies
Nedelska, Z., Ferman, T. J., Boeve, B. F., Przybelski, S. A., Lesnick, T. G., Murray, M. E., Gunter, J. L., Senjem, M. L., Vemuri, P., Smith, G. E., et al. (2015) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.07.005 · Cited by 155 · see citing works
Frontotemporal lobar degeneration: Pathogenesis, pathology and pathways to phenotype
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Self-citationSchocke, M. F. H., Seppi, K., Esterhammer, R., Kremser, C., Mair, K. J., Czermak, B. V., Jaschke, W. R., Poewe, W. H., & Wenning, G. K. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.12.005 · Cited by 151 · see citing works
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Bhattacharya, K., Saadia, D., Eisenkraft, B., Yahr, M. D., Olanow, W. C., Drayer, B. P., & Kaufmann, H. (2002) · PubMed · doi:10.1001/archneur.59.5.835 · Cited by 150 · see citing works
Sleep disturbance in spinocerebellar ataxias
Schöls, L. J., Haan, J., Rieß, O., Amoiridis, G., & Przuntek, H. (1998) · Neurology · doi:10.1212/wnl.51.6.1603 · Cited by 150 · see citing works
Suemoto, C. K., Ferretti‐Rebustini, R. E. D. L., Rodriguez, R. D., Leite, R. E. P., Soterio, L., Brucki, S. M. D., Spera, R. R., Cippiciani, T. M., Farfel, J. M., Filho, A. D. P. C., et al. (2017) · PLoS Medicine · doi:10.1371/journal.pmed.1002267 · Cited by 149 · see citing works
Apolipoprotein E ε4 and the pattern of regional brain atrophy in Alzheimer’s disease
Hashimoto, M., Yasuda, M., Tanimukai, S., Matsui, M., Hirono, N., Kazui, H., & Mori, E. (2001) · Neurology · doi:10.1212/wnl.57.8.1461 · Cited by 143 · see citing works
Geschwind, M. D., Haman, A., & Miller, B. L. (2007) · Neurologic Clinics · doi:10.1016/j.ncl.2007.04.001 · Cited by 143 · see citing works
Self-citationClinical spectrum of type IV collagen (COL4A1) mutations: a novel genetic multisystem disease
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Tullberg, M., Hultin, L., EKHOLM, S. E., Månsson, J., Fredman, P., & Wikkelsø, C. (2002) · Acta Neurologica Scandinavica · doi:10.1034/j.1600-0404.2002.01189.x · Cited by 142 · see citing works
The behavioural variant frontotemporal dementia (bvFTD) syndrome in psychiatry
Lanata, S., & Miller, B. L. (2015) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2015-310697 · Cited by 142 · see citing works
Gorno‐Tempini, M. L., Murray, R. C., Rankin, K. P., Weiner, M. W., & Miller, B. L. (2004) · Neurocase · doi:10.1080/13554790490894011 · Cited by 141 · see citing works
‘Idiopathic’ late onset cerebellar ataxia
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White matter hyperintensities predict amyloid increase in Alzheimer's disease
Grimmer, T., Faust, M., Auer, F., Alexopoulos, P., Förstl, H., Henriksen, G., Perneczky, R., Sorg, C., Yousefi, B. H., Drzezga, A. E., et al. (2012) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.01.016 · Cited by 139 · see citing works
COL4A1 mutations in patients with sporadic late‐onset intracerebral hemorrhage
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Characteristic Magnetic Resonance Imaging Findings in Machado-Joseph Disease
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Frontotemporal Lobar Degeneration: Epidemiology, Pathology, Diagnosis and Management
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Common variation in COL4A1/COL4A2 is associated with sporadic cerebral small vessel disease
Rannikmäe, K., Davies, G., Thomson, P. A., Bevan, S., Devan, W. J., Falcone, G. J., Traylor, M., Anderson, C. D., Battey, T. W. K., Radmanesh, F., et al. (2015) · Neurology · doi:10.1212/wnl.0000000000001309 · Cited by 132 · see citing works
Functional brain changes in presymptomatic Huntington's disease
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Snowden, J. S., Hu, Q., Rollinson, S. J., Halliwell, N. A., Robinson, A., Davidson, Y. S., Momeni, P., Baborie, A., Griffiths, T. D., Jaros, E., et al. (2011) · Acta Neuropathologica · doi:10.1007/s00401-011-0816-0 · Cited by 131 · see citing works
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Self-citationThe minimum prevalence of CADASIL in northeast England
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Brain Ventricular Size in Healthy Elderly
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Diagnosis and Management of Behavioral Variant Frontotemporal Dementia
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Frequency and patterns of MRI abnormalities due to status epilepticus
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Diffusion-weighted MRI in herpes simplex encephalitis: a report of three cases
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Regional pattern of white matter microstructural changes in normal aging, MCI, and AD
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Flagged first, then by citations
Research works printed but not linked: 2
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- [297]Worker A Blain C Jarosz J Diffusion tensor imaging of Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy: a tract-based spatial statistics study PLoS One 2014 9 11 e112638 10.1371/journal.pone.0112638 25405990 PMC4236070 · DOI
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Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease
Broce, I. J., Tan, C. H., Fan, C. C., Jansen, I., Savage, J. E., Witoelar, A., Wen, N., Hess, C. P., Dillon, W. P., Glastonbury, C. M., et al. (2019) · Acta Neuropathologica · doi:10.1007/s00401-018-1928-6 · Cited by 140 · see citing works
Self-citationLongitudinal multimodal imaging and clinical endpoints for frontotemporal dementia clinical trials
Staffaroni, A. M., Ljubenkov, P. A., Kornak, J., Cobigo, Y., Datta, S., Marx, G., Walters, S. M., Chiang, K., Olney, N., Elahi, F. M., et al. (2019) · Brain · doi:10.1093/brain/awy319 · Cited by 105 · see citing works
Self-citationXiong, Y., Qu, Y., Min, Z., Wu, J., Zhang, S., & Xue, Z. (2022) · International Journal of Nanomedicine · doi:10.2147/ijn.s351830 · Cited by 4 · see citing works
- Authors
- Staffaroni, A., Elahi, F., McDermott, D., Marton, K., Karageorgiou, E., Sacco, S., Paoletti, M., Caverzasi, E., Hess, C., Rosen, H., & Geschwind, M.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Geschwind, Michael
- ORCID
- Venue
- Seminars in Neurology
- Publisher
- Georg Thieme Verlag KG
- Published
- 2017-10
- Volume
- 37
- Issue
- 05
- Pages
- 510-537
- ISSN
- 0271-8235 (ISSN-L)
- 1098-9021
- PMC
- PMC5823524
- OpenAlex
- W4241885035
- Cited by
- 91
- References
- 377
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 9 · 2023: 21 · 2022: 17 · 2021: 8 · 2020: 9 · 2019: 5 · 2018: 9
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Seminars in Neurology (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute on Aging (R01AG032306, K24AG045333, T32AG023481)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- dementia, neuroimaging, differential diagnosis, magnetic resonance imaging, preclinical diagnosis, dementia syndromes, treatment trials
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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