Suspected non-AD pathology in mild cognitive impairment
Open accessAbstract
We aim to better characterize mild cognitive impairment (MCI) patients with suspected non-Alzheimer's disease (AD) pathology (SNAP) based on their longitudinal outcome, cognition, biofluid, and neuroimaging profile. MCI participants (n = 361) from ADNI-GO/2 were designated "amyloid positive" with abnormal amyloid-beta 42 levels (AMY+) and "neurodegeneration positive" (NEU+) with abnormal hippocampal volume or hypometabolism using fluorodeoxyglucose-positron emission tomography. SNAP was compared with the other MCI groups and with AMY- controls. AMY-NEU+/SNAP, 16.6%, were older than the NEU- groups but not AMY- controls. They had a lower conversion rate to AD after 24 months than AMY+NEU+ MCI participants. SNAP-MCI participants had similar amyloid-beta 42 levels, florbetapir and tau levels, but larger white matter hyperintensity volumes than AMY- controls and AMY-NEU- MCI participants. SNAP participants performed worse on all memory domains and on other cognitive domains, than AMY-NEU- participants but less so than AMY+NEU+ participants. Subthreshold levels of cerebral amyloidosis are unlikely to play a role in SNAP-MCI, but pathologies involving the hippocampus and cerebrovascular disease may underlie the neurodegeneration and cognitive impairment in this group.
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The rate of cognitive decline is significantly greater for cognitively impaired and CU individuals who have abnormalities in both an amyloid biomarker and a second biomarker type (CSF T-tau or P-tau, atrophy, or hypometabolism) in comparison to individuals who have neither or only one of these biomarker abnormalities.
“The rate of cognitive decline is significantly greater for cognitively impaired and CU individuals who have abnormalities in both an amyloid biomarker and a second biomarker type (which could be CSF T-tau or P-tau, atrophy, or hypometabolism) in comparison to individuals who have neither or only one of these biomarker abnormalities [30–35,39,40,42–45].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [40]
Results §3.1 reports a higher 12- and 24-month dementia conversion rate for the amyloid-positive, neurodegeneration-positive MCI group than for the amyloid-negative, neurodegeneration-positive group; Discussion §4 says the dual-positive group had the worst outcomes. The paper does not establish the claimed rate of cognitive decline across all comparator groups or in cognitively unimpaired individuals. Its primary neurodegeneration classification uses atrophy or hypometabolism, not CSF tau. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe combination of an abnormal MRI, CSF T-tau, or FDG PET study with an abnormal amyloid biomarker provides much more powerful prediction of future cognitive decline than an abnormal amyloid study alone.
“However, the combination of an abnormal MRI, CSF T-tau, or FDG PET study with an abnormal amyloid biomarker provides much more powerful prediction of future cognitive decline [30–35,39,40,42–45] than an abnormal amyloid study alone.”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [40]
The Methods (§2.5; page unavailable) classify MCI participants by amyloid status and neurodegeneration measured with hippocampal volume or FDG-PET. The Discussion (§4; page unavailable) reports that the amyloid-positive, neurodegeneration-positive group had the worst outcomes, including 42.4% conversion to dementia at 12 months. This supports the direction of the claim, but the paper does not establish that the combination is a much more powerful predictor than amyloid alone, and it does not use CSF total tau in that outcome classification. (LLM, conf=0.82)
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Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,803 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,003 · see citing works
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 2× · see citing works
“These individuals display evidence of tangle pathology after Braak distribution (Braak and Braak, 1991) in the medial temporal lobe in the absence of cerebral amyloid and may have associated mild cognitive deficits.”
BackgroundAdvances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882 · see citing works
VALIDITY OF THE TRAIL MAKING TEST AS AN INDICATOR OF ORGANIC BRAIN DAMAGE
Reitan, R. M. (1958) · Perceptual and Motor Skills · doi:10.2466/pms.1958.8.3.271 · Cited by 7,925 · see citing works
Sheikh (1986) · doi:10.1300/j018v05n01_09 · Cited by 7,236 · see citing works
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155 · see citing works
Nonparametric permutation tests for functional neuroimaging: A primer with examples
Nichols, T. E., & Holmes, A. P. (2002) · Human Brain Mapping · doi:10.1002/hbm.1058 · Cited by 6,256 · see citing works
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,618 · see citing works
SMITH, S., & NICHOLS, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.061 · Cited by 5,250 · see citing works
Jack, C. R., Knopman, D. S., Jagust, W. J., Petersen, R. C., Weiner, M. W., Aisen, P. S., Shaw, L. M., Vemuri, P., Wiste, H. J., Weigand, S. D., et al. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70291-0 · Cited by 4,396 · see citing works
Kaplan, E., Goodglass, H., Weıntraub, S., & Segal, O. (2001) · Medical Entomology and Zoology · Cited by 4,378 · see citing works
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347 · see citing works
A new rating scale for Alzheimer's disease
(1984) · American Journal of Psychiatry · doi:10.1176/ajp.141.11.1356 · Cited by 4,175 · see citing works
L’examen Clinique en Psychologie
Rubé, P. (1959) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.1959.13.4.989 · Cited by 3,299 · see citing works
Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects
Shaw, L. M., Vanderstichele, H., Knapik‐Czajka, M., Clark, C. M., Aisen, P. S., Petersen, R. C., Blennow, K., Soares, H., Simon, A., Lewczuk, P., et al. (2009) · Annals of Neurology · doi:10.1002/ana.21610 · Cited by 2,065 · see citing works
Alzheimer’s Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W. J., Trojanowski, J. Q., Toga, A. W., & Beckett, L. A. (2007) · Springer eBooks · doi:10.1007/978-0-387-72076-0_18 · Cited by 1,493 · see citing works
Primary age-related tauopathy (PART): a common pathology associated with human aging
Crary, J. F., Trojanowski, J. Q., Schneider, J. A., Abisambra, J. F., Abner, E. L., Alafuzoff, I., Arnold, S. E., Attems, J., Beach, T. G., Bigio, E. H., et al. (2014) · Acta Neuropathologica · doi:10.1007/s00401-014-1349-0 · Cited by 1,467 · see citing works
Dementia: The Estimation of Premorbid Intelligence Levels Using the New Adult Reading Test
Nelson, H. E., & O’Connell, A. (1978) · Cortex · doi:10.1016/s0010-9452(78)80049-5 · Cited by 1,120 · see citing works
Clinical Gerontology; A Guide to Assessment and Intervention
Essa, M. (1987) · American Journal of Psychotherapy · doi:10.1176/appi.psychotherapy.1987.41.3.478 · Cited by 1,064 · see citing works
Prins, N. D., van Dijk, E. J., den Heijer, T., Vermeer, S. E., JOLLES, J., Koudstaal, P. J., Albert Hofman, D., & Breteler, M. M. B. (2005) · Brain · doi:10.1093/brain/awh553 · Cited by 732 · see citing works
Amyloid Deposition, Hypometabolism, and Longitudinal Cognitive Decline
Landau, S. M., Mintun, M., Joshi, A. D., Koeppe, R., Petersen, R. C., Aisen, P., Weiner, M., & Jagust, W. J. (2012) · Annals of Neurology · doi:10.1002/ana.23650 · Cited by 723 · see citing works
Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements
Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.05.044 · Cited by 670 · see citing works
Self-citationNeuropathologic Features of Amnestic Mild Cognitive Impairment
Petersen, R. C., Parisi, J. E., Dickson, D. W., Johnson, K. A., Knopman, D. S., Boeve, B. F., Jicha, G. A., Ivnik, R. J., Smith, G. E., Tangalos, E. G., et al. (2006) · Archives of Neurology · doi:10.1001/archneur.63.5.665 · Cited by 616 · see citing works
Jack, C. R., Knopman, D. S., Weigand, S. D., Wiste, H. J., Vemuri, P., Lowe, V., Kantarci, K., Gunter, J. L., Senjem, M. L., Ivnik, R. J., et al. (2012) · Annals of Neurology · doi:10.1002/ana.22628 · Cited by 602 · see citing works
Episodic and Semantic Memory: A Comparison of Amnesic and Demented Patients
Butters, N., Granholm, E. L., Salmon, D. P., Grant, I., & Wolfe, J. (1987) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/01688638708410764 · Cited by 596 · see citing works
Clinical Gerontology: A Guide to Assessment and Intervention
Brink, T. L. (1986) · Medical Entomology and Zoology · Cited by 592 · see citing works
Preclinical Alzheimer's disease and its outcome: a longitudinal cohort study
Vos, S. J., Xiong, C., Visser, P. J., Jasielec, M. S., Hassenstab, J., Grant, E. A., Cairns, N. J., Morris, J. C., Holtzman, D. M., & Fagan, A. M. (2013) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70194-7 · Cited by 564 · see citing works
Development and Validation of a Model for Estimating Premorbid Verbal Intelligence in the Elderly
Grober, E. H., Sliwinsk, M., & Korey, S. R. (1991) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/01688639108405109 · Cited by 562 · see citing works
An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data
Avants, B., Tustison, N. J., Wu, J., Cook, P. A., & Gee, J. C. (2011) · Neuroinformatics · doi:10.1007/s12021-011-9109-y · Cited by 559 · see citing works
Clinical core of the Alzheimer's disease neuroimaging initiative: Progress and plans
Aisen, P. S. S., Petersen, R. C., Donohue, M., Gamst, A. C., Raman, R., Thomas, R. G., Walter, S., Trojanowski, J. Q., Shaw, L. M. J., Beckett, L. A., et al. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.006 · Cited by 516 · see citing works
Saykin, A. J., Shen, L., Foroud, T. M., Potkin, S. G., Swaminathan, S., Kim, S., Risacher, S. L., Nho, K., Huentelman, M. J., Craig, D. W., et al. (2010) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2010.03.013 · Cited by 442 · see citing works
Au, R., Massaro, J. M., Wolf, P. A., Young, M. E., Beiser, A., Seshadri, S., D’Agostino, R. B., & DeCarli, C. (2006) · Archives of Neurology · doi:10.1001/archneur.63.2.246 · Cited by 381 · see citing works
Mormino, E. C., Betensky, R. A., Hedden, T., Schultz, A. P., Amariglio, R. E., Rentz, D. M., Johnson, K. A., & Sperling, R. A. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2014.2031 · Cited by 328 · see citing works
Mild cognitive impairment due to Alzheimer disease in the community
Petersen, R. C., Aisen, P., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Knopman, D. S., Mielke, M., Pankratz, V. S., Roberts, R., Rocca, W. A., et al. (2013) · Annals of Neurology · doi:10.1002/ana.23931 · Cited by 265 · see citing works
Carmichael, O. (2010) · Archives of Neurology · doi:10.1001/archneurol.2010.284 · Cited by 248 · see citing works
Registration based cortical thickness measurement
Das, S. R., Avants, B., Grossman, M., & Gee, J. C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.016 · Cited by 246 · see citing works
Self-citationShort-term clinical outcomes for stages of NIA-AA preclinical Alzheimer disease
Knopman, D. S., Jack, C. R., Wiste, H. J., Weigand, S. D., Vemuri, P., Lowe, V. J., Kantarci, K., Gunter, J. L., Senjem, M. L., Ivnik, R. J., et al. (2012) · Neurology · doi:10.1212/wnl.0b013e3182563bbe · Cited by 243 · see citing works
Association between blood pressure, white matter lesions, and atrophy of the medial temporal lobe
den Heijer, T., Launer, L. J., Prins, N. D., van Dijk, E. J., Vermeer, S. E., Albert Hofman, D., Koudstaal, P. J., & Breteler, M. M. B. (2005) · Neurology · doi:10.1212/01.wnl.0000149641.55751.2e · Cited by 237 · see citing works
Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity
Jack, C. R., Wiste, H. J., Weigand, S. D., Knopman, D. S., Lowe, V. J., Vemuri, P., Mielke, M. M., Jones, D. T., Senjem, M. L., Gunter, J. L., et al. (2013) · Neurology · doi:10.1212/01.wnl.0000435556.21319.e4 · Cited by 200 · see citing works
Relationship Between White Matter Hyperintensities, Cortical Thickness, and Cognition
Tuladhar, A. M., Reid, A. T., Shumskaya, E., de Laat, K. F., van Norden, A. G. W., van Dijk, E. J., Norris, D. G., & de Leeuw, F.-E. (2015) · Stroke · doi:10.1161/strokeaha.114.007146 · Cited by 195 · see citing works
Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease
Prestia, A., Caroli, A., van der Flier, W. M., Ossenkoppele, R., Van Berckel, B., Barkhof, F., Teunissen, C. E., Wall, A. E., Carter, S. F., Schöll, M., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e3182872830 · Cited by 184 · see citing works
Brain injury biomarkers are not dependent on β‐amyloid in normal elderly
Knopman, D. S., Jack, C. R., Wiste, H. J., Weigand, S. D., Vemuri, P., Lowe, V. J., Kantarci, K., Gunter, J. L., Senjem, M. L., Mielke, M. M., et al. (2013) · Annals of Neurology · doi:10.1002/ana.23816 · Cited by 183 · see citing works
Blood pressure levels and brain volume reduction
Beauchet, O., Celle, S., Roche, F., Bartha, R., Montero‐Odasso, M., Allali, G., & Annweiler, C. (2013) · Journal of Hypertension · doi:10.1097/hjh.0b013e32836184b5 · Cited by 182 · see citing works
Wirth, M., Villeneuve, S., Haase, C. M., Madison, C. M., Oh, H., Landau, S. M., Rabinovici, G. D., & Jagust, W. J. (2013) · JAMA Neurology · doi:10.1001/jamaneurol.2013.4013 · Cited by 171 · see citing works
Mild cognitive impairment with suspected nonamyloid pathology (SNAP): Prediction of progression
Caroli, A., Prestia, A., Galluzzi, S., Ferrari, C., van der Flier, W. M., Ossenkoppele, R., Van Berckel, B., Barkhof, F., Teunissen, C., Wall, A. E., et al. (2015) · Neurology · doi:10.1212/wnl.0000000000001209 · Cited by 142 · see citing works
Self-citationAntemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND
Vemuri, P., Simon, G., Kantarci, K., Whitwell, J. L., Senjem, M. L., Przybelski, S. A., Gunter, J. L., Josephs, K. A., Knopman, D. S., Boeve, B. F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.073 · Cited by 108 · see citing works
Schwarz, C. G., Fletcher, E., DeCarli, C. S., & Carmichael, O. T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02498-6_20 · Cited by 100 · see citing works
Tau Positron Emission Tomography (PET) Imaging: Past, Present, and Future.
Ariza, M., Kolb, H., Moechars, D., Rombouts, F., & Andrés, J. (2015) · Journal of Medicinal Chemistry · doi:10.1021/jm5017544 · Cited by 97 · see citing works
Chowdhury, M. H., Nagai, A., Bokura, H., Nakamura, E., Kobayashi, S., & Yamaguchi, S. (2010) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2009.12.010 · Cited by 91 · see citing works
Jack, C. R. (2014) · Acta Neuropathologica · doi:10.1007/s00401-014-1362-3 · Cited by 88 · see citing works
Duara, R., Loewenstein, D., Shen, Q., Barker, W., Potter, E., Varón, D., Heurlin, K., Vandenberghe, R. R., & Buckley, C. J. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2012.01.006 · Cited by 60 · see citing works
Anterior and posterior MTL networks in aging and MCI
Das, S. R., Pluta, J. B., Mancuso, L., Kliot, D., Yushkevich, P. A., & Wolk, D. A. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.03.041 · Cited by 59 · see citing works
Self-citationCortical signatures of cognition and their relationship to Alzheimer’s disease
Initiative, A. D. N., Gross, A. L., Manly, J. J., Pa, J., Johnson, J. K., Park, L. Q., Mitchell, M. B., Melrose, R. J., Inouye, S. K., & McLaren, D. G. (2012) · Brain Imaging and Behavior · doi:10.1007/s11682-012-9180-5 · Cited by 26 · see citing works
Das, S. R., Mancuso, L., Olson, I. R., Arnold, S. E., & Wolk, D. A. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv022 · Cited by 22 · see citing works
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Flagged first, then by citations
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Sorted by citations
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537 · see citing works
“This latter biomarker profile implies evidence of one or more neuropathologic processes other than AD [35,40,107] and has been labeled “suspected nonAlzheimer’s pathophysiology” (or SNAP) [38].”
Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report
Nelson, P. T., Dickson, D. W., Trojanowski, J. Q., Jack, C. R., Boyle, P. A., Arfanakis, K., Rademakers, R., Alafuzoff, I., Attems, J., Brayne, C., et al. (2019) · Brain · doi:10.1093/brain/awz099 · Cited by 1,547 · see citing works
Defining imaging biomarker cut points for brain aging and Alzheimer's disease
Jack, C. R., Wiste, H. J., Weigand, S. D., Therneau, T. M., Lowe, V. J., Knopman, D. S., Gunter, J. L., Senjem, M. L., Jones, D. T., Kantarci, K., et al. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.08.005 · Cited by 931 · see citing works
Jack, C. R., Wiste, H. J., Weigand, S. D., Therneau, T. M., Knopman, D. S., Lowe, V., Vemuri, P., Mielke, M. M., Roberts, R. O., Machulda, M. M., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30077-7 · Cited by 333 · see citing works
Jack, C. R., Wiste, H. J., Botha, H., Weigand, S. D., Therneau, T. M., Knopman, D. S., Graff-Radford, J., Jones, D. T., Ferman, T. J., Boeve, B. F., et al. (2019) · Brain · doi:10.1093/brain/awz268 · Cited by 200 · see citing works
Predicting future rates of tau accumulation on PET
Jack, C. R., Wiste, H. J., Weigand, S. D., Therneau, T. M., Lowe, V. J., Knopman, D. S., Botha, H., Graff-Radford, J., Jones, D. T., Ferman, T. J., et al. (2020) · Brain · doi:10.1093/brain/awaa248 · Cited by 117 · see citing works
- Authors
- Wisse, L. E. M., Butala, N., Das, S. R., Davatzikos, C., Dickerson, B. C., Vaishnavi, S. N., Yushkevich, P. A., & Wolk, D. A.
- Affiliations
- University of Pennsylvania (US)
- Harvard University (US)
- Corresponding author
- Wisse, Laura E.M.
- ORCID
- Venue
- Neurobiology of Aging
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 36
- Issue
- 12
- Pages
- 3152-3162
- ISSN
- 0197-4580 (ISSN-L)
- PMC
- PMC4641774
- OpenAlex
- W1440144999
- Cited by
- 78
- References
- 59
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 7 · 2023: 6 · 2022: 6 · 2021: 7 · 2020: 4 · 2019: 8 · 2018: 10 · 2017: 16 · 2016: 6
- FWCI
- 8.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neurobiology of Aging (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute on Aging (R01 AG037376, P30 AG010124, R01 AG040271)
- Alzheimer’s Disease Neuroimaging Initiative (ADNI)
- National Institutes of Health (U01 AG024904)
- DOD (W81XWH-12-2–0012)
- National Institute of Biomedical Imaging and Bioengineering
- Alzheimer's Association
- Alzheimer's Drug Discovery Foundation
- Araclon Biotech
- BioClinica, Inc
- Biogen Idec Inc
- Bristol-Myers Squibb Company
- Eisai Inc
- Elan Pharmaceuticals, Inc
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd
- Genentech, Inc
- Fujirebio
- GE Healthcare
- IXICO Ltd
- Janssen Alzheimer Immunotherapy Research & Development, LLC
- Johnson & Johnson Pharmaceutical Research & Development LLC
- Medpace, Inc
- Merck & Co, Inc
- Meso Scale Diagnostics, LLC
- NeuroRx Research
- Neurotrack Technologies
- Novartis Pharmaceuticals Corporation
- Pfizer Inc
- Piramal Imaging
- Servier
- Synarc Inc
- Takeda Pharmaceutical Company
- Canadian Institutes of Health Research
- 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 › Pulmonary and Respiratory Medicine › Cancer-related cognitive impairment studies
- Keywords
- mild cognitive impairment, amyloid-beta 42, neurodegeneration, hippocampal volume, FDG-PET hypometabolism, white matter hyperintensities, conversion to Alzheimer's disease, cognitive impairment, cerebrovascular disease, florbetapir, tau protein
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cognition, Female, Hippocampus, Humans, Male, Memory, Middle Aged, Nerve Degeneration, Peptide Fragments, Amyloid beta-Peptides, Neuroimaging, Cognitive Dysfunction
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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