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Neuroimaging correlates of pathologically defined subtypes of Alzheimer's disease: a case-control study
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Brain atrophy is a validated marker for identifying the risk of developing AD.
“Brain atrophy is a validated marker for identifying the risk of developing AD [16–19], assisting clinicians in making decisions regarding diagnosis and prognosis [20].”
From Association of APOC1 with cortical atrophy during conversion to Alzheimer’s disease (2025) · cites this paper as [19]
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Cites
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123
No adjustments are needed for multiple comparisons.
Rothman, K. J. (1990) · Epidemiology · doi:10.1097/00001648-199001000-00010 · Cited by 5,965
What's wrong with Bonferroni adjustments
Perneger, T. V. (1998) · BMJ · doi:10.1136/bmj.316.7139.1236 · Cited by 5,900
Murray, M. E., Graff-Radford, N. R., Ross, O. A., Petersen, R. C., Duara, R., & Dickson, D. W. (2011) · The Lancet Neurology · doi:10.1016/s1474-4422(11)70156-9 · Cited by 972
Self-citationFocal cortical presentations of Alzheimer's disease
Alladi, S., Xuereb, J., Bak, T., Nestor, P., Knibb, J., Patterson, K., & Hodges, J. R. (2007) · Brain · doi:10.1093/brain/awm213 · Cited by 581
Alzheimer and frontotemporal pathology in subsets of primary progressive aphasia
Mesulam, M., Wicklund, A., Johnson, N., Rogalski, E., Léger, G. C., Rademaker, A., Weintraub, S., & Bigio, E. H. (2008) · Annals of Neurology · doi:10.1002/ana.21388 · Cited by 513
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Research works printed but not linked: 22
- Whitwell, 2008, MRI correlates of neurofibrillary tangle pathology at autopsy: a voxel-based morphometry study, Neurology, 71, 743, 10.1212/01.wnl.0000324924.91351.7d · DOI · Paper page
- Jack, 2002, Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia, Neurology, 58, 750, 10.1212/WNL.58.5.750 · DOI · Paper page
- Gosche, 2002, Hippocampal volume as an index of Alzheimer neuropathology: findings from the Nun Study, Neurology, 58, 1476, 10.1212/WNL.58.10.1476 · DOI
- Csernansky, 2004, Correlations between antemortem hippocampal volume and postmortem neuropathology in AD subjects, Alzheimer Dis Assoc Disord, 18, 190
- Hyman, 1997, Consensus recommendations for the postmortem diagnosis of Alzheimer disease from the National Institute on Aging and the Reagan Institute Working Group on diagnostic criteria for the neuropathological assessment of Alzheimer disease, J Neuropathol Exp Neurol, 56, 1095, 10.1097/00005072-199710000-00002 · DOI · Paper page
- Petersen, 1990, Mayo Clinic Alzheimer's Disease Patient Registry, Aging (Milano), 2, 408
- Mirra, 1991, The Consortium to Establish a Registry for Alzheimer's Disease (CERAD): part II: standardization of the neuropathologic assessment of Alzheimer's disease, Neurology, 41, 479, 10.1212/WNL.41.4.479 · DOI · Paper page
- Knopman, 2003, Neuropathology of cognitively normal elderly, J Neuropathol Exp Neurol, 62, 1087, 10.1093/jnen/62.11.1087 · DOI · Paper page
- Sled, 1998, A nonparametric method for automatic correction of intensity nonuniformity in MRI data, IEEE Trans Med Imaging, 17, 87, 10.1109/42.668698 · DOI · Paper page
- Ashburner, 2000, Voxel-based morphometry—the methods, Neuroimage, 11, 805, 10.1006/nimg.2000.0582 · DOI · Paper page
- Vemuri, 2008, Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies, Neuroimage, 39, 1186, 10.1016/j.neuroimage.2007.09.073 · DOI
- Ashburner, 2005, Unified segmentation, Neuroimage, 26, 839, 10.1016/j.neuroimage.2005.02.018 · DOI · Paper page
- Tzourio-Mazoyer, 2002, Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage, 15, 273, 10.1006/nimg.2001.0978 · DOI · Paper page
- Whitwell, 2011, Temporoparietal atrophy: a marker of AD pathology independent of clinical diagnosis, Neurobiol Aging, 32, 1531, 10.1016/j.neurobiolaging.2009.10.012 · DOI
- Li, 2008, ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies, Biostatistics, 9, 566, 10.1093/biostatistics/kxm050 · DOI
- Galton, 2000, Atypical and typical presentations of Alzheimer's disease: a clinical, neuropsychological, neuroimaging and pathological study of 13 cases, Brain, 123, 484, 10.1093/brain/123.3.484 · DOI
- Josephs, 2008, Progressive aphasia secondary to Alzheimer disease vs FTLD pathology, Neurology, 70, 25, 10.1212/01.wnl.0000287073.12737.35 · DOI
- Lehmann, 2010, Reduced cortical thickness in the posterior cingulate gyrus is characteristic of both typical and atypical Alzheimer's disease, J Alzheimers Dis, 20, 587, 10.3233/JAD-2010-1401 · DOI
- Migliaccio, 2009, Clinical syndromes associated with posterior atrophy: early age at onset AD spectrum, Neurology, 73, 1571, 10.1212/WNL.0b013e3181c0d427 · DOI
- Licht, 2007, Cognitive differences between early- and late-onset Alzheimer's disease, Am J Alzheimer's Dis Other Demen, 22, 218, 10.1177/1533317506299156 · DOI
- Frisoni, 2005, Structural correlates of early and late onset Alzheimer's disease: voxel-based morphometric study, J Neurol Neurosurg Psychiatry, 76, 112, 10.1136/jnnp.2003.029876 · DOI
- Ishii, 2005, Voxel-based morphometric comparison between early- and late-onset mild Alzheimer's disease and assessment of diagnostic performance of z score images, AJNR Am J Neuroradiol, 26, 333
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Sweeney, M. D., Sagare, A. P., & Zlokovic, B. V. (2018) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.188 · Cited by 3,076
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Metadata
- Authors
- Whitwell, J. L., Dickson, D. W., Murray, M. E., Weigand, S. D., Tosakulwong, N., Senjem, M. L., Knopman, D. S., Boeve, B. F., Parisi, J. E., Petersen, R. C., Jack, C. R., & Josephs, K. A.
- Venue
- The Lancet Neurology
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 11
- Issue
- 10
- Pages
- 868-877
- ISSN
- 1474-4422
- PMC
- PMC3490201
- Cited by
- 419
- References
- 6
- Language
- en
- Funders
- National Institutes of Health (P50-AG016574, R01-AG011378, R21-AG038736, R01-AG037491)
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