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The cortical signature of prodromal AD: Regional thinning predicts mild AD dementia
Open accessAbstract
OBJECTIVE: We previously used exploratory analyses across the entire cortex to determine that mild Alzheimer disease (AD) is reliably associated with a cortical signature of thinning in specific limbic and association regions. Here we investigated whether the cortical signature of AD-related thinning is present in individuals with questionable AD dementia (QAD) and whether a greater degree of regional cortical thinning predicts mild AD dementia. METHODS: Participants included 49 older adults with mild impairment consistent with mild cognitive impairment (Clinical Dementia Rating [CDR] = 0.5) at the time of structural MRI scanning. Cortical thickness was measured in nine regions of interest (ROIs) identified previously from a comparison of patients with mild AD and controls. RESULTS: Longitudinal clinical follow-up revealed that 20 participants converted to mild AD dementia (progressors) while 29 remained stable (nonprogressors) approximately 2.5 years after scanning. At baseline, QAD participants showed a milder degree of cortical thinning than typically seen in mild AD, and CDR Sum-of-Boxes correlated with thickness in temporal and parietal ROIs. Compared to nonprogressors, progressors showed temporal and parietal thinning. Using receiver operating characteristic curves, the thickness of an aggregate measure of these regions predicted progression to mild AD with 83% sensitivity and 65% specificity. CONCLUSIONS: Thinning in specific cortical areas known to be affected by Alzheimer disease (AD) is detectable in individuals with questionable AD dementia (QAD) and predicts conversion to mild AD dementia. This method could be useful for identifying individuals at relatively high risk for imminent progression from QAD to mild AD dementia, which may be of value in clinical trials.
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Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
Buckner, R. L., Snyder, A. Z., Shannon, B. J., LaRossa, G., Sachs, R., Fotenos, A. F., Sheline, Y. I., Klunk, W. E., Mathis, C. A., Morris, J. C., et al. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.2177-05.2005 · Cited by 2,140
Self-citationThinning of the Cerebral Cortex in Aging
Salat, D. H. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh032 · Cited by 1,851
Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment
Jack, C. R., Petersen, R. C., Xu, Y. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Waring, S. C., Tangalos, E. G., & Kokmen, E. (1999) · Neurology · doi:10.1212/wnl.52.7.1397 · Cited by 1,404
Regional and progressive thinning of the cortical ribbon in Huntington’s disease
Rosas, H. D., Liu, A. K., Hersch, S., Glessner, M., Ferrante, R. J., Salat, D. H., van der Kouwe, A., Jenkins, B. G., Dale, A. M., & Fischl, B. (2002) · Neurology · doi:10.1212/wnl.58.5.695 · Cited by 1,044
Regionally Localized Thinning of the Cerebral Cortex in Schizophrenia
Kuperberg, G. R., Broome, M. R., McGuire, P. K., David, A. S., Eddy, M., Ozawa, F., Goff, D., West, W. C., Williams, S. C. R., van der Kouwe, A. J. W., et al. (2003) · Archives of General Psychiatry · doi:10.1001/archpsyc.60.9.878 · Cited by 843
Chételat, G., Landeau, B., Eustache, F., Mézenge, F., Viader, F., de la Sayette, V., Desgranges, B., & Baron, J.-C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.05.015 · Cited by 526
MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment
Whitwell, J. L., Shiung, M. M., Przybelski, S. A., Weigand, S. D., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2008) · Neurology · doi:10.1212/01.wnl.0000280575.77437.a2 · Cited by 357
Alzheimer’s disease-related alterations in synaptic density: neocortex and hippocampus.
Scheff, S. W., & Price, D. A. (2006) · Journal of Alzheimer's Disease · doi:10.3233/jad-2006-9s312 · Cited by 284
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Morris, J. C., Roe, C. M., Xiong, C., Fagan, A. M., Goate, A. M., Holtzman, D. M., & Mintun, M. A. (2010) · Annals of Neurology · doi:10.1002/ana.21843 · Cited by 830
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Bickart, K. C., Wright, C. I., Dautoff, R. J., Dickerson, B. C., & Barrett, L. F. (2011) · Nature Neuroscience · doi:10.1038/nn.2724 · Cited by 556
Self-citationFagan, A. M., Mintun, M. A., Shah, A. R., Aldea, P., Roe, C. M., Mach, R. H., Marcus, D., Morris, J. C., & Holtzman, D. M. (2009) · EMBO Molecular Medicine · doi:10.1002/emmm.200900048 · Cited by 353
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Nir, T. M., Jahanshad, N., Villalon-Reina, J. E., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M. (2013) · NeuroImage: Clinical · doi:10.1016/j.nicl.2013.07.006 · Cited by 348
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Becker, J. A., Hedden, T., Carmasin, J., Maye, J., Rentz, D. M., Putcha, D., Fischl, B., Greve, D. N., Marshall, G. A., Salloway, S., et al. (2011) · Annals of Neurology · doi:10.1002/ana.22333 · Cited by 342
Jacobs, H. I. L., Van Boxtel, M. P. J., Jolles, J., Verhey, F. R. J., & Uylings, H. B. M. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.06.009 · Cited by 293
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Campbell, N. L., Unverzagt, F., LaMantia, M. A., Khan, B. A., & Boustani, M. A. (2013) · Clinics in Geriatric Medicine · doi:10.1016/j.cger.2013.07.009 · Cited by 288
Wolk, D. A., Dickerson, B. C., Initiative, T. A. D. N., Weiner, M., Aiello, M., Aisen, P., Albert, M. S., Alexander, G., Anderson, H. S., Anderson, K., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1001412107 · Cited by 248
Self-citationLombardi, G., Crescioli, G., Cavedo, E., Lucenteforte, E., Casazza, G., Bellatorre, A.-G., Lista, C., Costantino, G., Frisoni, G., Virgili, G., et al. (2020) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009628.pub2 · Cited by 214
Weston, P. S. J., Simpson, I. J. A., Ryan, N. S., Ourselin, S., & Fox, N. C. (2015) · Alzheimer's Research & Therapy · doi:10.1186/s13195-015-0132-3 · Cited by 214
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Jack, C. R. (2012) · Radiology · doi:10.1148/radiol.12110433 · Cited by 211
Donovan, N. J., Wadsworth, L. P., Lorius, N., Locascio, J. J., Rentz, D. M., Johnson, K. A., Sperling, R. A., & Marshall, G. A. (2014) · The American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2013.03.006 · Cited by 106
Quantitative structural MRI for early detection of Alzheimer's disease
McEvoy, L. K., & Brewer, J. B. (2010) · Expert Review of Neurotherapeutics · doi:10.1586/ern.10.162 · Cited by 90
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Lefterov, I., Wolfe, C. M., Fitz, N. F., Nam, K. N., Letronne, F., Biedrzycki, R. J., Kofler, J., Han, X., Wang, J., Schug, J., et al. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0558-0 · Cited by 74
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Illán‐Gala, I., Pegueroles, J., Montal, V., Vilaplana, E., Carmona‐Iragui, M., Alcolea, D., Dickerson, B. C., Sánchez‐Valle, R., de Leon, M. J., Blesa, R., et al. (2018) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.03.004 · Cited by 62
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Alliance for aging research AD biomarkers work group: structural MRI
Jack, C. R. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.09.011 · Cited by 64
Schwarz, A. J., Sundell, K. L., Charil, A., Case, M. G., Jaeger, R. K., Scott, D., Bracoud, L., Oh, J., Suhy, J., Pontecorvo, M. J., et al. (2019) · Alzheimer's & Dementia: Translational Research & Clinical Interventions · doi:10.1016/j.trci.2019.05.007 · Cited by 40
“01) [22], whereas significant negative correlations between cortical thickness and CDR box scores in the parietal, but not in the temporal, lobe was found in patients with AD [23].”
BackgroundSelf-citationStaging Neurodegenerative Disorders: Structural, Regional, Biomarker, and Functional Progressions
Archer, T., Kostrzewa, R. M., Beninger, R. J., & Palomo, T. (2011) · Neurotoxicity Research · doi:10.1007/s12640-010-9190-2 · Cited by 34
Bae, S., Harada, K., Lee, S., Harada, K., Makino, K., Chiba, I., Park, H., & Shimada, H. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9051312 · Cited by 29
White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease
Tian, J., Raghavan, S., Reid, R. I., Przybelski, S. A., Lesnick, T. G., Gebre, R. K., Graff-Radford, J., Schwarz, C. G., Lowe, V. J., Kantarci, K., et al. (2023) · Neurology · doi:10.1212/wnl.0000000000207250 · Cited by 29
Wang, Z., Dai, Z., Shu, H., Liu, D., Guo, Q., He, Y., & Zhang, Z. (2017) · Journal of Alzheimer’s Disease · doi:10.3233/jad-160724 · Cited by 26
Cortical signatures of cognition and their relationship to Alzheimer’s disease
Initiative, F. T. 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 25
Teipel, S. J., Cavedo, E., Weschke, S., Grothe, M. J., Rojkova, K., Fontaine, G., Dauphinot, L., Gonzalez-Escamilla, G., Potier, M.-C., Bertin, H., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.05.016 · Cited by 22
Metadata
- Authors
- Bakkour, A., Morris, J. C., & Dickerson, B. C.
- Venue
- Neurology
- Published
- 2009-03-24
- Volume
- 72
- Issue
- 12
- Pages
- 1048-1055
- ISSN
- 0028-3878 (ISSN-L)
- 1526-632X
- OpenAlex
- W2062860346
- Cited by
- 317
- References
- 9
- FWCI
- 11.07
- Type
- journal article
- Language
- en
- Open access
- bronze
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