Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns
Open accessPublished version of a preprint
Abstract
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date, subtyping of MCI has been based primarily on cognitive measures, often resulting in indistinct boundaries between subgroups and limited validity. Here, we introduce a subtyping method for MCI based solely upon brain atrophy. We train a deep learning model to differentiate between Alzheimer's disease (AD) and cognitively normal (CN) subjects based on whole-brain MRI features. We then deploy the trained model to classify MCI subjects based on whole-brain gray matter resemblance to AD-like or CN-like patterns. We subsequently validate the subtyping approach using cognitive, clinical, fluid biomarker, and molecular imaging data. Overall, the results suggest that atrophy patterns in MCI are sufficiently heterogeneous and can thus be used to subtype individuals into biologically and clinically meaningful subgroups.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI technologies are being developed to detect early signs of dementia through speech and language patterns analyzing short snippets of speech to predict and monitor cognitive decline.
“Notably, there are AI technologies being developed to detect early signs of dementia through speech and language patterns analyzing short snippets of speech to predict and monitor cognitive decline (Kwak et al., 2021).”
From The impact of digital technology, social media, and artificial intelligence on cognitive functions: a review (2023) · cites this paper as Kwak et al., 2021
The Discussion describes predicting progression from mild cognitive impairment to Alzheimer’s disease using brain-atrophy patterns, not speech. The Methods mention language tests, but neither those passages nor searches for short speech snippets and speech-pattern analysis establish the claimed AI technology. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Nonparametric Estimation from Incomplete Observations
Kaplan, E. L., & Meier, P. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501452 · Cited by 39,158 · see citing works
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341 · see citing works
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594 · 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 1× · see citing works
“Our results also suggest that the occipital lobe played a more minor role in the performance of the model, consistent with Braak’s staging scheme, 57 where the occipital lobe is shown to be affectedonlyatlaterstagesofAD.”
ResultDesikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324 · see citing works
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
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · see citing works
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980 · see citing works
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122 · 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
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507 · see citing works
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501 · see citing works
Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L. ‐., Nordberg, A., Bäckman, L., Albert, M., Almkvist, O., et al. (2004) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2004.01380.x · Cited by 4,946 · see citing works
Current concepts in mild cognitive impairment
Petersen, R. C., Doody, R., Kurz, A., Mohs, R. C., Morris, J. C., Rabins, P. V., Ritchie, K., Rossor, M., Thal, L., & Winblad, B. (2001) · Archives of Neurology · doi:10.1001/archneur.58.12.1985 · Cited by 4,781 · see citing works
Automatically Parcellating the Human Cerebral Cortex
Fischl, B. R. (2003) · Cerebral Cortex · doi:10.1093/cercor/bhg087 · Cited by 4,208 · see citing works
Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria
Dubois, B., Feldman, H. H., Jacova, C., Hampel, H., Molinuevo, J. L., Blennow, K., DeKosky, S. T., Gauthier, S., Selkoe, D., Bateman, R., et al. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70090-0 · Cited by 3,321 · see citing works
Braak, H., Alafuzoff, I., Arzberger, T., Kretzschmar, H., & Del Tredici, K. (2006) · Acta Neuropathologica · doi:10.1007/s00401-006-0127-z · Cited by 3,172 · see citing works
The Regression Analysis of Binary Sequences
Cox, D. R. (1958) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1958.tb00292.x · Cited by 2,909 · see citing works
Albert, M., DeKosky, S. T., Dickson, D. W., Dubois, B., Feldman, H., Fox, N. C., Gamst, A. C., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2013) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.11.1.96 · Cited by 2,630 · 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
Petersen, R. C., Stevens, J. C., Ganguli, M., Tangalos, E. G., Cummings, J. L., & DeKosky, S. T. (2001) · Neurology · doi:10.1212/wnl.56.9.1133 · Cited by 2,065 · see citing works
Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works
Hansson, O., Zetterberg, H., Buchhave, P., Londos, E., Blennow, K., & Minthon, L. (2006) · The Lancet Neurology · doi:10.1016/s1474-4422(06)70355-6 · Cited by 1,700 · see citing works
Prevalence of Cerebral Amyloid Pathology in Persons Without Dementia: A Meta-analysis
Jansen, W. J., Ossenkoppele, R., Knol, D. L., Tijms, B. M., Scheltens, P., Verhey, F. R. J., Visser, P. J., Aalten, P., Aarsland, D., Alcolea, D., et al. (2015) · JAMA · doi:10.1001/jama.2015.4668 · Cited by 1,610 · see citing works
Mild Cognitive Impairment: Ten Years Later
Petersen, R. C., Roberts, R. O., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Smith, G. E., & Jack, C. R. (2009) · Archives of Neurology · doi:10.1001/archneurol.2009.266 · Cited by 1,447 · see citing works
101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol
Klein, A., & Tourville, J. A. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00171 · Cited by 1,195 · see citing works
CSF Biomarkers and Incipient Alzheimer Disease in Patients With Mild Cognitive Impairment
Mattsson, N. (2009) · JAMA · doi:10.1001/jama.2009.1064 · Cited by 1,176 · see citing works
Dynamics of Gray Matter Loss in Alzheimer's Disease
Thompson, P. M., Hayashi, K. M., de Zubicaray, G., Janke, A. L., Rose, S. E., Semple, J., Herman, D., Hong, M. S., Dittmer, S. S., Doddrell, D. M., et al. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-03-00994.2003 · Cited by 1,132 · see citing works
Raincloud plots: a multi-platform tool for robust data visualization
Allen, M., Poggiali, D., Whitaker, K. J., Marshall, T. R., & Kievit, R. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15191.1 · Cited by 1,125 · see citing works
Robust statistical methods in R using the WRS2 package
Mair, P., & Wilcox, R. R. (2019) · Behavior Research Methods · doi:10.3758/s13428-019-01246-w · Cited by 1,089 · see citing works
Yang, G., Yu, S., Dong, H., Slabaugh, G., Dragotti, P. L., Ye, X., Liu, F., Arridge, S., Keegan, J., Guo, Y., et al. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2785879 · Cited by 1,075 · see citing works
Grundman, M. (2004) · Archives of Neurology · doi:10.1001/archneur.61.1.59 · Cited by 972 · see citing works
Rates of hippocampal atrophy correlate with change in clinical status in aging and AD
Jack, C. R., Petersen, R. C., Xu, Y., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Tangalos, E. G., & Kokmen, E. (2000) · Neurology · doi:10.1212/wnl.55.4.484 · Cited by 937 · see citing works
Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD
Jack, C. R., Shiung, M. M., Gunter, J. L., O’Brien, P. C., Weigand, S. D., Knopman, D. S., Boeve, B. F., Ivnik, R. J., Smith, G. E., Cha, R. H., et al. (2004) · Neurology · doi:10.1212/01.wnl.0000110315.26026.ef · Cited by 827 · cited 1× · see citing works
“cortex.(26,53,54) These findings are also consistent with recent findings on thepossible neuroprotective role of redundancy in the hippocampus(55) and other large-scale brain networks,(56) which may contribute to the observed differences between the subgroups.”
ResultLaMontagne, P. J., Benzinger, T. L., Morris, J. C., Keefe, S., Hornbeck, R., Xiong, C., Grant, E., Hassenstab, J., Moulder, K., Vlassenko, A. G., et al. (2019) · medRxiv · doi:10.1101/2019.12.13.19014902 · Cited by 779 · see citing works
Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI
Landau, S. M., Harvey, D., Madison, C. M., Koeppe, R. A., Reiman, E. M., Foster, N. L., Weiner, M. W., & Jagust, W. J. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2009.07.002 · Cited by 750 · see citing works
Scahill, R. I., Schott, J. M., Stevens, J., Rossor, M. N., & Fox, N. C. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.052587399 · Cited by 748 · 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
Comparing predictors of conversion and decline in mild cognitive impairment
Landau, S. M., Harvey, D., Madison, C. M., Reiman, E. M., Foster, N. L., Aisen, P. S., Petersen, R. C., Shaw, L. M., Trojanowski, J. Q., Jack, C. R., et al. (2010) · Neurology · doi:10.1212/wnl.0b013e3181e8e8b8 · Cited by 616 · see citing works
Verbal fluency in aging and dementia
Rosen, W. G. (1980) · Journal of Clinical and Experimental Neuropsychology · doi:10.1080/01688638008403788 · Cited by 597 · see citing works
Young, A. L., Marinescu, R. V., Oxtoby, N. P., Bocchetta, M., Yong, K., Firth, N. C., Cash, D. M., Thomas, D. L., Dick, K. M., Cardoso, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05892-0 · Cited by 596 · see citing works
Tabert, M. H., Manly, J. J., Liu, X., Pelton, G. H., Rosenblum, S., Jacobs, M. B., Zamora, D., Goodkind, M. S., Bell, K. L., Stern, Y., et al. (2006) · Archives of General Psychiatry · doi:10.1001/archpsyc.63.8.916 · Cited by 595 · see citing works
Frequency and course of mild cognitive impairment in a multiethnic community
Manly, J. J., Tang, M., Schupf, N., Stern, Y., Vonsattel, J., & Mayeux, R. P. (2008) · Annals of Neurology · doi:10.1002/ana.21326 · Cited by 592 · see citing works
Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment
DeCarli, C. S. (2002) · The Lancet Neurology · doi:10.1016/s1474-4422(03)00262-x · Cited by 569 · see citing works
The topography of grey matter involvement in early and late onset Alzheimer's disease
Frisoni, G. B., Pievani, M., Testa, C., Sabattoli, F., Bresciani, L. C., Bonetti, M., Beltramello, A., Hayashi, K. M., Toga, A. W., & Thompson, P. M. (2007) · Brain · doi:10.1093/brain/awl377 · Cited by 433 · see citing works
Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β‐amyloid
Landau, S. M., Lu, M., Joshi, A. D., Pontecorvo, M., Mintun, M. A., Trojanowski, J. Q., Shaw, L. M., Jagust, W. J., & Initiative, F. T. A. D. N. (2013) · Annals of Neurology · doi:10.1002/ana.23908 · Cited by 401 · see citing works
A meta-analysis of hippocampal atrophy rates in Alzheimer's disease
Barnes, J., Bartlett, J. W., van de Pol, L. A., Loy, C. T., Scahill, R. I., Frost, C. D., Thompson, P. M., & Fox, N. C. (2008) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2008.01.010 · Cited by 396 · see citing works
Detection of grey matter loss in mild Alzheimer's disease with voxel based morphometry
Frisoni, G. B. (2002) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.73.6.657 · Cited by 386 · see citing works
Update on the Magnetic Resonance Imaging core of the Alzheimer's Disease Neuroimaging Initiative
Jack, C. R., Bernstein, M. A., Borowski, B., Gunter, J. L., Fox, N. C., Thompson, P. M., Schuff, N. W., Krüger, G., Killiany, R., DeCarli, C. S., et al. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.004 · Cited by 379 · see citing works
Koepsell, T. D., & Monsell, S. E. (2012) · Neurology · doi:10.1212/wnl.0b013e31826e26b7 · Cited by 369 · see citing works
The Role of Eyebrows in Face Recognition
Sadr, J., Jarudi, I. N., & Sinha, P. (2003) · Perception · doi:10.1068/p5027 · Cited by 367 · see citing works
Ewers, M., Franzmeier, N., Suárez-Calvet, M., Morenas-Rodriguez, E., Caballero, M. A. A., Kleinberger, G., Piccio, L., Cruchaga, C., Deming, Y., Dichgans, M., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aav6221 · Cited by 327 · see citing works
Davatzikos, C., Xu, F., An, Y., Fan, Y., & Resnick, S. M. (2009) · Brain · doi:10.1093/brain/awp091 · Cited by 315 · see citing works
Progression to dementia in patients with isolated memory loss
Bowen, J. D., Teri, L. L., Kukull, W. A., McCormick, W. C., McCurry, S. M., & Larson, E. B. (1997) · The Lancet · doi:10.1016/s0140-6736(96)08256-6 · Cited by 309 · see citing works
Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects
Trojanowski, J. Q., Vandeerstichele, H., Korecka, M., Clark, C. M., Aisen, P. S. S., Petersen, R. C., Blennow, K., Soares, H. D., Simon, A. J., Lewczuk, P., et al. (2010) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2010.03.008 · Cited by 308 · see citing works
Callen, D. J., Black, S. E., Gao, F., Caldwell, C. B., & Szalai, J. P. (2001) · Neurology · doi:10.1212/wnl.57.9.1669 · Cited by 301 · see citing works
Li, H., Habes, M., Wolk, D. A., Yong Fan, A., & Initiative, A. D. N. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.02.007 · Cited by 273 · see citing works
Edmonds, E. C., Delano‐Wood, L., Clark, L. R., Jak, A. J., Nation, D. A., McDonald, C. R., Libon, D. J., Au, R., Galasko, D., Salmon, D. P., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.03.005 · Cited by 256 · see citing works
Nonamnestic mild cognitive impairment progresses to dementia with Lewy bodies
Ferman, T. J., Smith, G. E., Kantarci, K., Boeve, B. F., Pankratz, V. S., Dickson, D. W., Graff-Radford, N. R., Wszolek, Z., Van Gerpen, J., Uitti, R., et al. (2013) · Neurology · doi:10.1212/01.wnl.0000436942.55281.47 · Cited by 236 · see citing works
Gray Matter Age Prediction as a Biomarker for Risk of Dementia
Wang, J. Z., Knol, M. J., Tiulpin, A., Dubost, F., de Bruijne, M., Vernooij, M. W., Adams, H. H. H., Ikram, M. A., Niessen, W. J., & Roshchupkin, G. V. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902376116 · Cited by 211 · see citing works
Is Mild Cognitive Impairment Prodromal for Vascular Dementia Like Alzheimer’s Disease?
Meyer, J. S., Xu, G., Thornby, J. A., Chowdhury, M. H., & Quach, M. (2002) · Stroke · doi:10.1161/01.str.0000024432.34557.10 · Cited by 195 · see citing works
Domain Transfer Learning for MCI Conversion Prediction
Cheng, B., Liu, M., Zhang, D., Munsell, B. C., & Shen, D. G. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2404809 · Cited by 192 · see citing works
Reisberg, ., Ferris, S. H., de Leon, M. J., Franssen, E., Kluger, A., Mir, P., Borenstein, J., GEORGE, A. E., Shulman, E., Steinberg, G., et al. (1988) · Drug Development Research · doi:10.1002/ddr.430150203 · Cited by 191 · see citing works
Anatomical heterogeneity of Alzheimer disease
Noh, Y., Jeon, S., Lee, J. M., Seo, S. W., Kim, G. H., Cho, H., Ye, B. S., Yoon, C. W., Kim, H. J., Chin, J., et al. (2014) · Neurology · doi:10.1212/wnl.0000000000001003 · Cited by 183 · see citing works
Zhang, X., Mormino, E. C., Sun, N., Sperling, R. A., Sabuncu, M. R., Yeo, B. T. T., Initiative, T. A. D. N., Weiner, M. W., Aisen, P., Weiner, M. W., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611073113 · Cited by 178 · see citing works
Dong, A., Toledo, J. B., Honnorat, N., Doshi, J. J., Varol, E., Sotiras, A., Wolk, D. A., Trojanowski, J. Q., Davatzikos, C. A., & Initiative, A. D. N. (2016) · Brain · doi:10.1093/brain/aww319 · Cited by 177 · 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
A multiomics approach to heterogeneity in Alzheimer’s disease: focused review and roadmap
Badhwar, A., McFall, G. P., Sapkota, S., Black, S. E., Chertkow, H. M., Duchesne, S., Masellis, M., Li, L., Dixon, R. A., & Bellec, P. (2019) · Brain · doi:10.1093/brain/awz384 · Cited by 168 · see citing works
Are Empirically-Derived Subtypes of Mild Cognitive Impairment Consistent with Conventional Subtypes?
Clark, L. R., Delano-Wood, L., Libon, D. J., McDonald, C. R., Nation, D. A., Bangen, K. J., Jak, A. J., Au, R., Salmon, D. P., & Bondi, M. W. (2013) · Journal of the International Neuropsychological Society · doi:10.1017/s1355617713000313 · Cited by 167 · see citing works
Disentangling Heterogeneity in Alzheimer’s Disease and Related Dementias Using Data-Driven Methods
Habes, M., Grothe, M. J., Tunc, B., McMillan, C., Wolk, D. A., & Davatzikos, C. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.01.016 · Cited by 167 · see citing works
Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline
ten Kate, M., Dicks, E., Visser, P. J., van der Flier, W. M., Teunissen, C. E., Barkhof, F., Scheltens, P., Tijms, B. M., & Initiative, A. D. N. (2018) · Brain · doi:10.1093/brain/awy264 · Cited by 150 · see citing works
Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations
Chong-yaw Wee, A., Liu, C., Lee, A., Poh, J. S., Ji, H., & Qiu, A. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101929 · Cited by 146 · see citing works
Predictive validity and diagnostic stability of mild cognitive impairment subtypes
Han, J. W., Kim, T. H., Lee, S. B., Park, J. H., Lee, J. J., Huh, Y., Park, J. E., Jhoo, J. H., Lee, D. Y., & Kim, K. W. (2012) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.08.007 · Cited by 131 · see citing works
Biological heterogeneity in ADNI amnestic mild cognitive impairment
Nettiksimmons, J., DeCarli, C. S., Landau, S. M., Beckett, L. A., & Initiative, A. D. N. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2013.09.003 · Cited by 118 · see citing works
Should mild cognitive impairment be subtyped?
Hughes, T. F., Snitz, B. E., & Ganguli, M. (2011) · Current Opinion in Psychiatry · doi:10.1097/yco.0b013e328344696b · Cited by 81 · see citing works
Gray matter atrophy rate as a marker of disease progression in AD
Anderson, V. M., Schott, J. M., Bartlett, J. W., Leung, K. K., Miller, D. H., & Fox, N. C. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.11.001 · Cited by 81 · see citing works
A longitudinal study of brain volume changes in normal aging
Takao, H., Hayashi, N., & Ohtomo, K. (2011) · European Journal of Radiology · doi:10.1016/j.ejrad.2011.10.011 · Cited by 79 · see citing works
Neuropsychological subtypes of incident mild cognitive impairment in the Mayo Clinic Study of Aging
Machulda, M. M., Lundt, E. S., Albertson, S. M., Kremers, W. K., Mielke, M. M., Knopman, D. S., Bondi, M. W., & Petersen, R. C. (2019) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2019.03.014 · Cited by 71 · see citing works
Tau and atrophy: domain-specific relationships with cognition
Initiative, A. D. N., Digma, L. A., Madsen, J. R., Reas, E. T., Dale, A. M., Brewer, J. B., & Banks, S. J. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-019-0518-8 · Cited by 64 · see citing works
Predicting long‐term clinical stability in amyloid‐positive subjects by FDG‐PET
Iaccarino, L., Sala, A., & Perani, D. (2019) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.782 · Cited by 56 · see citing works
Discordant amyloid-β PET and CSF biomarkers and its clinical consequences
de Wilde, A., Reimand, J., Teunissen, C. E., Zwan, M. D., Windhorst, A. D., Boellaard, R., van der Flier, W. M., Scheltens, P., van Berckel, B. N. M., Bouwman, F. H., et al. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-019-0532-x · Cited by 54 · see citing works
FDG- and amyloid-PET in Alzheimer's disease: is the whole greater than the sum of the parts?
Mosconi, L., & McHugh, P. F. (2011) · PubMed · Cited by 53 · see citing works
Kwak, K., Niethammer, M., Giovanello, K. S., Styner, M. A., Dayan, E., & Initiative, A. D. N. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab223 · Cited by 43 · see citing works
Self-citationNogovitsyn, N., de Souza, R. M., Muller, M., Srajer, A., Hassel, S., Arnott, S. R., Davis, A. D., Hall, G. B., Harris, J. K., Zamyadi, M., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.017 · Cited by 37 · see citing works
Accrual of functional redundancy along the lifespan and its effects on cognition
Sadiq, M. U., Langella, S., Giovanello, K. S., Mucha, P. J., & Dayan, E. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117737 · Cited by 33 · see citing works
Self-citationLower functional hippocampal redundancy in mild cognitive impairment
Langella, S., Sadiq, M. U., Mucha, P. J., Giovanello, K. S., Dayan, E., & Initiative, A. D. N. (2021) · Translational Psychiatry · doi:10.1038/s41398-020-01166-w · Cited by 29 · see citing works
Self-citationA highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia
Tam, A., Dansereau, C. L., Iturria‐Medina, Y., Urchs, S. G. W., Orban, P., Sharmarke, H., Breitner, J. C. S., Bellec, P., & Initiative, A. D. N. (2019) · GigaScience · doi:10.1093/gigascience/giz055 · Cited by 23 · see citing works
Pascoal, T. A., Therriault, J., Mathotaarachchi, S. S., Kang, M. S., Shin, M., Benedet, A. L., Chamoun, M., Tissot, C., Lussier, F. Z., Mohaddes, S., et al. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12037 · Cited by 16 · see citing works
Styner, M. A., Dayan, E., Giovanello, K. S., et al. (2024) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/cq3a-7342 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [29]Huang G. Liu Z. Van Der Maaten L. Weinberger K.Q. Densely connected convolutional networks. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition 2017 CVPR 2261 2269
- [41]Hosseini-Asl E. Keynton R. El-Baz A. Alzheimer’s disease diagnostics by adaptation of 3D convolutional network. In Proceedings - International Conference on Image Processing ICIP 2016 126 130
- [78]Kingma D.P. Ba J.L. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations. ICLR 2015 - Conference Track Proceedings 2015
Cited by
Sorted by citations
Shanmugasundaram, M., & Tamilarasu, A. (2023) · Frontiers in Cognition · doi:10.3389/fcogn.2023.1203077 · Cited by 291 · see citing works
- Authors
- Kwak, K., Giovanello, K. S., Bozoki, A., Styner, M., & Dayan, E.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Dayan, Eran
- Venue
- Cell Reports Medicine
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 2
- Issue
- 12
- Pages
- 100467
- ISSN
- 2666-3791 (ISSN-L)
- PMC
- PMC8714856
- OpenAlex
- W4206353576
- Cited by
- 47
- References
- 90
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 14 · 2023: 12 · 2022: 7
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Medicine (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion, other-oa)
- UNC Libraries
- Funders
- Pfizer
- Takeda Pharmaceutical Company
- Genentech
- Northern California Institute for Research and Education
- Merck
- Alzheimer's Disease Neuroimaging Initiative
- Bristol-Myers Squibb
- BioClinica
- Alzheimer's Drug Discovery Foundation
- Novartis Pharmaceuticals Corporation
- National Institute on Aging (R01AG062590)
- AbbVie
- National Institute of Biomedical Imaging and Bioengineering
- GE Healthcare
- Alzheimer's Association
- Fujirebio US
- Janssen Research and Development
- Servier
- National Institutes of Health (P01 AG003991, P01 AG026276, P30 NS09857781, P50 AG00561, R01 AG043434, R01 EB009352, UL1 TR000448)
- Biogen
- H. Lundbeck A/S
- U.S. Department of Defense (W81XWH-12-2-0012)
- University of Southern California
- IXICO
- 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, brain atrophy patterns, Alzheimer's disease, gray matter atrophy, deep learning classification, cognitive decline trajectories, fluid biomarkers, molecular imaging
- MeSH terms
- Deep Learning, Aged, Atrophy, Brain, Cognition, Female, Humans, Male, Reproducibility of Results, Cohort Studies, Biomarkers, Positron-Emission Tomography, 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 ›
Are you an author, or do you see a problem with this page? Request a review
