Accurate multimodal probabilistic prediction of conversion to Alzheimer’s disease in patients with mild cognitive impairment
Open accessAbstract
Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.
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Multi-omics machine learning is still at an early stage, yet it has been explored across many application areas.
“Although ML analysis of multi-omics is still in its embryonic stage, it has already been explored for a wide range of applications, as reported in recent reviews on brain diseases (Garali et al., 2018;Young et al., 2013), diabetes (Kavakiotis et al., 2017), cancers (Borad and LoRusso, 2017;Chaudhary et al., 2017;Wong et al., 2016) cardiovascular disease (Weng et al., 2017), medical imaging (Ericks”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Young et al., 2013
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Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705 · see citing works
Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., Roses, A. D., Haines, J. L., & Pericak-Vance, M. A. (1993) · Science · doi:10.1126/science.8346443 · Cited by 9,429 · 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
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676 · see citing works
Global prevalence of dementia: a Delphi consensus study
Ferri, C. P., Prince, M. J., Brayne, C., Brodaty, H., Fratiglioni, L., Ganguli, M., Hall, K. S., Hasegawa, K., Hendrie, H. C., Huang, Y., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67889-0 · Cited by 5,336 · see citing works
Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages
McNemar, Q. (1947) · Psychometrika · doi:10.1007/bf02295996 · Cited by 4,754 · see citing works
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,782 · see citing works
Circular analysis in systems neuroscience: the dangers of double dipping
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2303 · Cited by 2,696 · see citing works
Staging of alzheimer's disease-related neurofibrillary changes
Braak, H., & Braak, E. (1995) · Neurobiology of Aging · doi:10.1016/0197-4580(95)00021-6 · Cited by 2,357 · see citing works
Warfield, S. K., Zou, K. H., & Wells, W. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.828354 · Cited by 2,072 · see citing works
Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease
Corder, E. H., Saunders, A. M., Risch, N. J., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Rimmler, J. B., Locke, P. A., Conneally, P. M., Schmader, K. E., et al. (1994) · Nature Genetics · doi:10.1038/ng0694-180 · Cited by 1,920 · see citing works
The Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525 · 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
Multiple kernel learning, conic duality, and the SMO algorithm
Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015424 · Cited by 1,453 · see citing works
Expectation Propagation for approximate Bayesian inferencePreprint
Minka, T. P. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.2294 · Cited by 1,391 · see citing works
Multimodal classification of Alzheimer's disease and mild cognitive impairment
Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.008 · Cited by 1,312 · see citing works
Automatic classification of MR scans in Alzheimer’s disease.
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2008) · Brain · doi:10.1093/brain/awm319 · Cited by 1,170 · see citing works
Beach, T. G., Monsell, S. E., Phillips, L. E., & Kukull, W. (2012) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e31824b211b · Cited by 1,103 · cited 1× · see citing works
“(Beach et al., 2012).”
Fast free-form deformation using graphics processing units
Marc Modat, A., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., Fox, N. C., & Sebastien Ourselin, A. (2009) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.09.002 · Cited by 1,087 · see citing works
Overconfidence as a Cause of Diagnostic Error in Medicine
Berner, E. S., & Graber, M. L. (2008) · The American Journal of Medicine · doi:10.1016/j.amjmed.2008.01.001 · Cited by 1,034 · see citing works
Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging
Rowe, C. C., Ellis, K. A., Rimajova, M., Bourgeat, P., Pike, K. E., Jones, G., Fripp, J., Tochon-Danguy, H., Morandeau, L., O'Keefe, G., et al. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.04.007 · Cited by 986 · see citing works
Cuingnet, R., Gérardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M., Marie Chupin, A., Benali, H., & Olivier Colliot, A. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.013 · Cited by 962 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · 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
Siebner, H. R., Riemenschneider, M. J., Willoch, F., Minoshima, S., Schwaiger, M., Kurz, A., Drzezga, A. E., & Lautenschlager, N. T. (2003) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-003-1194-1 · Cited by 603 · see citing works
Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification
Davatzikos, C. A., Bhatt, P., Shaw, L. M. J., Batmanghelich, K. N., & Trojanowski, J. Q. (2010) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2010.05.023 · Cited by 578 · see citing works
Fan, Y., Batmanghelich, N., Clark, C. M., & Davatzikos, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.031 · Cited by 477 · see citing works
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
Vemuri, P., Gunter, J. L., Senjem, M. L., Whitwell, J. L., Kantarci, K., Knopman, D. S., Boeve, B. F., Petersen, R. C., & Jack, C. R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.073 · Cited by 455 · see citing works
Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve
Querbes, O., Aubry, F., Pariente, J., Lotterie, J.-A., Démonet, J., Duret, V., Puel, M., Berry, I., Fort, J., Celsis, P., et al. (2009) · Brain · doi:10.1093/brain/awp105 · Cited by 438 · see citing works
Gérardin, E., Chételat, G., Marie Chupin, A., Cuingnet, R., Desgranges, B., Kim, H., Niethammer, M., Dubois, B., Lehéricy, S., Garnero, L., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.036 · Cited by 415 · see citing works
Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease
Desikan, R. S., Cabral, H. J., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., Schmansky, N. J., Greve, D. N., Salat, D. H., Buckner, R. L., et al. (2009) · Brain · doi:10.1093/brain/awp123 · Cited by 405 · see citing works
Chris Hinrichs, A., Singh, V., Xu, G., & Johnson, S. C. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.10.081 · Cited by 405 · see citing works
MRI and CSF biomarkers in normal, MCI, and AD subjects
Vemuri, P., Wiste, H. J., Weigand, S. D., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., Knopman, D. S., Petersen, R. C., & Jack, C. R. (2009) · Neurology · doi:10.1212/wnl.0b013e3181af79fb · Cited by 399 · see citing works
Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest
Gousias, I. S., Rueckert, D., Heckemann, R. A., Dyet, L. E., Boardman, J. P., Edwards, A. D., & Hammers, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.034 · Cited by 356 · see citing works
Approximations for Binary Gaussian Process Classification
Nickisch, H., & Rasmussen, C. E. (2008) · Cited by 298 · see citing works
Lerch, J. P., Pruessner, J. C., Zijdenbos, A. P., Collins, D. L., Teipel, S. J., Hampel, H. J., & Evans, A. C. (2006) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2006.09.013 · Cited by 281 · see citing works
Pre-Clinical Detection of Alzheimer's Disease Using FDG-PET, with or without Amyloid Imaging
Mosconi, L., Berti, V., Glodzik, L., Pupi, A., Santi, S. D., & de Leon, M. J. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-091504 · Cited by 279 · see citing works
Walhovd, K. B., Fjell, A. M., Brewer, J., McEvoy, L. K., Fennema-Notestine, C., Hagler, D. J., Jennings, R. G., Karow, D., Dale, A. M., & Initiative, T. A. D. N. (2010) · American Journal of Neuroradiology · doi:10.3174/ajnr.a1809 · Cited by 279 · see citing works
Eskildsen, S. F., Pierrick Coupé, A., García-Lorenzo, D., Fonov, V., Pruessner, J. C., & Collins, D. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.058 · Cited by 269 · see citing works
Fonteijn, H., Marc Modat, A., Clarkson, M. J., Barnes, J., Lehmann, M., Hobbs, N. Z., Scahill, R. I., Tabrizi, S. J., Sebastien Ourselin, A., Fox, N. C., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.062 · Cited by 261 · see citing works
Chong-yaw Wee, A., Yap, P., Shen, D. G., & Initiative, A. D. N. (2012) · Human Brain Mapping · doi:10.1002/hbm.22156 · Cited by 255 · see citing works
Reducing between scanner differences in multi-center PET studies
Joshi, A., Koeppe, R. A., & Fessler, J. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.057 · Cited by 247 · see citing works
Automated hippocampal shape analysis predicts the onset of dementia in mild cognitive impairment
Costafreda, S. G., Dinov, I. D., Tu, Z., Shi, Y., Liu, C.-Y., Kłoszewska, I., Mecocci, P., Soininen, H. S., Tsolaki, M., Vellas, B. J., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.050 · Cited by 213 · see citing works
Fjell, A. M., Walhovd, K. B., Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Brewer, J. B., Dale, A. M., & Initiative, A. D. N. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.3785-09.2010 · Cited by 209 · see citing works
Yuan, L., Wang, Y., Thompson, P. M., Narayan, V. A., & Ye, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.059 · Cited by 195 · see citing works
Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease
Gray, K. R., Wolz, R., Heckemann, R. A., Aljabar, P., Hammers, A., & Rueckert, D. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.071 · Cited by 177 · see citing works
2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)
(2012) · doi:10.1109/isbi17944.2012 · Cited by 165 · see citing works
Vounou, M., Janoušová, E., Wolz, R., Stein, J. L., Thompson, P. M., Rueckert, D., & Montana, G. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.029 · Cited by 147 · see citing works
Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data
Initiative, A. D. N., Ye, J., Farnum, M. A., Yang, E., Verbeeck, R., Lobanov, V. S., Raghavan, N., Novak, G., DiBernardo, A., & Narayan, V. A. (2012) · BMC Neurology · doi:10.1186/1471-2377-12-46 · Cited by 143 · see citing works
CSF biomarkers for Alzheimer's disease: current utility and potential future use
Holtzman, D. M. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.09.003 · Cited by 122 · see citing works
Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease
Pierrick Coupé, A., Eskildsen, S. F., Manjón, J. V., Fonov, V., Pruessner, J. C., Michèle Allard, A., & Collins, D. L. (2012) · NeuroImage Clinical · doi:10.1016/j.nicl.2012.10.002 · Cited by 109 · see citing works
Brain Atrophy in Healthy Aging Is Related to CSF Levels of Aβ1-42
Fjell, A. M., Walhovd, K. B., Fennema‐Notestine, C., McEvoy, L. K., Hagler, D. J., Holland, D., Blennow, K., Brewer, J. B., Dale, A. M., & Initiative, T. A. D. N. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp279 · Cited by 105 · see citing works
Differentiating AD from aging using semiautomated measurement of hippocampal atrophy rates
Barnes, J., Scahill, R. I., Boyes, R. G., Frost, C. D., Lewis, E. B., Rossor, C. L., Rossor, M. N., & Fox, N. C. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.028 · Cited by 103 · see citing works
LoAd: A locally adaptive cortical segmentation algorithm
Cardoso, M. J., Clarkson, M. J., Ridgway, G. R., Marc Modat, A., Fox, N. C., & Sebastien Ourselin, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.013 · Cited by 84 · see citing works
Ferrarini, L., Frisoni, G. B., Pievani, M., Reiber, J. H. C., Ganzola, R., & Milles, J. (2009) · Journal of Alzheimer s Disease · doi:10.3233/jad-2009-1082 · Cited by 52 · see citing works
Engineered antibody approaches for Alzheimer’s disease immunotherapy
Robert, R., & Wark, K. L. (2012) · Archives of Biochemistry and Biophysics · doi:10.1016/j.abb.2012.02.022 · Cited by 41 · see citing works
Singh, N., Wang, A. Y., Sankaranarayanan, P., Fletcher, P., & Joshi, S. (2012) · International Conference on Medical Image Computing and Computer-Assisted Intervention · doi:10.1007/978-3-642-33415-3_17 · Cited by 32 · see citing works
Nho, K. T., Shen, L., Kim, S., Risacher, S. L., West, J. D., Foroud, T., Jack, C. R., Weiner, M. W., & Saykin, A. J. (2010) · PubMed Central · Cited by 27 · see citing works
Multi-STEPS: Multi-label similarity and truth estimation for propagated segmentations
Cardoso, M. J., Marc Modat, A., Sebastien Ourselin, A., Shiva Keihaninejad, A., & Cash, D. M. (2012) · IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) · doi:10.1109/mmbia.2012.6164748 · Cited by 24 · see citing works
Classification of Alzheimer's disease patients and controls with Gaussian processes
Young, J., Marc Modat, A., Cardoso, M. J., Ashburner, J., & Sebastien Ourselin, A. (2012) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2012.6235862 · Cited by 7 · see citing works
Chu, C., Bandettini, P. A., Ashburner, J., Marquand, A. F., & Klöppel, S. (2010) · doi:10.1109/wbd.2010.11 · Cited by 6 · see citing works
Marquand, A. F., Howard, M., Brammer, M. J., & Mourão-Miranda, J. (2009) · NeuroImage · doi:10.1016/s1053-8119(09)70221-6 · Cited by 2 · see citing works
Flagged first, then by citations
2 of the 64 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912 · see citing works
Using machine learning approaches for multi-omics data analysis: A review
Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works
Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.057 · Cited by 622 · see citing works
Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M. B., Neil, D., Saffari, A., Mead, R., Hautbergue, G. M., Holbrook, J. D., & Ferraiuolo, L. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0377-8 · Cited by 595 · see citing works
Wefel, J. S., Kesler, S. R., Noll, K. R., & Schagen, S. B. (2014) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21258 · Cited by 546 · see citing works
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation
Zhang, Y., Dong, Z., Wang, S., Yu, X., Yao, X., Zhou, Q., Hu, H., Li, M., Jiménez-Mesa, C., Ramı́rez, J., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.07.006 · Cited by 456 · see citing works
Spasov, S., Passamonti, L., Duggento, A., Lió, P., & Toschi, N. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.031 · Cited by 429 · see citing works
Predicting alzheimer’s disease progression using multi-modal deep learning approach
Lee, G., Nho, K., Kang, B., Sohn, K.-A., Kim, D., Initiative, F. A. D. N., Weiner, M. W., Aisen, P., Petersen, R., Jack, C. R., et al. (2019) · Scientific Reports · doi:10.1038/s41598-018-37769-z · Cited by 406 · see citing works
Lin, W.-M., Tong, T., Gao, Q., Guo, D., Du, X., Yang, Y., Guo, G., Xiao, M., Du, M., Qu, X., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00777 · Cited by 391 · see citing works
Lu, D., Popuri, K., Ding, G. W., Balachandar, R., Beg, M. F., Initiative, A. D. N., Weiner, M. W., Aisen, P., Petersen, R. C., Jack, C. R., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-22871-z · Cited by 386 · see citing works
Weiner, M. W., Veitch, D. P., Aisen, P. S., Beckett, L. A., Cairns, N. J., Cedarbaum, J., Green, R. C., Harvey, D., Jack, C. R., Jagust, W., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.11.001 · Cited by 348 · see citing works
A data-driven model of biomarker changes in sporadic Alzheimer’s disease
Young, A. L., Oxtoby, N. P., Daga, P., Cash, D. M., Fox, N. C., Ourselin, S., Schott, J. M., & Alexander, D. C. (2014) · Brain · doi:10.1093/brain/awu176 · Cited by 308 · see citing works
Grueso, S., & Viejo-Sobera, R. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00900-w · Cited by 303 · see citing works
A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images
Mehmood, A., Yang, S., Feng, Z., Wang, M., Smadi, A. A., Khan, R., Maqsood, M., & Yaqub, M. (2021) · Neuroscience · doi:10.1016/j.neuroscience.2021.01.002 · Cited by 293 · see citing works
Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection
Teipel, S. J., Drzezga, A. E., Grothe, M. J., Barthel, H., Chételat, G., Schuff, N. W., Skudlarski, P., Cavedo, E., Frisoni, G. B., Hoffmann, W. U., et al. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00093-9 · Cited by 263 · see citing works
Multi-modal classification of Alzheimer's disease using nonlinear graph fusion
Tong, T., Gray, K. R., Gao, Q., Chen, L., & Rueckert, D. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.009 · Cited by 258 · see citing works
Beheshti, I., Demirel, H., & Matsuda, H. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.02.011 · Cited by 246 · see citing works
Lombardi, 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 · see citing works
Lu, D., Popuri, K., Ding, G. W., Balachandar, R., & Beg, M. F. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.002 · Cited by 211 · see citing works
Nobili, F. M., Arbizu, J., Bouwman, F. H., Drzezga, A. E., Agosta, F., Nestor, P. J., Walker, Z., Boccardi, M., & the EANM‐EAN Task Force for the Prescription of FDG‐PET for Dementing Neurodegenerative Disorders (2018) · European Journal of Neurology · doi:10.1111/ene.13728 · Cited by 212 · see citing works
Korolev, I. O., Symonds, L. L., Bozoki, A., & Initiative, A. D. N. (2016) · PLoS ONE · doi:10.1371/journal.pone.0138866 · Cited by 192 · see citing works
Inference in the age of big data: Future perspectives on neuroscience
Bzdok, D., & Yeo, B. T. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.061 · Cited by 186 · see citing works
Dyrba, M., Grothe, M. J., Kirste, T., & Teipel, S. J. (2015) · Human Brain Mapping · doi:10.1002/hbm.22759 · Cited by 185 · see citing works
Gao, X., Shi, F., Shen, D. G., & Liu, M. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3097721 · Cited by 188 · see citing works
Structural neuroimaging as clinical predictor: A review of machine learning applications
Mateos-Pérez, J. M., Dadar, M., Lacalle-Aurioles, M., Iturria‐Medina, Y., Zeighami, Y., & Evans, A. C. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.08.019 · Cited by 185 · 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
Challis, E., Hurley, P. D., Serra, L., Bozzali, M., Oliver, S., & Cercignani, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.037 · Cited by 179 · see citing works
Samper‐Gonzàlez, J., Burgos, N., Bottani, S., Fontanella, S., Lu, P., Marcoux, A., Routier, A., Guillon, J., Bacci, M., Wen, J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.042 · Cited by 171 · see citing works
Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning
Janssen, R. J., Mourão-Miranda, J., & Schnack, H. G. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.04.004 · Cited by 168 · see citing works
Hypergraph based multi-task feature selection for multimodal classification of Alzheimer's disease
Shao, W., Peng, Y., Zu, C., Wang, M., & Zhang, D. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101663 · Cited by 167 · see citing works
Tong, T., Gao, Q., Guerrero, R., Ledig, C., Chen, L., Rueckert, D., & Initiative, A. D. N. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2549363 · Cited by 162 · see citing works
Gupta, Y., Lama, R. K., Kwon, G., & Initiative, A. D. N. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00072 · Cited by 157 · see citing works
Caminiti, S. P., Ballarini, T., Sala, A., Cerami, C., Presotto, L., Santangelo, R., Fallanca, F., Vanoli, E. G., Gianolli, L., Iannaccone, S., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.01.019 · Cited by 146 · see citing works
Liu, X., Chen, K., Wu, T., Weidman, D. A., Lure, F. Y. M., & Li, J. (2018) · Translational research · doi:10.1016/j.trsl.2018.01.001 · Cited by 141 · see citing works
Kang, W., Lin, L., Zhang, B., Shen, X., & Wu, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104678 · Cited by 137 · see citing works
Lorenzi, M., Filippone, M., Frisoni, G. B., Alexander, D. C., & Sebastien Ourselin, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.059 · Cited by 134 · see citing works
Amoroso, N., Diacono, D., Fanizzi, A., Rocca, M. L., Monaco, A., Lombardi, A., Guaragnella, C., Bellotti, R., & Tangaro, S. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.12.011 · Cited by 131 · see citing works
Multi-modal cross-attention network for Alzheimer’s disease diagnosis with multi-modality data
Zhang, J., He, X., Liu, Y., Cai, Q., Chen, H., & Qing, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107050 · Cited by 117 · see citing works
Predictive modelling using neuroimaging data in the presence of confounds
Rao, A. W., Monteiro, J. M., & Mourão-Miranda, J. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.066 · Cited by 114 · see citing works
Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease
Johnson, P., Vandewater, L., Wilson, W. J., Maruff, P. T., Savage, G. R., Graham, P. L., Macaulay, L. S., Ellis, K. A., Szoeke, C. E. I., Martins, R. N., et al. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s16-s11 · Cited by 110 · see citing works
Ahmed, O. B., Benois‐Pineau, J., Allard, M., Catheline, G., & Amar, C. B. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.08.041 · Cited by 110 · see citing works
Early identification of MCI converting to AD: a FDG PET study
Pagani, M., Nobili, F. M., Morbelli, S. D., Arnaldi, D., Giuliani, A., Öberg, J., Girtler, N. G., Brugnolo, A., Picco, A., Bauckneht, M., et al. (2017) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-017-3761-x · Cited by 104 · see citing works
Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI
Hojjati, S. H., Ebrahimzadeh, A., & Abbas Babajani-feremi, A. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00904 · Cited by 105 · see citing works
Gupta, Y., Lee, K. H., Choi, K. Y., Lee, J. J., Kim, B. C., Kwon, G., & Initiative, A. D. N. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222446 · Cited by 107 · see citing works
Multiview learning for understanding functional multiomics
Nguyen, N. D., & Wang, D. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007677 · Cited by 107 · see citing works
Imaging plus X: multimodal models of neurodegenerative disease
Oxtoby, N. P., & Alexander, D. C. (2017) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000460 · Cited by 105 · see citing works
Kim, Y., & Na, K. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.06.024 · Cited by 102 · see citing works
Multimodal manifold-regularized transfer learning for MCI conversion prediction
Initiative, A. D. N., Cheng, B., Liu, M., Suk, H., Shen, D. G., & Zhang, D. (2015) · Brain Imaging and Behavior · doi:10.1007/s11682-015-9356-x · Cited by 102 · see citing works
Alzheimer Disease Classification through Transfer Learning Approach
Raza, N., Naseer, A., Tamoor, M., & Zafar, K. (2023) · Diagnostics · doi:10.3390/diagnostics13040801 · Cited by 106 · see citing works
TADPOLE Challenge: Prediction of Longitudinal Evolution in Alzheimer's DiseasePreprint
Marinescu, R. V., Oxtoby, N. P., Young, A. L., Bron, E. E., Toga, A. W., Weiner, M. W., Barkhof, F., Fox, N. C., Klein, S., Alexander, D. C., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.03909 · Cited by 98 · see citing works
Dyrba, M., Barkhof, F., Fellgiebel, A., Filippi, M., Hausner, L., Hauenstein, K., Kirste, T., Teipel, S. J., & the EDSD study group (2015) · Journal of Neuroimaging · doi:10.1111/jon.12214 · Cited by 96 · see citing works
Popuri, K., Ma, D., Wang, L., & Beg, M. F. (2020) · Human Brain Mapping · doi:10.1002/hbm.25115 · Cited by 96 · see citing works
Teipel, S. J., Kurth, J., Krause, B. J., & Grothe, M. J. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.05.006 · Cited by 94 · see citing works
Lin, W.-M., Gao, Q., Du, M., Chen, W., & Tong, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104478 · Cited by 95 · see citing works
Garibotto, V., Herholz, K., Boccardi, M., Picco, A., Varrone, A., Nordberg, A. K., Nobili, F. M., & Ratib, O. M. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.03.033 · Cited by 91 · see citing works
Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer’s Disease
Initiative., T. A. D. N., Cheng, B., Liu, M., Shen, D. G., Li, Z., & Zhang, D. (2016) · Neuroinformatics · doi:10.1007/s12021-016-9318-5 · Cited by 87 · see citing works
Clinical utility of FDG-PET for the clinical diagnosis in MCI
for the EANM-EAN Task Force for the Prescription of FDG-PET for Dementing Neurodegenerative Disorders, Arbizu, J., Festari, C., Altomare, D., Walker, Z., Bouwman, F. H., Rivolta, J., Orini, S., Barthel, H., Agosta, F., et al. (2018) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-018-4039-7 · Cited by 87 · see citing works
Ziegler, G., Ridgway, G. R., Dahnke, R., & Christian Gaser, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.018 · Cited by 86 · see citing works
Dimitriadis, S., Laskaris, N., Bitzidou, M., Tarnanas, I., & Tsolaki, M. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00350 · Cited by 88 · see citing works
Dallora, A. L., Eivazzadeh, S., Mendes, E., Berglund, J. S., & Anderberg, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179804 · Cited by 82 · see citing works
Liu, C.-F., Padhy, S., Ramachandran, S., Wang, V. X., Efimov, A. Y., Bernal, A., Shi, L., Vaillant, M., Ratnanather, J. T., Faria, A. V., et al. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.07.003 · Cited by 81 · see citing works
Heterogeneous data fusion for predicting mild cognitive impairment conversion
Shen, H. T., Zhu, X., Zhang, Z., Wang, S., Chen, Y., Xu, X., & Shao, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.08.023 · Cited by 81 · see citing works
Predicting outcome in clinically isolated syndrome using machine learning
Wottschel, V., Alexander, D. C., Kwok, P., Chard, D., Stromillo, M. L., Stefano, N. D., Thompson, A. J., Miller, D. H., & Ciccarelli, O. (2014) · NeuroImage Clinical · doi:10.1016/j.nicl.2014.11.021 · Cited by 81 · see citing works
Lin, W.-M., Gao, Q., Yuan, J., Chen, Z., Feng, C., Chen, W., Du, M., & Tong, T. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00077 · Cited by 80 · see citing works
Hedayati, R., Khedmati, M., & Taghipour‐Gorjikolaie, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102397 · Cited by 76 · see citing works
Joules, R., Doyle, O., Schwarz, A. J., O’Daly, O., Brammer, M. J., Williams, S., & Mehta, M. A. (2015) · Psychopharmacology · doi:10.1007/s00213-015-3951-9 · Cited by 76 · see citing works
Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers∗
Ritter, K., Schumacher, J., Weygandt, M., Buchert, R., Allefeld, C., Haynes, J., & Initiative, A. D. N. (2015) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2015.01.006 · Cited by 76 · see citing works
Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework
Ning, K., Chen, B., Sun, F., Hobel, Z. B., Zhao, L., Matloff, W. J., & Toga, A. W. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.04.009 · Cited by 76 · see citing works
Frölich, L., Peters, O., Lewczuk, P., Gruber, O., Teipel, S. J., Gertz, H., Jahn, H., Jessen, F., Kurz, A., Luckhaus, C., et al. (2017) · Alzheimer s Research & Therapy · doi:10.1186/s13195-017-0301-7 · Cited by 75 · see citing works
Marinescu, R. V., Oxtoby, N. P., Young, A. L., Bron, E. E., Toga, A. W., Weiner, M. W., Barkhof, F., Fox, N. C., Golland, P., Klein, S., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32281-6_1 · Cited by 72 · see citing works
Lei, B., Chen, S., Ni, D., & Wang, T. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00077 · Cited by 72 · see citing works
Mao, C., Xu, J., Rasmussen, L., Li, Y., Adekkanattu, P., Pacheco, J., Bonakdarpour, B., Vassar, R., Shen, L., Jiang, G., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104442 · Cited by 67 · see citing works
Robust multi-label transfer feature learning for early diagnosis of Alzheimer’s disease
Initiative, A. D. N., Cheng, B., Liu, M., Zhang, D., & Shen, D. G. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9846-8 · Cited by 69 · see citing works
Initiative, T. A. D. N., Wu, C., Guo, S., Hong, Y.-J., Xiao, B., Wu, Y., & Zhang, Q. (2018) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2018.10.17 · Cited by 69 · see citing works
Chen, Z., Liu, Y., Zhang, Y., & Li, Q. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102698 · Cited by 69 · see citing works
Mannheim, J. G., Schmid, A. M., Schwenck, J., Katiyar, P., Herfert, K., Pichler, B. J., & Disselhorst, J. A. (2018) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2018.02.011 · Cited by 70 · see citing works
Marinescu, R. V., Oxtoby, N. P., Young, A. L., Bron, E. E., Toga, A. W., Weiner, M. W., Barkhof, F., Fox, N. C., Eshaghi, A., Toni, T., et al. (2021) · Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2021-2dcc · Cited by 68 · see citing works
Dukart, J., Sambataro, F., Bertolino, A., & Initiative, A. D. N. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150570 · Cited by 68 · see citing works
Lehallier, B., Essioux, L., Gayán, J., Alexandridis, R. A., Nikolcheva, T., Wyss‐Coray, T., Britschgi, M., & Initiative, A. D. N. (2015) · JAMA Neurology · doi:10.1001/jamaneurol.2015.3135 · Cited by 67 · see citing works
Ota, K., Oishi, N., Ito, K., & Fukuyama, H. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.08.020 · Cited by 65 · see citing works
AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction
Gao, F., Yoon, H., Xu, Y., Goradia, D., Luo, J., Wu, T., & Su, Y. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102290 · Cited by 64 · see citing works
Westerlund, A. M., Hawe, J. S., Heinig, M., & Schunkert, H. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221910291 · Cited by 61 · see citing works
MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework
Lee, G., Kang, B., Nho, K. T., Sohn, K.-A., & Kim, D. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00617 · Cited by 62 · see citing works
A Cochrane review on brain [18F]FDG PET in dementia: limitations and future perspectives
On behalf of the European Association of Nuclear Medicine, Morbelli, S., Garibotto, V., Van De Giessen, E., Arbizu, J., Chételat, G., Drezgza, A., Hesse, S., Lammertsma, A. A., Law, I., et al. (2015) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-015-3098-2 · Cited by 61 · see citing works
Smailagic, N., Lafortune, L., Kelly, S., Hyde, C., & Brayne, C. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-171125 · Cited by 61 · see citing works
Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score
Yee, E., Popuri, K., Beg, M. F., & Initiative, T. A. D. N. (2019) · Human Brain Mapping · doi:10.1002/hbm.24783 · Cited by 60 · see citing works
Liu, Y., Yue, L., Xiao, S., Yang, W., Shen, D. G., & Liu, M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102266 · Cited by 59 · see citing works
An image‐based model of brain volume biomarker changes in Huntington's disease
Wijeratne, P. A., Young, A. L., Oxtoby, N. P., Marinescu, R. V., Firth, N. C., Johnson, E. B., Mohan, A., Sampaio, C., Scahill, R. I., Tabrizi, S. J., et al. (2018) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.558 · Cited by 58 · see citing works
Nozadi, S. H., Kadoury, S., & Initiative, T. A. D. N. (2018) · International Journal of Biomedical Imaging · doi:10.1155/2018/1247430 · Cited by 57 · see citing works
Predicting Progression of Alzheimer’s Disease Using Ordinal Regression
Doyle, O., Westman, E., Marquand, A. F., Mecocci, P., Vellas, B. J., Tsolaki, M., Kłoszewska, I., Soininen, H. S., Lovestone, S., Williams, S., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105542 · Cited by 56 · see citing works
Lista, S., Molinuevo, J. L., Cavedo, E., Rami, L., Amouyel, P., Teipel, S. J., Garaci, F., Toschi, N., Habert, M.-O., Blennow, K., et al. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-150202 · Cited by 53 · see citing works
Mofrad, S. A., Lundervold, A., & Lundervold, A. S. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101910 · Cited by 50 · see citing works
Beheshti, I., Initiative, F. T. J.-A. D. N., Maikusa, N., Daneshmand, M., Matsuda, H., Demirel, H., & Anbarjafari, G. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161080 · Cited by 49 · see citing works
Shen, T., Jiang, J., Lu, J., Wang, M., Zuo, C., Yu, Z., & Yan, Z. (2019) · Molecular Imaging · doi:10.1177/1536012119877285 · Cited by 46 · see citing works
Feature Selection and Transfer Learning for Alzheimer’s Disease Clinical Diagnosis
Zhou, K., He, W., Xu, Y., Xiong, G., & Cai, J. (2018) · Applied Sciences · doi:10.3390/app8081372 · Cited by 47 · see citing works
Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks
Sherif, F. F., Zayed, N. M., & Fakhr, M. A. (2015) · Advances in Bioinformatics · doi:10.1155/2015/639367 · Cited by 46 · see citing works
Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer’s Disease
Oxtoby, N. P., Garbarino, S., Firth, N. C., Warren, J. D., Schott, J. M., Alexander, D. C., & Initiative, A. D. N. (2017) · Frontiers in Neurology · doi:10.3389/fneur.2017.00580 · Cited by 46 · see citing works
Gupta, Y., Kim, J., Kim, B. C., & Kwon, G. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00238 · Cited by 45 · see citing works
- Authors
- Young, J., Modat, M., Cardoso, M. J., Mendelson, A., Cash, D., & Ourselin, S.
- Affiliations
- University College London (GB)
- Corresponding author
- Young, Jonathan
- Venue
- NeuroImage Clinical
- Publisher
- Elsevier BV
- Published
- 2013
- Volume
- 2
- Pages
- 735-745
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC3777690
- OpenAlex
- W1978763244
- Cited by
- 249
- References
- 64
- Citations by year
- 2026: 12 · 2025: 10 · 2024: 11 · 2023: 11 · 2022: 19 · 2021: 24 · 2020: 25 · 2019: 28 · 2018: 32 · 2017: 22 · 2016: 15 · 2015: 31 · 2014: 7 · 2013: 1
- FWCI
- 12.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/H046410/1)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, mild cognitive impairment, conversion prediction, Gaussian process classification, probabilistic forecasting, multimodal data integration, mixed kernel, volumetric MRI, FDG-PET, cerebrospinal fluid biomarkers, APOE genotype, support vector machine comparison, ADNI
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