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3393 Total Publications

Multi-modal deep learning model for auxiliary diagnosis of Alzheimer's disease
Zhang, F, Li, Z, Zhang, B, Du, H, Wang, B and Zhang, X
2019; Journal Neurocomputing; vol. 361; pp. 185-195;
Multi-atlas label fusion with random local binary pattern features: Application to hippocampus segmentation
Zhu, H, Tang, Z, Cheng, H, Wu, Y and Fan, Y
2019; Journal Sci Rep; vol. 9; no. 1; pp. 16839;
A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia
Tam, A, Dansereau, C, Iturria-Medina, Y, Urchs, S, Orban, P, Sharmarke, H, Breitner, J, Bellec, P and Alzheimer's Dis, N
2019; Journal Gigascience; vol. 8; no. 5; pp. 16;
Cortisol, Amyloid-beta, and Reserve Predicts Alzheimer's Disease Progression for Cognitively Normal Older Adults
Udeh-Momoh, CTS, B.: Evans, S.: Zheng, B.: Sindi, S.: Tzoulaki, I.: Perneczky, R.: Middleton, L. T.
2019; Journal J Alzheimers Dis; vol. 70; no. 2; pp. 553-562;
Cognitive reserve and clinical progression in Alzheimer disease: A paradoxical relationship
van Loenhoud, ACvdF, W. M.: Wink, A. M.: Dicks, E.: Groot, C.: Twisk, J.: Barkhof, F.: Scheltens, P.: Ossenkoppele, R.
2019; Journal Neurology; vol. 93; no. 4; pp. e334-e346;
Added value of amyloid PET in individualized risk predictions for MCI patients
van Maurik, IS, van der Kall, LM, de Wilde, A, Bouwman, FH, Scheltens, P, van Berckel, BNM, Berkhof, J and van der Flier, WM
2019; Journal Alzheimers Dement (Amst); vol. 11; pp. 529-537;
Personalized risk for clinical progression in cognitively normal subjects-the ABIDE project
van Maurik, ISS, R. E. R.: Verfaillie, S. C. J.: Zwan, M. D.: Bouwman, F. H.: Prins, N. D.: Teunissen, C. E.: Scheltens, P.: Barkhof, F.: Wattjes, M. P.: Molinuevo, J. L.: Rami, L.: Wolfsgruber, S.: Peters, O.: Jessen, F.: Berkhof, J.: van der Flier, W. M.
2019; Journal Alzheimers Res Ther; vol. 11; no. 1; pp. 33;
Covariance statistics and network analysis of brain PET imaging studies
Veronese, M, Moro, L, Arcolin, M, Dipasquale, O, Rizzo, G, Expert, P, Khan, W, Fisher, PM, Svarer, C, Bertoldo, A, Howes, O and Turkheimer, FE
2019; Journal Scientific Reports; vol. 9; pp. 15;
Targeted genetic analysis of cerebral blood flow imaging phenotypes implicates the INPP5D gene
Yao, XH, Risacher, SL, Nho, K, Saykin, AJ, Wang, Z, Shen, L and Alzheimers Dis Neuroimaging, I
2019; Journal Neurobiology of Aging; vol. 81; pp. 213-221;