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3740 Total Publications

Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer's disease in the Japanese Alzheimer's Disease Neuroimaging Initiative study
Yagi, T, Kanekiyo, M, Ito, J, Ihara, R, Suzuki, K, Iwata, A, Iwatsubo, T and Aoshima, K
2019; Journal Alzheimer's and Dementia: Translational Research and Clinical Interventions; vol. 5; pp. 364-373;
Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia
Bartel, FV, M.: de Ruiter, M.: Belderbos, J.: Barkhof, F.: Vrenken, H.: de Munck, J. C.: van Herk, M.
2019; Journal Neuroimage Clin; vol. 23; pp. 101902;
Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study
van Maurik, IS, Vos, SJ, Bos, I, Bouwman, FH, Teunissen, CE, Scheltens, P, Barkhof, F, Frolich, L, Kornhuber, J, Wiltfang, J, Maier, W, Peters, O, Ruther, E, Nobili, F, Frisoni, GB, Spiru, L, Freund-Levi, Y, Wallin, AK, Hampel, H, Soininen, H, Tsolaki, M, Verhey, F, Kloszewska, I, Mecocci, P, Vellas, B, Lovestone, S, Galluzzi, S, Herukka, SK, Santana, I, Baldeiras, I, de Mendonca, A, Silva, D, Chetelat, G, Egret, S, Palmqvist, S, Hansson, O, Visser, PJ, Berkhof, J and van der Flier, WM
2019; Journal Lancet Neurol; vol. 18; no. 11; pp. 1034-1044;
Normative brain volumetry derived from different reference populations: impact on single-subject diagnostic assessment in dementia
Vinke, EJ, Huizinga, W, Bergtholdt, M, Adams, HH, Steketee, RME, Papma, JM, de Jong, FJ, Niessen, WJ, Ikram, MA, Wenzel, F, Vernooij, MW and Alzheimer's Dis Neuroimaging, I
2019; Journal Neurobiology of Aging; vol. 84; pp. 16-Sep;
Optimal population screening policies for Alzheimer’s disease*
Önen, Z, Sayin, S and Gürvit, IH
2019; Journal IISE Transactions on Healthcare Systems Engineering; vol. 9; no. 1; pp. 14-25;
Prediction of fast decline in amyloid positive mild cognitive impairment patients using multimodal biomarkers
Jang, H, Park, J, Woo, S, Kim, S, Kim, HJ, Na, DL, Lockhart, SN, Kim, Y, Kim, KW, Cho, SH, Kim, SJ, Seong, JK and Seo, SW
2019; Journal Neuroimage Clin; vol. 24; pp. 101941;
An interpretable machine learning model for diagnosis of Alzheimer's disease
Das, D., Ito, J., Kadowaki, T. and Tsuda, K.
2019; Journal PeerJ; vol. 7; pp. e6543;
EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease
Gaubert, S, Raimondo, F, Houot, M, Corsi, MC, Naccache, L, Diego Sitt, J, Hermann, B, Oudiette, D, Gagliardi, G, Habert, MO, Dubois, B, De Vico Fallani, F, Bakardjian, H and Epelbaum, S
2019; Journal Brain; vol. 142; no. 7; pp. 2096-2112;
Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling
Ghazi, MM, Nielsen, M, Pai, A, Cardoso, MJ, Modat, M, Ourselin, S, Sorensen, L and Alzheimers Dis Neuroimaging, I
2019; Journal Medical Image Analysis; vol. 53; pp. 39-46;
A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures
Grassi, MR, N.: Caldirola, D.: Loewenstein, D.: Schruers, K.: Perna, G.: Dumontier, M.
2019; Journal Front Neurol; vol. 10; pp. 756;