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Publications

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1800 Total Publications

Multi-stage Biomarker Models for Progression Estimation in Alzheimer's Disease
Schmidt-Richberg, A., Guerrero, R., Ledig, C., Molina-Abril, H., Frangi, A. F., Rueckert, D., & The Alzheimer’s Disease Neuroimaging Inititative.
PMID: 26221689 ; 2015; Journal Inf Process Med Imaging; vol. 24; pp. 387-398; AWTYlga6dzjauPDbjhoA
Fusing Heterogeneous Data for Alzheimer's Disease Classification
Pillai, P. S., & Leong, T.-Y.
PMID: 26262148 ; 2015; Journal Studies in Health Technology and Informatics; vol. 216; pp. 731-5; AWTYkLG4MOwVf_qbJr7F doi:10.1016/j.neuroimage.2014.04.054
Linking Genetics of Brain Changes to Alzheimer's Disease: Sparse Whole Genome Association Scan of Regional MRI Volumes in the ADNI and AddNeuroMed Cohorts
Khondoker, M., Newhouse, S., Westman, E., Muehlboeck, J.-S., Mecocci, P., Vellas, B., … Simmons, A.
PMID: 25649652 ; 2015; Journal Journal of Alzheimer's Disease; vol. 45; no. 3; pp. 851-64; AWTYJ1j0wAl_G9zjEvaF doi:10.3233/JAD-142214
A Multi-Marker Genetic Association Test Based on the Rasch Model Applied to Alzheimer’s Disease
Wang W, Mandel J, Bouaziz J, Commenges D, Nabirotchkine S, Chumakov I, Cohen D, Guedj M; Alzheimer’s Disease Neuroimaging Initiative.
PMID: 26379234 ; PMCID:4574966 ; 2015; Journal PLoS One; vol. 10; no. 9; pp. e0138223; AWTYnnPpwAl_G9zjEve0 doi:10.1371/journal.pone.0138223
Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease
Zhan L, Zhou J, Wang Y, Jin Y, Jahanshad N, Prasad G, Nir TM, Leonardo CD, Ye J, Thompson PM, For The Alzheimer's Disease Neuroimaging Initiative.
PMID: 25926791 ; PMCID:4396191 ; 2015; Journal Frontiers in Aging Neuroscience; vol. 7; pp. 48; AWTYs7amwAl_G9zjEvfa
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D.
PMID: 25993900 ; PMCID:4714963 ; 2015; Journal Brain Structure and Function; vol. 221; no. 5; pp. 2569-87; AWTYmIlBwAl_G9zjEveO doi:10.1007/s00429-015-1059-y
An Integrated Bioinformatics Approach for Identifying Genetic Markers that Predict Cerebrospinal Fluid Biomarker p-tau181/A􏰀1-42 Ratio in ApoE4-Negative Mild Cognitive Impairment Patients
Sun, Y., Bresell, A., Rantalainen, M., Hoglund, K., Lebouvier, T., & Salter, H.
PMID: 25720397 ; 2015; Journal Journal of Alzheimer's Disease; vol. 45; no. 4; pp. 1061-1076; AWTYmIlBwAl_G9zjEveQ doi:10.3233/JAD-142118
Functional Activities Questionnaire items that best discriminate and predict progression from clinically normal to mild cognitive impairment
Marshall GA, Zoller AS, Lorius N, Amariglio RE, Locascio JJ, Johnson KA, Sperling RA, Rentz DM, and for the Alzheimer’s Disease Neuroimaging Initiative.
PMID: 26017560 ; PMCID:4111766 ; 2015; Journal Current Alzheimer's Research; vol. 12; no. 5; pp. 493-502; AWTYNIDFwAl_G9zjEvbT doi:10.3233/JAD-132768
F-FDG PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)
Smailagic, N., Vacante, M., Hyde, C., Martin, S., Ukoumunne, O., & Sachpekidis, C.
PMID: 25629415 ; 2015; Journal The Cochrane Database of Systematic Reviews; vol. 1; pp. CD010632; AWTYmIlBwAl_G9zjEvd9 doi:10.1002/14651858.CD010632.pub2
Connectivity analysis of normal and mild cognitive impairment patients based on FDG and PiB-PET images
Son, S.-J., Kim, J.-H., Seo, J., Lee, J.-M., & Park, H.
PMID: 25896866 ; 2015; Journal Neuroscience Research; vol. 98; pp. 50-8; AWTYmIlBwAl_G9zjEvd_ doi:10.1016/j.neures.2015.04.002