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1800 Total Publications

Learning-Based Atlas Selection for Multiple-Atlas Segmentation
Sanroma G, Wu G, Gao Y, Shen D.
PMID: 25485313 ; PMCID:4254783 ; 2014; Journal Conf Comput Vis Pattern Recognit Workshops; vol. 2014; pp. 3111-3117; AWTYl1zNwAl_G9zjEvdk doi:10.1109/CVPR.2014.398
Apolipoprotein E epsilon 4 allele is associated with ventricular expansion rate and surface morphology in dementia and normal aging
Roussotte, F. F., Gutman, B. a, Madsen, S. K., Colby, J. B., Narr, K. L., & Thompson, P. M.
PMID: 24411483 ; PMCID:3961511 ; 2014; Journal Neurobiology of Aging; vol. 35; no. 6; pp. 1309-17; AWTYk_61wAl_G9zjEvdY doi:10.1016/j.neurobiolaging.2013.11.030
Disease progression model for Clinical Dementia Rating-Sum of Boxes in mild cognitive impairment and Alzheimer's subjects from the Alzheimer's Disease Neuroimaging Initiative
Samtani, M. N., Raghavan, N., Novak, G., Nandy, P., & Narayan, V. A.
PMID: 24926196 ; PMCID:4049432 ; 2014; Journal Neuropsychiatric Disease and Treatment; vol. 10; pp. 929-52; AWTYlga6dzjauPDbjhn2 doi:10.2147/NDT.S62323
Visual rating and volumetric measurement of medial temporal atrophy in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort: baseline diagnosis and the prediction of MCI outcome
Varon, D., Barker, W., Loewenstein, D., Greig, M., Bohorquez, A., Santos, I., … Duara, R.
PMID: 24816477 ; 2014; Journal International Journal of Geriatric Psychiatry; pp. 192-200; AWTYm_u8wAl_G9zjEveZ doi:10.1002/gps.4126
Serum cholesterol and variant in cholesterol-related gene CETP predict white matter microstructure
Warstadt NM1, Dennis EL1, Jahanshad N1, Kohannim O2, Nir TM1, McMahon KL3, de Zubicaray GI4, Montgomery GW5, Henders AK5, Martin NG5, Whitfield JB5, Jack CR Jr6, Bernstein MA6, Weiner MW7, Toga AW8, Wright MJ9, Thompson PM10; Alzheimer's Disease Neuroimaging Initiative (ADNI).
PMID: 24997672 ; PMCID:4198330 ; 2014; Journal Neurobiol Aging; vol. 35; no. 11; pp. 2504-2513; AWTYoUyLdzjauPDbjhpB doi:10.1016/j.neurobiolaging.2014.05.024
Genome-wide association study of monoamine metabolite levels in human cerebrospinal fluid
Kauwe JS1, Bailey MH1, Ridge PG1, Perry R1, Wadsworth ME1, Hoyt KL1, Staley LA1, Karch CM2, Harari O3, Cruchaga C2, Ainscough BJ4, Bales K5, Pickering EH5, Bertelsen S3; Alzheimer's Disease Neuroimaging Initiative, Fagan AM6, Holtzman DM7, Morris JC8, Goate AM9
PMID: 25340798 ; PMCID:4207667 ; 2014; Journal PLoS Genet.; vol. 10; no. 10; pp. e1004758; AWTYHKVzdzjauPDbjhl_ doi:10.1371/journal.pgen.1004758
Extension and refinement of the predictive value of different classes of markers in ADNI: four-year follow-up data
Gomar, J. J., Conejero-Goldberg, C., Davies, P., & Goldberg, T. E.
PMID: 24613706 ; PMCID:4416649 ; 2014; Journal Alzheimer's and Dementia; vol. 10; no. 6; pp. 704-12; AWTYHKVzdzjauPDbjhmF doi:10.1016/j.jalz.2013.11.009
Medication for Alzheimer’s Disease and Associated Fall Hazard: A Retrospective Cohort Study from the Alzheimer’s Disease Initiative
Epstein, N. U., Guo, R., Farlow, M. R., Singh, J. P., & Fisher, M.
PMID: 24357133 ; PMCID:5469288 ; 2014; Journal Drugs and Aging; vol. 31; no. 2; pp. 125-9; AWTYFhFidzjauPDbjhku doi:10.1007/s40266-013-0143-3
Manifold population modeling as a neuro-imaging biomarker: Application to ADNI and ADNI-GO
Guerrero, R., Wolz, R., Rao, a W., & Rueckert, D.
PMID: 24657351 ; 2014; Journal Neuroimage; vol. 94; pp. 275-86; AWTYHiL_wAl_G9zjEvYz doi:10.1016/j.neuroimage.2014.03.036
Defining Multivariate Normative Rules for Healthy Aging using Neuroimaging and Machine Learning: An Application to Alzheimer’s Disease
Andrade de Oliveira, A., Carthery-Goulart, M. T., Oliveira Júnior, P. P. D. M., Carrettiero, D. C., Sato, J. R., & Alzheimer’s Disease Neuroimaging Initative.
PMID: 25079801 ; 2014; Journal Journal of Alzheimer's Disease; vol. 43; no. 1; pp. 201-12; AWTX4BB4dzjauPDbjhi6 doi:10.3233/JAD-140189