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Publications

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3740 Total Publications

Multivariate Analysis of Structural MRI and PET (FDG and 18F- AV-45) for Alzheimer’s Disease and Its Prodromal Stages
Zhou Q, Goryawala M, Cabrerizo M, Barker W, Loewenstein D, Duara R, Adjouadi M
PMID: 25570142 ; 2014; Journal Conf Proc IEEE Eng Med Biol Soc; vol. 2014; pp. 1051-4; doi:10.1109/EMBC.2014.6943774

Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes.
Xu, Z., Shen, X., & Pan, W.
2014; Journal PloS One; vol. 9; no. 8; pp. e102312; doi:10.1371/journal.pone.0102312

Identifying cost-effective predictive rules of amyloid-β level by integrating neuropsychological tests and plasma-based markers. J
Haghighi, M., Smith, A., Morgan, D., Small, B., & Huang, S.
2014; Journal Journal of Alzheimer's Disease; vol. 43; no. 4; pp. 1261-1270; doi:10.3233/JAD-140705

Genetic variation modifies risk for neurodegeneration based on biomarker status.
Hohman, T. J., Koran, M. E. I., & Thornton-Wells, T. A
2014; Journal Frontiers in Aging Neuroscience; vol. 6; pp. 183; doi:10.3389/fnagi.2014.00183

A Bayesian Nonparametric Regression Model With Normalized Weights: A Study of Hippocampal Atrophy in Alzheimer’s Disease.
Antoniano-Villalobos, I., Wade, S., & Walker, S. G.
2014; Journal Journal of the American Statistical Association; vol. 109; no. 506; pp. 477-490; doi:10.1080/01621459.2013.879061

Cholinergic basal forebrain atrophy predicts amyloid burden in Alzheimer’s disease
Stefan Teipel, Helmut Heinsen, Edson Amaro, Jr., Lea T. Grinberg, Bernd Krause, and Michel Grothe, for the Alzheimer’s Disease Neuroimaging Initiative.
PMID: 24176625 ; PMCID:4120959 ; 2014; Journal Neurobiology of Aging; vol. 35; no. 3; pp. 482-491; doi:10.1016/j.neurobiolaging.2013.09.029

Multiple instance learning for classification of dementia in brain MRI
Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J. V, & Rueckert, D.
PMID: 24858570 ; 2014; Journal Medical Image Analysis; vol. 18; no. 5; pp. 808-818; doi:10.1016/j.media.2014.04.006

Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer’s dementia
Trzepacz, P. T., Yu, P., Sun, J., Schuh, K., Case, M., Witte, M. M., … Hake, A.
PMID: 23954175 ; 2014; Journal Neurobiology of Aging; vol. 35; no. 1; pp. 143-151; doi:10.1016/j.neurobiolaging.2013.06.018

Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers Disease Progression via Convex Fused Sparse Group Lasso
Tsao S1, Gajawelli N2, Zhou J3, Shi J3, Ye J3, Wang Y3, Lepore N2.
PMID: 25076826 ; PMCID:4112760 ; 2014; Journal Proc SPIE Int Soc Opt Eng; pp. 9034; doi:10.1117/12.2042720

Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer’s Disease
Li, R., Perneczky, R., Yakushev, I., Förster, S., Kurz, A., Drzezga, A., & Kramer, S.
PMID: 25919662 ; PMCID:4412726 ; 2014; Journal PloS One; vol. 10; no. 4; pp. e0122731; doi:10.1371/journal.pone.0122731