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2110 Total Publications

Amyloid-β imaging with Pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods.
Landau, S. M., Breault, C., Joshi, A. D., Pontecorvo, M., Mathis, C. a, Jagust, W. J., & Mintun, M. a.
2013; Journal Journal of Nuclear Medicine; vol. 54; no. 1; pp. 70-7; doi:10.2967/jnumed.112.109009
Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease.
Lambert, J. C., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., … Amouyel, P.
2013; Journal Nature Genetics; vol. 45; no. 12; pp. 1452-8; doi:10.1038/ng.2802
Man versus machine: comparison of radiologists’ interpretations and NeuroQuant(R) volumetric analyses of brain MRIs in patients with traumatic brain injury
Ross, D. E., Ochs, a L., Seabaugh, J. M., Shrader, C. R., & Alzheimer’s Disease Neuroimaging, I.
2013; Journal Journal of Neuropsychiatry and Clinical Neuroscience; vol. 25; no. 1; pp. 32-39; doi:10.1176/appi.neuropsych.11120377
Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization.
Ortiz, A., G??rriz, J. M., Ram??rez, J., & Salas-Gonz??lez, D.
2013; Journal Neurocomputing; vol. 114; pp. 118-131; doi:10.1016/j.neucom.2012.08.047
Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning.
Eskildsen, S. F., Coupé, P., García-Lorenzo, D., Fonov, V., Pruessner, J. C., & Collins, D. L.
2013; Journal Neuroimage; vol. 65; pp. 511-521; doi:10.1016/j.neuroimage.2012.09.058
Bi-level multi-source learning for heterogeneous block-wise missing data.
Xiang, S., Yuan, L., Fan, W., Wang, Y., Thompson, P. M., & Ye, J.
2013; Journal Neuroimage; vol. 102; pp. 192-206; doi:10.1016/j.neuroimage.2013.08.015
Machine learning-based method for personalized and cost-effective detection of Alzheimer’s disease.
Escudero, J., Ifeachor, E., Zajicek, J. P., Green, C., Shearer, J., & Pearson, S.
2013; Journal IEEE Transactions on Bio-Medical Engineering; vol. 60; no. 1; pp. 164-8; doi:10.1109/TBME.2012.2212278
Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer’s disease. Int J Data Min Bioinform
Ayhan MS, Benton RG, Raghavan V V, Choubey S.
2013; Journal International Journal of Data Mining and Bioinformatics; vol. 7; no. 2; pp. 146-65;
Spatial and anatomical regularization of SVM: A general framework for neuroimaging data.
Cuingnet, R., Glaunés, J. A., Chupin, M., Benali, H., & Colliot, O.
2013; Journal IEEE Transactions on Pattern Analysis and Machine Intelligence; vol. 35; no. 3; pp. 682-696; doi:10.1109/TPAMI.2012.142
The receiver operational characteristic for binary classification with multiple indices and its application to the neuroimaging study of Alzheimer’s disease.
Wu, X., Li, J., Ayutyanont, N., Protas, H., Jagust, W., Fleisher, A., … Chen, K.
2013; Journal IEEE/ACM Transactions on Computational Biology and Bioinformatics; vol. 10; no. 1; pp. 173-80; doi:10.1109/TCBB.2012.141