3911 Total Publications
Dynamics of brain structure and cognitive function in the Alzheimer’s disease neuroimaging initiative.
Song, X., Mitnitski, A., Zhang, N., Chen, W., & Rockwood, K.
2013; Journal Journal of Neurology, Neurosurgery, and Psychiatry; vol. 84; no. 1; pp. 71-8;
doi:10.1136/jnnp-2012-303579
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
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