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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
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