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1739 Total Publications

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
Longitudinal progression of cognitive decline correlates with changes in the spatial pattern of brain 18F-FDG PET.
Shokouhi, S., Claassen, D., Kang, H., Ding, Z., Rogers, B., Mishra, A., & Riddle, W. R.
2013; Journal Journal of Nuclear Medicine; vol. 54; no. 9; pp. 1564-9; doi:10.2967/jnumed.112.116137
Network-Guided Sparse Learning for Predicting Cognitive Outcomes from MRI Measures.
Yan, J., Huang, H., Risacher, S. L., Kim, S., Inlow, M., Moore, J. H., … Shen, L.
2013; Journal Multimodal Brain Image Analysis; vol. 8159; pp. 202-210; doi:10.1007/978-3-319-02126-3_20
LCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm.
Lorenzi, M., Ayache, N., Frisoni, G. B., & Pennec, X.
2013; Journal Neuroimage; vol. 81; pp. 470-483; doi:10.1016/j.neuroimage.2013.04.114
Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging.
Nir, T. M., Jahanshad, N., Villalon-Reina, J. E., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, P. M.
2013; Journal Neuroimage: Clinical; vol. 3; pp. 180-95; doi:10.1016/j.nicl.2013.07.006