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Publications

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4453 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

Putting the Alzheimer’s cognitive test to the test II: Rasch Measurement Theory.
Hobart, J., Cano, S., Posner, H., Selnes, O., Stern, Y., Thomas, R., & Zajicek, J.
2013; Journal Alzheimer's and Dementia; vol. 9; no. 1; pp. S10-S20; doi:10.1016/j.jalz.2012.08.006

Frontolimbic atrophy is associated with agitation and aggression in mild cognitive impairment and Alzheimer’s disease.
Trzepacz, P. T., Yu, P., Bhamidipati, P. K., Willis, B., Forrester, T., Tabas, L., … Saykin, A. J.
2013; Journal Alzheimer's and Dementia; vol. 9; pp. 95-104; doi:10.1016/j.jalz.2012.10.005

The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI).
Risacher, S. L., Kim, S., Shen, L., Nho, K., Foroud, T., Green, R. C., … Saykin, A. J.
2013; Journal Frontiers in Aging Neuroscience; vol. 5; pp. 11; doi:10.3389/fnagi.2013.00011

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

Small-World Properties in Mild Cognitive Impairment and Early Alzheimer’s Disease: A Cortical Thickness MRI Study.
Zhou, Y., & Lui, Y. W.
2013; Journal ISRN Geriatrics; vol. 2013; no. 6684; pp. 1-11; doi:10.1155/2013/542080

Genetic susceptibility for Alzheimer disease neuritic plaque pathology.
Shulman, J. M., Chen, K., Keenan, B. T., Chibnik, L. B., Fleisher, A., Thiyyagura, P., … Bennett, D. a.
2013; Journal JAMA Neurology; vol. 70; no. 9; pp. 1150-7; doi:10.1001/jamaneurol.2013.2815

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;