Alzheimer’s Disease Neuroimaging Initiative

4453 Total Publications

Year	Title	Author	Journal
2015	Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism	Yun, HJ, Kwak, K and Lee, JM	Journal PLoS One;
2015	Resting-State Whole-Brain Functional Connectivity Networks for MCI Classification Using L2-Regularized Logistic Regression	Zhang, XW, Hu, B, Ma, X and Xu, LX	Journal Ieee Transactions on Nanobioscience;
2015	Alzheimer‚Äôs Disease Neuroimaging Initiative 2 Clinical Core: Progress and plans.	Aisen, P. S., Petersen, R. C., Donohue, M., & Weiner, M. W.	Journal Alzheimer's and Dementia;
2015	Comparing 3T and 1.5T MRI for mapping hippocampal atrophy in the Alzheimer's Disease Neuroimaging Initiative	Chow, N, Hwang, KS, Hurtz, S, Green, AE, Somme, JH, Thompson, PM, Elashoff, DA, Jack, CR, Weiner, M and Apostolova, LG	Journal AJNR Am J Neuroradiol;
2015	Single time point high-dimensional morphometry in Alzheimer‚Äôs disease: group statistics on longitudinally acquired data.	Duchesne S, Valdivia F, Mouiha A, Robitaille N.	Journal Neurobiology of Aging;
2015	Genetic overlap between Alzheimer‚Äôs disease and Parkinson's disease at the MAPT locus.	Desikan, R. S., Schork, a J., Wang, Y., Witoelar, a, Sharma, M., McEvoy, L. K., ‚Ä¶ Dale, a M.	Journal Molecular Psychiatry;
2014	Alzheimer'sdisease Neuroimaging Initiative1. Amygdalar atrophy in early Alzheimer's disease.	Klein-Koerkamp Y, Heckemann RA, Ramdeen KT, Moreaud O, Keignart S, Krainik A, Hammers A, Baciu M, Hot P;	Journal Current Alzheimer's Research;
2014	Integration and relative value of biomarkers for prediction of MCI to AD progression: Spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers.	Da, X., Toledo, J. B., Zee, J., Wolk, D. a., Xie, S. X., Ou, Y., … Davatzikos, C.	Journal Neuroimage: Clinical;
2014	Genetic variation modifies risk for neurodegeneration based on biomarker status.	Hohman, T. J., Koran, M. E. I., & Thornton-Wells, T. A	Journal Frontiers in Aging Neuroscience;
2014	Longitudinal analysis is more powerful than cross-sectional analysis in detecting genetic association with neuroimaging phenotypes.	Xu, Z., Shen, X., & Pan, W.	Journal PloS One;