Alzheimer’s Disease Neuroimaging Initiative

4453 Total Publications

Year	Title	Author	Journal
2015	Cognitive Impairment Precedes and Predicts Functional Impairment in Mild Alzheimer's Disease	Liu-Seifert, H, Siemers, E, Price, K, Han, B, Selzler, KJ, Henley, D, Sundell, K, Aisen, P, Cummings, J, Raskin, J and Mohs, R	Journal J Alzheimers Dis;
2015	Comparison of Automated Brain Volume Measures obtained with NeuroQuant and FreeSurfer	Ochs, AL, Ross, DE, Zannoni, MD, Abildskov, TJ and Bigler, ED	Journal J Neuroimaging;
2015	Measuring Global Brain Atrophy with the Brain Volume/Cerebrospinal Fluid Index: Normative Values, Cut-Offs and Clinical Associations	Orellana, C;D. Ferreira, J. S. Muehlboeck, P. Mecocci, B. Vellas, M. Tsolaki, I. Kloszewska, H. Soininen, S. Lovestone, A. Simmons, L. O. Wahlund and E. Westman	Journal Neurodegener Dis;
2015	GWAS of longitudinal amyloid accumulation on 18F-florbetapir PET in Alzheimer's disease implicates microglial activation gene IL1RAP	Ramanan, V. K., Risacher, S. L., Nho, K., Kim, S., Shen, L., McDonald, B. C., ‚Ä¶ Saykin, A. J.	Journal Brain;
2015	Multimodal Imaging of Brain Connectivity Using the MIBCA Toolbox: Preliminary Application to Alzheimer's Disease	Ribeiro, AS, Lacerda, LM, da Silva, NA, Ferreira, HA and Alzheimer's Dis, N	Journal Ieee Transactions on Nuclear Science;
2015	Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers	Ritter, K, Schumacher, J, Weygandt, M, Buchert, R, Allefeld, C and Haynes, JD	Journal Alzheimers Dement (Amst);
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;