×
  • Select the area you would like to search.
  • ACTIVE INVESTIGATIONS Search for current projects using the investigator's name, institution, or keywords.
  • EXPERTS KNOWLEDGE BASE Enter keywords to search a list of questions and answers received and processed by the ADNI team.
  • ADNI PDFS Search any ADNI publication pdf by author, keyword, or PMID. Use an asterisk only to view all pdfs.
Filter results by:

1739 Total Publications

White Matter Abnormalities and Structural Hippocampal Disconnections in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease.
Rowley, J., Fonov, V., Wu, O., Eskildsen, S. F., Schoemaker, D., Wu, L., … Rosa-Neto, P.
2013; Journal PloS One; vol. 8; no. 9; pp. 1-13; doi:10.1371/journal.pone.0074776
Multi-atlas segmentation without registration: a supervoxel-based approach.
Wang, H., & Yushkevich, P. a.
2013; Journal Medical Image Computing and Computer-Assisted Intervention; vol. 16; no. 3; pp. 535-42; doi:10.1007/978-3-642-40760-4_67
Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease.
Gray, K. R., Aljabar, P., Heckemann, R. a., Hammers, A., & Rueckert, D.
2013; Journal Neuroimage; vol. 65; pp. 167-175; doi:10.1016/j.neuroimage.2012.09.065
Modeling disease progression via multi-task learning.
Zhou, J., Liu, J., Narayan, V. a, & Ye, J.
2013; Journal Neuroimage; vol. 78; pp. 233-48; doi:10.1016/j.neuroimage.2013.03.073
Deformable templates guided discriminative models for robust 3D brain MRI segmentation.
Liu, C.-Y., Iglesias, J. E., & Tu, Z.
2013; Journal Neuroinformatics; vol. 11; no. 4; pp. 447-68; doi:10.1007/s12021-013-9190-5
Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined cerebrospinal fluid, MR imaging, and PET biomarkers.
Shaffer, J. L., Petrella, J. R., Sheldon, F. C., Choudhury, K. R., Calhoun, V. D., Coleman, R. E., & Doraiswamy, P. M.
2013; Journal Radiology; vol. 266; no. 2; pp. 583-591; doi:10.1148/radiol.12120010
Longitudinal change in CSF Tau and Aβ biomarkers for up to 48 months in ADNI.
Toledo, J. B., Xie, S. X., Trojanowski, J. Q., & Shaw, L. M.
2013; Journal Acta Neuropathologica; vol. 126; no. 5; pp. 659-70; doi:10.1007/s00401-013-1151-4
Predicting the Location of Human Perirhinal Cortex, Brodmann's area 35, from MRI
Augustinack, J. C., Huber, K. E., Stevens, A. a., Roy, M., Frosch, M. P., van der Kouwe, A. J. W., … Fischl, B.
PMID: 22960087 ; PMCID:3508349 ; 2013; Journal Neuroimage; vol. 64; pp. 32-42; doi:10.1016/j.neuroimage.2012.08.071
Mapping the Genetic Variation of Regional Brain Volumes as Explained by All Common SNPs from the ADNI Study
Bryant, C., Giovanello, K. S., Ibrahim, J. G., Chang, J., Shen, D., Peterson, B. S., & Zhu, H.
PMID: 24015190 ; PMCID:3756017 ; 2013; Journal PloS One; vol. 8; no. 8; pp. e71723; doi:10.1371/journal.pone.0071723
Multifold Bayesian Kernelization in Alzheimer’s Diagnosis
Liu, S., Song, Y., Cai, W., Pujol, S., Kikinis, R., Wang, X., & Feng, D.
PMID: 24579154 ; PMCID:4017205 ; 2013; Journal Medical Image Computing and Computer-Assisted Intervention; vol. 16; no. 2; pp. 303-10; doi:10.1007/978-3-642-40763-5_38