×
  • 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:

1800 Total Publications

Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness
Kim, W. H., Singh, V., Chung, M. K., Hinrichs, C., Pachauri, D., Okonkwo, O. C., & Johnson, S. C.
PMID: 24614060 ; PMCID:4095794 ; 2014; Journal Neuroimage; vol. 93; no. 1; pp. 107-123; AWTYJ1j0wAl_G9zjEvaJ doi:10.1016/j.neuroimage.2014.02.028
Neuropsychological Criteria for Mild Cognitive Impairment Improves Diagnostic Precision, Biomarker Associations, and Progression Rates
Bondi, M. W., Edmonds, E. C., Jak, A. J., Clark, L. R., Delano-Wood, L., McDonald, C. R., … Salmon, D. P.
PMID: 24844687 ; PMCID:4133291 ; 2014; Journal Journal of Alzheimer's Disease; vol. 42; no. 1; pp. 275-89; AWTX7HmYdzjauPDbjhjJ doi:10.3233/JAD-140276
Cholinergic basal forebrain atrophy predicts amyloid burden in Alzheimer’s disease
Stefan Teipel, Helmut Heinsen, Edson Amaro, Jr., Lea T. Grinberg, Bernd Krause, and Michel Grothe, for the Alzheimer’s Disease Neuroimaging Initiative.
PMID: 24176625 ; PMCID:4120959 ; 2014; Journal Neurobiology of Aging; vol. 35; no. 3; pp. 482-491; AWTYmoe0dzjauPDbjhoT doi:10.1016/j.neurobiolaging.2013.09.029
Multiple instance learning for classification of dementia in brain MRI
Tong, T., Wolz, R., Gao, Q., Guerrero, R., Hajnal, J. V, & Rueckert, D.
PMID: 24858570 ; 2014; Journal Medical Image Analysis; vol. 18; no. 5; pp. 808-818; AWTYmoe0dzjauPDbjhom doi:10.1016/j.media.2014.04.006
Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer’s dementia
Trzepacz, P. T., Yu, P., Sun, J., Schuh, K., Case, M., Witte, M. M., … Hake, A.
PMID: 23954175 ; 2014; Journal Neurobiology of Aging; vol. 35; no. 1; pp. 143-151; AWTYmoe0dzjauPDbjhov doi:10.1016/j.neurobiolaging.2013.06.018
Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers Disease Progression via Convex Fused Sparse Group Lasso
Tsao S1, Gajawelli N2, Zhou J3, Shi J3, Ye J3, Wang Y3, Lepore N2.
PMID: 25076826 ; PMCID:4112760 ; 2014; Journal Proc SPIE Int Soc Opt Eng; pp. 9034; AWTYmoe0dzjauPDbjhow doi:10.1117/12.2042720
Hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition
Wu, G., Kim, M., Sanroma, G., Wang, Q., Munsell, B. C., & Shen, D.
PMID: 25463474 ; PMCID:4285661 ; 2014; Journal Neuroimage; vol. 106; pp. 177-188; AWTYolG5wAl_G9zjEvfR doi:10.1016/j.neuroimage.2014.11.025
Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification.
Wee, C.-Y., Yap, P.-T., Zhang, D., Wang, L., & Shen, D.
2014; Journal Brain Structure and Function; vol. 219; no. 2; pp. 641-56; AWUVKtlhdzjauPDbjhra doi:10.1007/s00429-013-0524-8
Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer’s disease: Detecting, quantifying, and predicting.
Tang, X., Holland, D., Dale, A. M., Younes, L., & Miller, M. I.
2014; Journal Human Brain Mapping; vol. 35; no. 8; pp. 3701-3725; AWUVKtlhdzjauPDbjhrc doi:10.1002/hbm.22431
Alleles that increase risk for type 2 diabetes mellitus are not associated with increased risk for Alzheimer’s disease.
Proitsi, P., Lupton, M. K., Velayudhan, L., Hunter, G., Newhouse, S., Lin, K., … Powell, J. F.
2014; Journal Neurobiology of Aging; vol. 35; no. 12; pp. 2883.e3-2883.e10; AWUVKtlhdzjauPDbjhrg doi:10.1016/j.neurobiolaging.2014.07.023