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1739 Total Publications

Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Pipitone J1, Park MT2, Winterburn J2, Lett TA3, Lerch JP4, Pruessner JC5, Lepage M6, Voineskos AN7, Chakravarty MM8; Alzheimer's Disease Neuroimaging Initiative.
PMID: 20042704 ; PMCID:2809036 ; 2014; Journal Neurology; vol. 74; no. 3; pp. 201-9;
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;
2014; Journal Current Alzheimer's Research; vol. 11; no. 3; pp. 239-52;
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.
2014; Journal Neuroimage: Clinical; vol. 4; pp. 164-173; doi:10.1016/j.nicl.2013.11.010
Ontology driven decision support for the diagnosis of mild cognitive impairment
Zhang, X., Hu, B., Ma, X., Moore, P., & Chen, J.
PMID: 24468160 ; 2014; Journal Computer Methods and Programs in Biomedicine; vol. 113; no. 3; pp. 781-791; doi:10.1016/j.cmpb.2013.12.023
Multivariate Analysis of Structural MRI and PET (FDG and 18F- AV-45) for Alzheimer’s Disease and Its Prodromal Stages
Zhou Q, Goryawala M, Cabrerizo M, Barker W, Loewenstein D, Duara R, Adjouadi M
PMID: 25570142 ; 2014; Journal Conf Proc IEEE Eng Med Biol Soc; vol. 2014; pp. 1051-4; doi:10.1109/EMBC.2014.6943774
Identifying cost-effective predictive rules of amyloid-β level by integrating neuropsychological tests and plasma-based markers. J
Haghighi, M., Smith, A., Morgan, D., Small, B., & Huang, S.
2014; Journal Journal of Alzheimer's Disease; vol. 43; no. 4; pp. 1261-1270; doi:10.3233/JAD-140705
Genetic variation modifies risk for neurodegeneration based on biomarker status.
Hohman, T. J., Koran, M. E. I., & Thornton-Wells, T. A
2014; Journal Frontiers in Aging Neuroscience; vol. 6; pp. 183; doi:10.3389/fnagi.2014.00183