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3740 Total Publications

Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease
Zhan L, Zhou J, Wang Y, Jin Y, Jahanshad N, Prasad G, Nir TM, Leonardo CD, Ye J, Thompson PM, For The Alzheimer's Disease Neuroimaging Initiative.
PMID: 25926791 ; PMCID:4396191 ; 2015; Journal Frontiers in Aging Neuroscience; vol. 7; pp. 48;
Deep sparse multi-task learning for feature selection in Alzheimer’s disease diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D.
PMID: 25993900 ; PMCID:4714963 ; 2015; Journal Brain Structure and Function; vol. 221; no. 5; pp. 2569-87; doi:10.1007/s00429-015-1059-y
An Integrated Bioinformatics Approach for Identifying Genetic Markers that Predict Cerebrospinal Fluid Biomarker p-tau181/A􏰀1-42 Ratio in ApoE4-Negative Mild Cognitive Impairment Patients
Sun, Y., Bresell, A., Rantalainen, M., Hoglund, K., Lebouvier, T., & Salter, H.
PMID: 25720397 ; 2015; Journal Journal of Alzheimer's Disease; vol. 45; no. 4; pp. 1061-1076; doi:10.3233/JAD-142118
Functional Activities Questionnaire items that best discriminate and predict progression from clinically normal to mild cognitive impairment
Marshall GA, Zoller AS, Lorius N, Amariglio RE, Locascio JJ, Johnson KA, Sperling RA, Rentz DM, and for the Alzheimer’s Disease Neuroimaging Initiative.
PMID: 26017560 ; PMCID:4111766 ; 2015; Journal Current Alzheimer's Research; vol. 12; no. 5; pp. 493-502; doi:10.3233/JAD-132768
F-FDG PET for the early diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI)
Smailagic, N., Vacante, M., Hyde, C., Martin, S., Ukoumunne, O., & Sachpekidis, C.
PMID: 25629415 ; 2015; Journal The Cochrane Database of Systematic Reviews; vol. 1; pp. CD010632; doi:10.1002/14651858.CD010632.pub2
Connectivity analysis of normal and mild cognitive impairment patients based on FDG and PiB-PET images
Son, S.-J., Kim, J.-H., Seo, J., Lee, J.-M., & Park, H.
PMID: 25896866 ; 2015; Journal Neuroscience Research; vol. 98; pp. 50-8; doi:10.1016/j.neures.2015.04.002
Combined Genome-Wide CSF Aβ-42's Associations and Simple Network Properties Highlight New Risk Factors for Alzheimer's Disease.
M. B. R. Souza, G. Y. Araújo, I. G. Costa, J. R. M. Oliveira and A. s. D. N. Initiative
2015; Journal Journal of Molecular Neuroscience; vol. 58; no. 1; pp. 120-128; doi:10.1007/s12031-015-0667-6
Regional brain hypometabolism is unrelated to regional amyloid plaque burden
Altmann A, Ng B, Landau SM, Jagust WJ, Greicius MD.
PMID: 26419799 ; PMCID:4806718 ; 2015; Journal Brain; vol. 138; no. 12; pp. 3734-46;
Does MRI scan acceleration affect power to track brain change?
Ching, C. R. K., Hua, X., Hibar, D. P., Ward, C. P., Gunter, J. L., Bernstein, M. a., … Thompson, P. M.
PMID: 26515904 ; 2015; Journal Neurobiology of Aging; vol. 36; pp. S167-S177; doi:10.1016/j.neurobiolaging.2014.05.039
Lifetime History of Depression Predicts Increased Amyloid-β Accumulation in Patients with Mild Cognitive Impairment.
Chung, J. K., Plitman, E., Nakajima, S., Chow, T. W., Chakravarty, M. M., Caravaggio, F., … Graff-Guerrero, A.
PMID: 26756327 ; 2015; Journal Journal of Alzheimer's Disease; vol. 45; no. 3; pp. 907-919; doi:10.3233/JAD-142931