Blog Archives
How Can We Use Mathematical Modeling of Amyloid-β in Alzheimer’s Disease Research and Clinical Practices?
Chu C, Low YLC, Ma L, Wang Y, Cox T, Doré V, Masters CL, Goudey B, Jin L, Pan Y.
A robust harmonization approach for cognitive data from multiple aging and dementia cohorts.
Joseph Giorgio, Ankeet Tanna, Maura Malpetti, Simon R. White, Jingshen Wang, Suzanne Baker, Susan Landau, Tomotaka Tanaka, Christopher Chen, James B. Rowe, John O’Brien, Jurgen Fripp, Michael Breakspear, William Jagust, Zoe Kourtzi, for the Alzheimer’s Disease Neuroimaging Initiative, Australian Imaging Biomarkers and Lifestyle flagship study
Plasma p217+ tau vs NAV4694 amyloid and MK6240 tau PET across the Alzheimer continuum
Doré V, Doecke JD, Saad ZS, Triana-Baltzer G, Slemmon R, Krishnadas N, Bourgeat P, Huang K, Burnham S, Fowler C, Rainey-Smith SR, Bush A, Ward L, Robertson J, Martins RN, Masters CL, Villemagne VL, Fripp J, Kolb HC, Rowe CC (2022).
Plasma Aβ42/40 ratio, p-tau181, GFAP, and NfL across the Alzheimer’s disease continuum: A cross-sectional and longitudinal study in the AIBL cohort
Chatterjee, P, Pedrini, S, Doecke, JD, et al (2022).
The dawn of robust individualised risk models for dementia
Burnham SC, Loi SM, Doecke JD, Fedyashov V, Dore V, Villemagne VL, Masters CL (2019).
The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease
Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, Lautenschlager NT, Lenzo N, Martins RN, Maruff P, Masters C, Milner A, Pike K, Rowe C, Savage G, Szoeke C, Taddei K, Villemagne V, Woodward M, Ames D, AIBL (2009).
Stay up to date with the latest news from the AIBL study.