This paper develops and validates an integrated model combining MRI and blood-based biomarkers to predict brain amyloid PET positivity with high accuracy.

Journal of Alzheimer s Disease · Apr 2, 2024Peer-reviewedNeuroscience

Neuroimaging within the Dominantly Inherited Alzheimer's Network (DIAN): PET and MRI

Nicole Sarah McKay, Brian Andrew Gordon, Russ C. Hornbeck, Clifford R. Jack, Robert A. Koeppe, Shaney E. Flores, Sarah Keefe, Diana A. Hobbs, Nelly Joseph‐Mathurin, Qing Wang, Farzaneh Rahmani, Charles D. Chen, Austin A. McCullough, Deborah Koudelis, Jasmin Chua, , Dominantly Inherited Alzheimer Network

The paper describes MRI and PET protocols used in the DIAN study to investigate preclinical Alzheimer's disease and reports key imaging findings.

bioRxiv · Mar 25, 2022 · PreprintNeuroscience

FEDERATED MORPHOMETRY FEATURE SELECTION FOR HIPPOCAMPAL MORPHOMETRY ASSOCIATED BETA-AMYLOID AND TAU PATHOLOGY

Jianfeng Wu, Qunxi Dong, Jie Zhang, Yi Su, Teresa Wu, Richard J. Caselli, Eric M. Reiman, Jieping Ye, Natasha Leporé, Kewei Chen, Paul M. Thompson, Yalin Wang, Alzheimer’s Disease Neuroimaging Initiative

This paper introduces FMFS, a framework for selecting hippocampal morphometry features linked to Aβ/tau pathology, showing it outperforms existing methods and identifies key subregions for biomarker development.

Frontiers in Neuroscience · Nov 25, 2021Peer-reviewedNeuroscience

Predicting Brain Amyloid using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals from the ADNI and OASIS Databases

Jianfeng Wu, Qunxi Dong, Jie Gui, Jie Zhang, Yi Su, Kewei Chen, Paul M. Thompson, Richard J. Caselli, Eric M. Reiman, Jieping Ye, Yalin Wang

This paper develops a method to predict brain amyloid burden using MRI-based morphometry statistics and sparse coding, achieving high accuracy in ADNI and OASIS cohorts.

Frontiers in Neuroscience · Aug 6, 2021Peer-reviewedNeuroscience