3 citations · 5 across the 11 of their papers we have counts for
12 papers
XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction
Yang Qiao, Junjie Wu, Deqiang Qiu +2
Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reproducible or neurobiologically…
Laplace-Mixture Dipole Inversion for Quantitative Susceptibility Mapping
Shuai Huang, James J. Lah, Jason W. Allen +1
Purpose: To develop an automatic dipole inversion method for quantitative susceptibility mapping (QSM) that preserves fine anatomical structures without the need for manual regular…
Non-linear associations of amyloid- with resting-state functional networks and their cognitive relevance in a large community-based cohort of cognitively normal older adults
Junjie Wu, Benjamin B Risk, Taylor A James +6
Background: Non-linear alterations in brain network connectivity may represent early neural signatures of Alzheimer's disease (AD) pathology in cognitively normal older adults. Und…
Atlas-free Brain Network Transformer
Shuai Huang, Xuan Kan, James J. Lah +1
Current atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce…
DeepJIVE: Learning Joint and Individual Variation Explained from Multimodal Data Using Deep Learning
Matthew Drexler, Benjamin Risk, James J Lah +2
Conventional multimodal data integration methods provide a comprehensive assessment of the shared or unique structure within each individual data type but suffer from several limit…
Agentic AI for Scaling Diagnosis and Care in Neurodegenerative Disease
Andrew G. Breithaupt, Michael Weiner, Alice Tang +14
United States healthcare systems are struggling to meet the growing demand for neurological care, particularly in Alzheimer's disease and related dementias (ADRD). Generative AI bu…
A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals
Yuxiang Wei, Anees Abrol, James Lah +2
Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, emphasizing the importance of early detection for proper treatment. Functional magnetic resonanc…
Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns
Shuai Huang, James J. Lah, Jason W. Allen +1
Purpose: To achieve automatic hyperparameter estimation for the joint recovery of quantitative MR images, we propose a Bayesian formulation of the reconstruction problem that incor…
Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits
Ganzhong Tian, John Hanfelt, James Lah +1
There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies. Unsupervised learning can provide insight into the pathophysiology of AD. A mixture…
Robust Quantitative Susceptibility Mapping via Approximate Message Passing with Parameter Estimation
Shuai Huang, James J. Lah, Jason W. Allen +1
Purpose: For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion…
A Probabilistic Bayesian Approach to Recover map and Phase Images for Quantitative Susceptibility Mapping
Shuai Huang, James J. Lah, Jason W. Allen +1
Purpose: Undersampling is used to reduce the scan time for high-resolution 3D magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning…
Fast Nonconvex Mapping Using ADMM
Shuai Huang, James J. Lah, Jason W. Allen +1
Magnetic resonance (MR)- mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise map…