11 papers
Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
Farica Zhuang, Shu Yang, Dinara Aliyeva +6
Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data…
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
Sophie Kearney, Shu Yang, Zixuan Wen +8
Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to…
Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs
Sophie Kearney, Shu Yang, Zixuan Wen +6
Early and accurate diagnosis of Alzheimer's disease (AD), a complex neurodegenerative disorder, requires analysis of heterogeneous biomarkers (e.g., neuroimaging, genetic risk fact…
Alpha-Z divergence unveils further distinct phenotypic traits of human brain connectivity fingerprint
Md Kaosar Uddin, Nghi Nguyen, Huajun Huang +2
The accurate identification of individuals from functional connectomes (FCs) is critical for advancing individualized assessments in neuropsychiatric research. Traditional methods,…
Fair CCA for Fair Representation Learning: An ADNI Study
Bojian Hou, Zhanliang Wang, Zhuoping Zhou +6
Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes cru…
Reconstructing Brain Causal Dynamics for Subject and Task Fingerprints using fMRI Time-series Data
Dachuan Song, Li Shen, Duy Duong-Tran +1
Purpose: Recently, there has been a revived interest in system neuroscience causation models, driven by their unique capability to unravel complex relationships in multi-scale brai…