The field of neurodegenerative disease diagnosis is moving towards the development of innovative multimodal data integration methods. These methods aim to combine data from various sources, such as imaging, genetics, and clinical information, to improve diagnostic accuracy and robustness. Recent advancements have focused on leveraging deep learning techniques, graph-based approaches, and domain adaptation methods to address the challenges of high-dimensional data, nonlinear structures, and domain shifts. Noteworthy papers in this area include: DeepJIVE, which introduces a deep-learning approach to performing Joint and Individual Variance Explained (JIVE) for multimodal data analysis. Meta Fusion, a unified framework for multimodality fusion with mutual learning, which constructs a cohort of models based on various combinations of latent representations across modalities. OmniBrain, a multimodal framework that integrates brain MRI, radiomics, gene expression, and clinical data using a unified model with cross-attention and modality dropout. GDAIP, a graph-based domain adaptive framework for individual brain parcellation, which integrates Graph Attention Networks (GAT) with Minimax Entropy (MME)-based domain adaptation. Collaborative Domain Adaptation (CDA) framework for late-life depression assessment, which leverages a Vision Transformer (ViT) to capture global anatomical context and a Convolutional Neural Network (CNN) to extract local structural features. TAP-GPT, a novel framework that adapts TableGPT2 for Alzheimer's disease diagnosis using structured biomarker data with small sample sizes.