Although brain imaging has provided crucial insights into Alzheimer’s disease (AD) progression, its limitations in capturing molecular changes have motivated the need to integrate and interpret transcriptomic data, which can reveal early-stage molecular disruptions. By interpreting both structural and gene expression patterns, there is potential to gain a more comprehensive understanding of the disease and identify interactions that drive AD pathogenesis. This study introduces a novel deep learning framework that integrates structural magnetic resonance imaging (sMRI) and gene expression (GE) data for AD prediction. The model extracts features from both data modalities and employs a novel approach to quantify cross-modal interactions. Our multimodal architecture achieves 83.6% accuracy in AD prediction, demonstrating its effectiveness in combining imaging and transcriptomic data. We present a technique for interpreting interactions between specific brain regions and gene expression patterns, providing insights into their joint contribution to AD risk. This approach enables the identification of both established and potential new AD biomarkers, spanning structural brain changes and transcriptomic alterations. By offering a deeper understanding of AD’s molecular and structural aspects, our work advances the field of multimodal biomarker discovery in neurodegenerative diseases and paves the way for more comprehensive early diagnosis strategies.

Heterogeneity is present in Alzheimer’s disease (AD), making it challenging to study. To address this, we propose a graph neural network (GNN) approach to identify disease subtypes from magnetic resonance imaging (MRI) and functional MRI (fMRI) scans. Subtypes are identified by encoding the patients’ scans in brain graphs (via cortical similarity networks) and clustering the representations learnt by the GNN. These subtyping information are used to construct population graphs for an ensemble of local networks, each producing intermediate predictions that are subsequently combined to produce the model’s final decision. Using MRI and fMRI scans from two datasets on AD, we demonstrate that our proposed architecture outperforms existing methods. Three subtypes of AD were identified and left cuneus was found to be a consistent class-wide biomarker. Subtype-specific biomarkers produced by our method further revealed deeper insights, including a unique subtype with significant degeneration in the left isthmus cingulate cortex.

Predicting fluid cognition via neuroimaging data is essential for understanding the neural mechanisms underlying various complex cognitions in the human brain. Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for fluid cognition. In addition, interactions between SC and FC within distributed association regions are related to improvements in fluid cognition. However, existing learning-based methods that leverage both modality-specific embeddings and high-order interactions between the two modalities for prediction are scarce. To tackle these challenges, this study proposes an end-to-end brain structure-function interaction network that incorporates both modality-specific embeddings and structure-function interactions to predict fluid cognition. In this model, we generate embeddings from both FC and SC separately using a graph convolution encoder-decoder module. Subsequently, we learn the interactive weights between corresponding regions of FC and SC, reflecting the coupling strength, by employing an interactive module on the embeddings of both modalities. A novel graph structure - utilizing modality-specific embeddings and interactive weights - is constructed and used for the final prediction. Experimental results demonstrate that our proposed method outperforms other state-of-the-art methods employed on uni-modal and multi-modal brain features. We further identify that strong structure-function coupling in the inferior frontal, postcentral, superior temporal and cingulate cortices are associated with fluid intelligence.