Amyloid-β Deposition Prediction With Large Language Model Driven and Task-Oriented Learning of Brain Functional Networks

Abstract

Amyloid- $\beta $ positron emission tomography can reflect the Amyloid- $\beta $ protein deposition in the brain and thus serves as one of the golden standards for Alzheimer’s disease (AD) diagnosis. However, its practical cost and high radioactivity hinder its application in large-scale early AD screening. Recent neuroscience studies suggest a strong association between changes in functional connectivity network (FCN) derived from functional MRI (fMRI), and deposition patterns of Amyloid- $\beta $ protein in the brain. This enables an FCN-based approach to assess the Amyloid- $\beta $ protein deposition with less expense and radioactivity. However, an effective FCN-based Amyloid- $\beta $ assessment remains lacking for practice. In this paper, we introduce a novel deep learning framework tailored for this task. Our framework comprises three innovative components: 1) a pre-trained Large Language Model Nodal Embedding Encoder, designed to extract task-related features from fMRI signals; 2) a task-oriented Hierarchical-order FCN Learning module, used to enhance the representation of complex correlations among different brain regions for improved prediction of Amyloid- $\beta $ deposition; and 3) task-feature consistency losses for promoting similarity between predicted and real Amyloid- $\beta $ values and ensuring effectiveness of predicted Amyloid- $\beta $ in downstream classification task. Experimental results show superiority of our method over several state-of-the-art FCN-based methods. Additionally, we identify crucial functional sub-networks for predicting Amyloid- $\beta $ depositions. The proposed method is anticipated to contribute valuable insights into the understanding of mechanisms of AD and its prevention.