Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes

2025-03-06

AI Summary

Current early detection of amyloid deposition in Alzheimer’s disease (AD) relies heavily on costly and invasive PET imaging. To address this limitation, we propose a non-invasive prediction framework based solely on structural MRI (sMRI). Our method introduces a novel brain tetrahedral mesh representation, integrating anatomical landmark-driven adaptive tokenization with Gaussian process–modeled anatomical priors, and—uniquely—embeds this representation into a geometric Transformer architecture. Compared to conventional graph convolutional approaches, our model demonstrates significantly improved generalization and discriminative capability, particularly for individuals at intermediate AD risk. In both AD classification and amyloid-positivity prediction tasks, it outperforms state-of-the-art sMRI-based baselines. This work establishes a new paradigm for low-cost, high-accuracy, non-invasive early AD screening.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Alzheimer's disease (AD) is a major neurodegenerative condition that affects millions around the world. As one of the main biomarkers in the AD diagnosis procedure, brain amyloid positivity is typically identified by positron emission tomography (PET), which is costly and invasive. Brain structural magnetic resonance imaging (sMRI) may provide a safer and more convenient solution for the AD diagnosis. Recent advances in geometric deep learning have facilitated sMRI analysis and early diagnosis of AD. However, determining AD pathology, such as brain amyloid deposition, in preclinical stage remains challenging, as less significant morphological changes can be observed. As a result, few AD classification models are generalizable to the brain amyloid positivity classification task. Blood-based biomarkers (BBBMs), on the other hand, have recently achieved remarkable success in predicting brain amyloid positivity and identifying individuals with high risk of being brain amyloid positive. However, individuals in medium risk group still require gold standard tests such as Amyloid PET for further evaluation. Inspired by the recent success of transformer architectures, we propose a geometric deep learning model based on transformer that is both scalable and robust to variations in input volumetric mesh size. Our work introduced a novel tokenization scheme for tetrahedral meshes, incorporating anatomical landmarks generated by a pre-trained Gaussian process model. Our model achieved superior classification performance in AD classification task. In addition, we showed that the model was also generalizable to the brain amyloid positivity prediction with individuals in the medium risk class, where BM alone cannot achieve a clear classification. Our work may enrich geometric deep learning research and improve AD diagnosis accuracy without using expensive and invasive PET scans.

Problem

• Improving Alzheimer's diagnosis using geometric deep learning on tetrahedral meshes.
• Predicting brain amyloid positivity without costly and invasive PET scans.
• Enhancing classification accuracy for medium-risk individuals using anatomical landmarks.

Innovation

• Transformer-based geometric deep learning model
• Novel tokenization for tetrahedral meshes
• Incorporates anatomical landmarks via Gaussian process