用脑区功能连接设计新读出层，提升阿尔茨海默病早期诊断准确率。
Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging
- 基于功能连接与节点嵌入聚类脑区，构建脑感知读出层
- 在383人数据上预测PACC得分显著优于传统模型
- 可定位关键脑区，适合神经影像与临床研究者使用
阿尔茨海默病（AD）是一种以进行性记忆与认知衰退为特征的神经退行性疾病，影响全球数百万人群。由于其异质性和进展差异，诊断极具挑战。本研究提出一种新型脑感知读出层（BA readout layer），用于图神经网络（GNNs），旨在提升神经影像中早期AD诊断的可解释性与预测准确性。通过基于功能连接和节点嵌入对脑区进行聚类，该层增强了GNN捕捉复杂脑网络特征的能力。研究分析了383名受试者（包括认知正常及前临床期AD个体）的神经影像数据，采用T1加权MRI、静息态fMRI和FBB-PET构建脑图。结果表明，搭载BA读出层的GNN在预测前临床阿尔茨海默病认知复合量表（PACC）得分方面显著优于传统模型，表现出更高的鲁棒性与稳定性。自适应的BA读出层还通过突出与任务相关的关键脑区，增强了模型可解释性。这些发现表明，该方法为阿尔茨海默病的早期诊断与分析提供了有力工具。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Diagnosing AD is challenging due to its heterogeneous nature and variable progression. This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs), designed to improve interpretability and predictive accuracy in neuroimaging for early AD diagnosis. By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. We analyzed neuroimaging data from 383 participants, including both cognitively normal and preclinical AD individuals, using T1-weighted MRI, resting-state fMRI, and FBB-PET to construct brain graphs. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score, demonstrating higher robustness and stability. The adaptive BA readout layer also offers enhanced interpretability by highlighting task-specific brain regions critical to cognitive functions impacted by AD. These findings suggest that our approach provides a valuable tool for the early diagnosis and analysis of Alzheimer's disease.
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