用多脑图谱融合GNN模型，提升抑郁症功能MRI诊断准确率。
Multi-atlas Ensemble Graph Neural Network Model For Major Depressive Disorder Detection Using Functional MRI Data
- 融合多个脑区图谱构建集成GNN，捕捉大脑网络复杂性
- 在多中心数据集上达到75.8%准确率，灵敏度高达88.89%
- 适合精神疾病影像辅助诊断、神经科学与深度学习交叉研究者
重度抑郁症（MDD）是全球最常见精神障碍之一，严重损害日常生活与生活质量，位列残疾第二大原因。当前诊断主要依赖临床观察与患者自述，忽视其复杂的病理生理机制。神经科学研究表明，抑郁是一种脑网络紊乱，功能磁共振成像（fMRI）在识别和治疗中具有重要作用。静息态功能磁共振（rs-fMRI）是研究MDD的主流技术。近年来，图神经网络（GNN）因其擅长处理图结构数据，在神经影像分析中受到关注。本研究提出一种基于多脑图谱的集成GNN模型，通过融合多个脑区分割图谱特征，更精准地捕捉大脑复杂性并识别差异特征，以实现MDD检测。在大规模多中心MDD数据集上验证，最佳模型表现：准确率75.80%，敏感性88.89%，特异性61.84%，精确率71.29%，F1分数79.12%。
原文摘要 · Abstract (English)
Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies on clinical observations and patient-reported symptoms, overlooking the diverse underlying causes and pathophysiological factors contributing to depression. Therefore, scientific researchers and clinicians must gain a deeper understanding of the pathophysiological mechanisms involved in MDD. There is growing evidence in neuroscience that depression is a brain network disorder, and the use of neuroimaging, such as magnetic resonance imaging (MRI), plays a significant role in identifying and treating MDD. Rest-state functional MRI (rs-fMRI) is among the most popular neuroimaging techniques used to study MDD. Deep learning techniques have been widely applied to neuroimaging data to help with early mental health disorder detection. Recent years have seen a rise in interest in graph neural networks (GNNs), which are deep neural architectures specifically designed to handle graph-structured data like rs-fMRI. This research aimed to develop an ensemble-based GNN model capable of detecting discriminative features from rs-fMRI images for the purpose of diagnosing MDD. Specifically, we constructed an ensemble model by combining features from multiple brain region segmentation atlases to capture brain complexity and detect distinct features more accurately than single atlas-based models. Further, the effectiveness of our model is demonstrated by assessing its performance on a large multi-site MDD dataset. The best performing model among all folds achieved an accuracy of 75.80%, a sensitivity of 88.89%, a specificity of 61.84%, a precision of 71.29%, and an F1-score of 79.12%.
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