标题： 3DViT-GAT：一种基于统一图谱的三维视觉变换器和图学习框架，用于使用结构磁共振成像数据检测重度抑郁症 显示英文标题

标题： 3DViT-GAT: A Unified Atlas-Based 3D Vision Transformer and Graph Learning Framework for Major Depressive Disorder Detection Using Structural MRI Data

摘要： 重度抑郁症（MDD）是一种普遍的心理健康状况，对个人福祉和全球公共卫生产生负面影响。 使用结构磁共振成像（sMRI）和深度学习（DL）方法进行MDD的自动检测，有望提高诊断准确性并实现早期干预。 现有的大多数方法仅采用体素级特征或从预定义的大脑图谱构建的手工区域表示，限制了它们捕捉复杂大脑模式的能力。 本文开发了一个统一的流程，利用视觉变压器（ViTs）从sMRI数据中提取3D区域嵌入，并利用图神经网络（GNN）进行分类。 我们探索了两种定义区域的策略：（1）基于图谱的方法，使用预定义的结构和功能大脑图谱，以及（2）基于立方体的方法，其中ViTs被直接训练从均匀提取的3D块中识别区域。 此外，生成余弦相似性图以建模区域间的关系，并指导基于GNN的分类。 进行了大量实验，使用REST-meta-MDD数据集来证明我们模型的有效性。 通过分层的10折交叉验证，最佳模型获得了78.98%的准确率，76.54%的灵敏度，81.58%的特异性，81.58%的精确率，以及78.98%的F1分数。 此外，基于图谱的模型始终优于基于立方体的方法，突显了在MDD检测中使用领域特定解剖先验的重要性。 显示英文摘要

摘要： Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (sMRI) and deep learning (DL) methods holds increasing promise for improving diagnostic accuracy and enabling early intervention. Most existing methods employ either voxel-level features or handcrafted regional representations built from predefined brain atlases, limiting their ability to capture complex brain patterns. This paper develops a unified pipeline that utilizes Vision Transformers (ViTs) for extracting 3D region embeddings from sMRI data and Graph Neural Network (GNN) for classification. We explore two strategies for defining regions: (1) an atlas-based approach using predefined structural and functional brain atlases, and (2) an cube-based method by which ViTs are trained directly to identify regions from uniformly extracted 3D patches. Further, cosine similarity graphs are generated to model interregional relationships, and guide GNN-based classification. Extensive experiments were conducted using the REST-meta-MDD dataset to demonstrate the effectiveness of our model. With stratified 10-fold cross-validation, the best model obtained 78.98% accuracy, 76.54% sensitivity, 81.58% specificity, 81.58% precision, and 78.98% F1-score. Further, atlas-based models consistently outperformed the cube-based approach, highlighting the importance of using domain-specific anatomical priors for MDD detection.