Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
作者: Roberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng, Christopher R. K. Ching, Andre Aleman, Alyssa R. Amod, Zeynep Basgoze, Francesco Benedetti, Bianca Besteher, Katharina Brosch, Robin Bülow, Romain Colle, Colm G. Connolly, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Kathryn Cullen, Udo Dannlowski, Christopher G. Davey, Annemiek Dols, Jan Ernsting, Jennifer W. Evans, Lukas Fisch, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Hans J. Grabe, Nynke A. Groenewold, Dominik Grotegerd, Tim Hahn, J. Paul Hamilton, Laura K. M. Han, Ben J. Harrison, Tiffany C. Ho, Neda Jahanshad, Alec J. Jamieson, Andriana Karuk, Tilo Kircher, Bonnie Klimes-Dougan, Sheri-Michelle Koopowitz, Thomas Lancaster, Ramona Leenings, Meng Li, David E. J. Linden, Frank P. MacMaster, David M. A. Mehler, Susanne Meinert, Elisa Melloni, Bryon A. Mueller, Benson Mwangi, Igor Nenadić, Amar Ojha, Yasumasa Okamoto, Mardien L. Oudega, Brenda W. J. H. Penninx, Sara Poletti, Edith Pomarol-Clotet, Maria J. Portella, Elena Pozzi, Joaquim Radua, Elena Rodríguez-Cano, Matthew D. Sacchet, Raymond Salvador, Anouk Schrantee, Kang Sim, Jair C. Soares, Aleix Solanes, Dan J. Stein, Frederike Stein, Aleks Stolicyn, Sophia I. Thomopoulos, Yara J. Toenders, Aslihan Uyar-Demir, Eduard Vieta, Yolanda Vives-Gilabert, Henry Völzke, Martin Walter, Heather C. Whalley, Sarah Whittle, Nils Winter, Katharina Wittfeld, Margaret J. Wright, Mon-Ju Wu, Tony T. Yang, Carlos Zarate, Dick J. Veltman, Lianne Schmaal, Paul M. Thompson
分类: q-bio.QM, cs.LG, q-bio.NC
发布日期: 2023-11-18 (更新: 2025-01-24)
备注: arXiv admin note: text overlap with arXiv:2206.08122
💡 一句话要点
利用深度学习模型分类重度抑郁症患者
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 重度抑郁症 深度学习 支持向量机 脑形态学 分类模型 神经影像 多模态信息 数据分析
📋 核心要点
- 现有方法在基于线性机器学习的MDD分类中表现不佳，准确率较低，难以有效区分患者与健康对照。
- 论文提出通过整合顶点级皮层特征，利用深度学习模型DenseNet与传统SVM进行MDD分类，期望提高分类性能。
- 实验结果显示，尽管DenseNet和SVM的分类性能有所提升，但仍未能有效区分MDD患者与健康对照，表明该方法的局限性。
📝 摘要（中文）
重度抑郁症（MDD）是一种复杂的精神疾病，影响全球数亿人。尽管已有研究探讨大脑形态学变化与MDD的关联，但由于该疾病的异质性，相关性仍存在争议。本文利用ENIGMA-MDD工作组的全球代表性数据，评估了基于顶点级皮层特征的DenseNet和支持向量机（SVM）在MDD患者与健康对照组（HC）分类中的表现。结果显示，两种分类器的表现接近随机水平，表明在此特征和分类器组合下，MDD的分类存在困难。未来可能需要更复杂的多模态信息整合以提高诊断性能。
🔬 方法详解
问题定义：本文旨在解决重度抑郁症患者与健康对照组的分类问题。现有基于线性机器学习的方法在准确性上存在明显不足，难以有效识别MDD患者。
核心思路：论文提出通过整合顶点级皮层特征，利用深度学习模型DenseNet与支持向量机（SVM）进行分类。该设计旨在捕捉复杂的非线性模式，以期提高分类性能。
技术框架：研究使用来自ENIGMA-MDD工作组的全球数据，包含7,012名参与者，分为2,772名MDD患者和4,240名健康对照。通过交叉验证评估模型性能，比较不同分类器的效果。
关键创新：本研究的创新点在于首次将顶点级皮层特征与深度学习模型结合用于MDD分类，尝试突破传统线性方法的局限。
关键设计：DenseNet和SVM的参数设置经过优化，使用了适当的损失函数和网络结构。然而，实验结果显示两者的分类性能接近随机水平，表明该特征与分类器组合的有效性不足。
📊 实验亮点
实验结果显示，DenseNet的平衡准确率为51%，SVM为53%，在包含所有站点的交叉验证中，DenseNet的准确率略微提高至58%。尽管有一定提升，但整体性能仍未达到有效区分MDD患者与健康对照的水平。
🎯 应用场景
该研究的潜在应用领域包括精神健康诊断和个性化治疗方案的制定。通过改进的分类方法，未来可能为重度抑郁症的早期识别和干预提供新的思路，帮助改善患者的生活质量。
📄 摘要（原文）
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. Here, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of such features and classifiers is unfeasible. Perhaps more sophisticated integration of multimodal information may lead to a higher performance in this diagnostic task.