Global Deep Learning Initiative to Understand Outcomes in Major Depression

摘要 严重抑郁障碍(MDD)是世界范围内导致残疾的主要原因，大约一半的MDD患者 患有难治性抑郁症。近年来，rTMS的应用和临床效益有了很大的提高。 由于只有40%-50%的患者对当前标准的rtms有反应，因此对预测哪些患者有很大的兴趣。 哪些大脑特征最能预测反应，以及这些特征与大脑核心的关系 MDD的生物签名。为了解决这个问题，并响应NIH对精准医学方法的呼吁，我们的 了解严重萧条结果的全球深度学习倡议将国际领导人团结在 MDD和rTMS研究、神经成像和人工智能，以确定rTMS反应的一般性预测因素，以及 评估它们与MDD的脑生物标志物之间的关系。提出了两个主要的创新。首先，我们使用小说 基于卷积神经网络的多峰预测特征提取深度学习方法 脑成像(sMRI、DTI和rsfMRI)；用于全脑和基于表面的脑功能标测的策略 和结构、用于特征提取的DVAE以及转移学习(从辅助数据集和任务中学习)将 在保护个人隐私的同时提取预测性特征。CNN针对我们的多模式脑图进行了培训 预测性任务将提取MDD研究中尚未充分利用的额外信息层， 以便更好地预测临床状态和治疗反应。其次，我们的全球谜-MDD网络将 提供来自全球代表性人群的多样化测试数据，以确保我们的预测模型不会 在对不同的数据进行测试时将其分解。Enigma在全球范围内协调提取大脑的测量 队列将提高严谨性，并确保分析得到充分支持和始终如一的执行。我们包括 与REST-META-MDD的重要合作伙伴关系，REST-META-MDD是一个从 以检验我们预测模型的泛化能力。我们工作的可能结果是一套 预先筛选工具，以预测谁对rTMS反应最好，并对大脑有更深入的了解 预测rTMS后治疗结果的MDD签名。所有工具将通过NITRC和 网站，并将在我们的Enigma网络上进行测试，以保证工作对大型- MDD研究内外的规模结果预测。

ABSTRACT Major depressive disorder (MDD) is the leading cause of disability worldwide, and around half of MDD patients have treatment-resistant depression. The use and clinical benefit of rTMS have escalated greatly in recent years. As only 40-50% of patients respond to current standard rTMS, there is great interest in predicting which patients are likely to respond, what brain features best predict response, and how these features relate to the core biosignatures of MDD. To address this, and responding to NIH’s call for Precision Medicine approaches, our Global Deep Learning Initiative to Understand Outcomes in Major Depression unites international leaders in MDD and rTMS research, neuroimaging, and AI to identify generalizable predictors of rTMS response, and assess how they relate to brain biomarkers of MDD. Two major innovations are proposed. First, we use novel deep learning methods, based on convolutional neural networks, to extract predictive features from multimodal brain images (sMRI, DTI, and rsfMRI); tactics applied in whole-brain and surface-based mapping of brain function and structure, DVAEs for feature extraction, and transfer learning (to learn from auxiliary datasets and tasks) will distill predictive features while protecting individual privacy. CNNs trained on multimodal brain maps for our predictive tasks will distill additional layers of information that have not yet been fully exploited in MDD research, to better predict clinical status and treatment response. Second, our worldwide ENIGMA-MDD network will provide diverse test data from globally representative populations, to ensure that our predictive models do not break down when tested on diverse data. ENIGMA’s harmonized extraction of brain measures across worldwide cohorts will enhance rigor and ensure that analyses are well-powered and consistently performed. We include an important partnership with REST-meta-MDD, a Chinese consortium collecting multimodal imaging data from patients with MDD, to test the generalizability of our predictive models. The likely outcome of our work is a set of pre-screening tools to predict who will respond best to rTMS, and a deeper understanding of the brain signatures of MDD that predict treatment outcomes following rTMS. All tools will be made public via NITRC and ENIGMA websites, and will be tested across our ENIGMA network, guaranteeing impact of the work for large- scale outcome prediction within and outside of MDD research.