AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.
AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.
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基于人工智能的多维神经成像系统用于表征重度抑郁症患者大脑结构和功能的异质性：COLISTRATE-MDD联合体设计和理论基础。
DOI：
10.1186/s12888-022-04509-7
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发表时间：
2023-01-23
期刊：
影响因子：
4.4
通讯作者：
中科院分区：
文献类型：
--
作者：
迄今为止，在个体水平上开发基于神经影像学的重度抑郁症（MDD）生物标志物的努力受到限制。由于目前的诊断标准以症状为基础，重度抑郁症被定义为一种障碍，而不是一种病因已知的疾病；此外，神经测量常被药物状态和异质症状状态混淆。我们描述了一个联盟，通过新的多元坐标系统（coordinate - mdd）的维度来量化神经解剖学和神经功能异质性。利用成像协调和机器学习方法，在大量无药物、深度表型的MDD参与者中，大脑改变的模式在可复制和基于神经生物学的维度上被定义，并提供了在个体水平上预测治疗反应的潜力。目前正在共享来自多民族社区人群、首发和复发性重度抑郁症（MDD）的国际数据集，这些数据均为无药物治疗、当前抑郁发作，具有前瞻性纵向治疗结果并处于缓解期。神经成像数据包括去识别、个体、结构MRI和静息状态功能MRI，并在特定部位附加正电子发射断层扫描（PET）数据。最先进的分析方法包括用于提取解剖和功能成像变量的自动图像处理，用于解释部位和扫描仪变化的成像变量的统计协调，以及用于从健康参与者的神经结构和功能中识别与MDD相关的主要模式的半监督机器学习方法。我们正在应用一个迭代过程，通过定义表征深度表型样本的神经维度，然后在新样本中测试这些维度，以评估特异性和可靠性。至关重要的是，我们的目标是使用机器学习方法来识别基于前瞻性纵向治疗结果数据的治疗反应的新预测因子，并且我们可以在完全独立的站点外部验证这些维度。我们描述财团，成像协议和分析使用初步结果。到目前为止，我们的研究结果表明，跨多个站点的数据集可以协调一致，并建设性地汇集在一起，以实现这个大型项目的执行。
Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.
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影响因子：
4.8
作者：
Chen AA;Beer JC;Tustison NJ;Cook PA;Shinohara RT;Shou H;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
影响因子：
16.2
作者：
Cui, Zaixu;Li, Hongming;Satterthwaite, Theodore D.
通讯作者：
Satterthwaite, Theodore D.
影响因子：
14.5
作者：
Chand, Ganesh B.;Dwyer, Dominic B.;Davatzikos, Christos
通讯作者：
Davatzikos, Christos
影响因子：
4.2
作者：
Bomyea, Jessica;Choi, Soo-Hee;Taylor, Charles
通讯作者：
Taylor, Charles