DEVELOPMENTAL MULTIMODAL IMAGING OF NEUROCOGNITIVE DYNAMICS (DEV-MIND)

项目概要/摘要 研究领域标准（RDoC）项目启动了一个研究分类框架 基于对基因、行为、电路等的转化研究中出现的功能维度， 认知生物学参数近十年后，RDoC已经发展成为一个由功能性 域（例如，认知系统），域特定构造（例如，注意力、感知）和分析单位 为了测量每个构造（例如，生理学、行为、基因等）。许多研究都有助于 根据认知生物学参数定义每个结构，虽然这些努力已经广泛地 虽然成功，但维度RDoC构建本身在很大程度上仍然未经验证。 这种缺乏充分验证的情况是RFA-MH-19-242的核心，它要求提案“执行无偏差 现有结构的数据驱动验证，可能涉及合并、细分或分层组织 通过在结构之间和结构内部集成数据来实现。”特别是，RFA呼吁使用“多个”的研究， 行为任务和每个结构的分析水平，”“多模式数据融合. 比较结构，”和“涉及结构和功能数据的结构的数据驱动的定义， 大脑状态、网络、电路动力学和信号中的层次结构如何与基于任务的输出相关， assays.”RFA还鼓励使用“加速纵向设计，特别强调 发展......和尖端的计算方法来分类，预测和解释发展 轨迹”神经认知和（Epi）基因组动力学的发育多模态成像（Dev- MIND）联盟响应这一呼吁，推出了一种创新的、大规模的发育多模式神经成像技术， 这项研究将利用先前开发的纵向儿科队列和数据融合算法， 通过NSF支持的Dev-Cog项目建立的团队。具体来说，Dev-MIND将评估 认知系统领域内三个构造的统一性和潜在的层次结构（即， 注意力、认知控制和工作记忆），使用一系列自定义认知任务，多模式 成像、（epi）基因组分析、加速纵向设计和数据驱动的相似性度量， 结构确认测试。我们的神经影像学方法将包括动态功能映射， 脑磁图（MEG），基于多模态分割的高分辨率体积MRI分析， 以及全脑动态功能连接的功能性MRI（fMRI）。这些神经成像和行为 性能度量也将与（EPI）遗传数据结合以识别基因组之间的协方差， 认知和神经活动模式。这种数据驱动的方法将使分类和预测 每个结构的发展轨迹，并且是计算精神病学目标的核心。总之，这 该项目汇集了领先的调查团队、一系列最先进的神经成像技术，以及 先进的分析方法，对现有的RDoC结构进行无偏的、数据驱动的验证。

Project Summary/Abstract The Research Domain Criteria (RDoC) project initiated a framework for developing research classifications based on functional dimensions emerging from translational research on genes, behaviors, circuits, and other cognitive-biological parameters. Almost a decade later, RDoC has grown into a matrix consisting of functional domains (e.g., cognitive systems), domain-specific constructs (e.g., attention, perception), and units of analysis for measuring each construct (e.g., physiology, behavior, genes, etc.). Numerous studies have contributed to defining each construct in terms of cognitive-biological parameters, and while these efforts have been broadly successful, the dimensional RDoC constructs themselves remain largely unvalidated. This lack of adequate validation is central to RFA-MH-19-242, which requests proposals that “perform unbiased data-driven validation of existing constructs that may involve merging, subdividing, or hierarchically organizing them by integrating data between and within constructs.” Specifically, the RFA calls for studies that use “multiple behavioral tasks and levels of analysis per construct,” “multimodal data fusion … to unbiasedly classify and compare constructs,” and “data-driven definitions of constructs that involve structural and functional data on how brain states, networks, circuit dynamics, and hierarchies in the signals relate to outputs from task-based assays.” The RFA also encourages the use of “accelerated longitudinal designs, with a particular emphasis on development … and cutting-edge computational approaches to classify, predict, and explain developmental trajectories.” The Developmental Multimodal Imaging of Neurocognitive and (Epi)genomic Dynamics (Dev- MIND) Consortium responds to this call with an innovative, large-scale developmental multimodal neuroimaging study that will leverage previously-developed longitudinal pediatric cohorts and data fusion algorithms that this team established through the NSF-supported Dev-Cog project. Specifically, Dev-MIND will evaluate the unitarity and potential hierarchical structure of three constructs within the cognitive systems domain (i.e., attention, cognitive control, and working memory) using a battery of custom cognitive tasks, multimodal imaging, (epi)genomic analysis, an accelerated longitudinal design, and data-driven similarity metrics for construct validation testing. Our neuroimaging approach will include dynamic functional mapping based on magnetoencephalography (MEG), high-resolution volumetric MRI analyses based on multimodal parcellation, and functional MRI (fMRI) for whole-brain dynamic functional connectivity. These neuroimaging and behavioral performance metrics will also be combined with (epi)genetic data to identify covariance between genomic, cognitive, and neural activity patterns. Such data-driven approaches will enable classification and prediction of developmental trajectories per construct, and are central to the goals of computational psychiatry. In sum, this project brings together leading investigative teams, an array of state-of-the-art neuroimaging technology, and cutting-edge analytical methods to perform unbiased, data-driven validation of existing RDoC constructs.