Modeling Developmental Change: Practical Integration of Advanced Neuroimaging and Statistical Methods

神经成像技术，包括结构和功能磁共振成像（MRI），使研究人员能够调查认知发展变化的神经基础。近年来，研究人员在整个发展过程中获得同一个体的多个指标变得越来越普遍。这些纵向MRI数据集需要特别考虑处理和分析，但该领域作为一个整体还没有标准化这些数据集的最佳实践，这可能是难以在实验室和研究中复制结果的原因之一。为期两天的研讨会将向60名发育神经成像研究人员开放，并将教授处理，分析，建模和解释纵向神经成像数据的最佳实践。这将有助于研究人员对大脑和认知如何在发展过程中变化进行稳健和一致的研究。重要的是，本次研讨会将资助至少九名学生或学员谁是计划，或直接与纵向神经影像数据，提供强大的实践技能，为新兴的研究科学家在发展认知神经科学领域。为发展认知神经科学领域的研究人员举办一个研讨会，以研究纵向建模，统计处理和分析以及解释方面的差异，这既及时又重要。最近的工作揭示了方法的差异可能会对神经影像学领域的可复制性产生不利影响，并且越来越多的人致力于验证神经影像学研究中采用的处理和统计技术。技术和报告标准的标准化也得到了越来越多的支持，例如最近用于组织和描述MRI数据集的脑成像数据结构（BIDS）协议。本次研讨会的总体目标是教授处理，分析，建模和解释纵向结构和功能神经成像数据的最佳实践指南，这将告知我们大脑和认知如何在发展过程中变化的知识。它将解决纵向神经影像学特有的其他统计问题，这些问题也需要验证，并考虑技术和报告标准的标准化，以提高结果的可比性。本次研讨会的主要成果将是，研究人员将回答有关处理纵向功能和结构MRI数据的问题，以及推进发展认知神经科学研究的正确工具。这种类型的工作有可能通过观察学习过程中的神经变化来回答有关神经可塑性和认知发展敏感期的基本问题。它也与发展情感，社会和临床神经科学领域有着内在的联系，因此有可能直接转化为新兴精神病理学的预防和治疗。本次研讨会将推动发展认知神经科学领域的发展，以开发强大而精确的模型，这些模型对公共政策具有强大的转化应用。

Neuroimaging techniques, including structural and functional magnetic resonance imaging (MRI), have allowed researchers to investigate the neural bases of developmental changes in cognition. In recent years, it has become more common for researchers to obtain multiple measures of the same individual across development. These longitudinal MRI datasets require special consideration for processing and analysis, yet the field as a whole has not standardized best practices for these datasets, which could be one reason why it is difficult to replicate results across laboratories and research studies. A two-day workshop will be open to 60 developmental neuroimaging researchers and will teach best practices for processing, analyzing, modeling, and interpreting longitudinal neuroimaging data. This will help researchers conduct robust and consistent research on how the brain and cognition change across development. Importantly, this workshop will fund at least nine students or trainees who are planning to, or are directly working with, longitudinal neuroimaging data, providing strong practical skills for emerging research scientists in the field of developmental cognitive neuroscience. It is both timely and vital to hold a workshop for researchers in the field of developmental cognitive neuroscience to examine differences in longitudinal modeling, statistical processing and analysis, and interpretation. Recent work has uncovered how methodological differences may be adversely affecting replicability in the field of neuroimaging, and there is an increasing drive to validate the processing and statistical techniques that are employed in neuroimaging research. There has also been increasing support for standardization of techniques and reporting criteria, such as the recent Brain Imaging Data Structure (BIDS) protocol for organizing and describing MRI datasets. The overarching aim of this workshop is to teach best practice guidelines for processing, analyzing, modeling, and interpreting longitudinal structural and functional neuroimaging data, which will inform our knowledge of how the brain and cognition change across development. It will address additional statistical concerns specific to longitudinal neuroimaging that also need validation, and consider standardization of techniques and reporting criteria that will improve the comparability of findings. The main outcome of this workshop will be that researchers leave with answers to questions about processing longitudinal functional and structural MRI data and the correct tools to move forward with research in developmental cognitive neuroscience. This type of work has the potential to answer fundamental questions about neural plasticity and sensitive periods of cognitive development through observing neural changes during learning. It is also inherently related to the fields of developmental affective, social, and clinical neuroscience, and therefore has the potential to translate directly to the prevention and treatment of emerging psychopathology. This workshop will push the field of developmental cognitive neuroscience forward to develop robust and precise models that have strong translational applications for public policy.