PDF(Pubmed)
Abstract：
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.
摘要：
神经影像学中的预测模型越来越多地设计为旨在改善风险分层并支持精神病学中的介入工作。这些模型中的许多都是在学龄儿童的样本中开发的。然而，尽管越来越多的证据表明胎儿大脑成熟改变，婴儿,和幼儿（FIT）时期调节儿童不良心理健康结果的风险，这些模型很少在FIT样本中实现。预测建模在这些年龄的儿童中的应用提供了开发强大工具的机会，以改善发育基础的神经机制的表征。为了促进FIT神经成像中预测模型的更广泛使用，我们对当前预测建模FIT研究中使用的方法进行了简要介绍和系统综述.反思过去十年进行的100多项研究中的当前实践，我们提供主题的概述，模态，以及该领域和研究不足领域常用的方法。然后，我们概述了对预测早年健康结果感兴趣的神经影像学研究人员的道德和未来考虑。包括可能对高级机器学习方法或使用FIT数据相对较新的研究人员。总之,机器学习领域FIT研究的最后十年为加速预测整个疾病和健康领域的早期生活轨迹奠定了基础。
