Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany.
Neuropsychopharmacology. 2024 Nov;50(1):29-36. doi: 10.1038/s41386-024-01918-y. Epub 2024 Aug 14.
Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.
精神神经影像学面临着严谨性和可重复性的挑战,这促使人们重新考虑研究设计的相对优势和局限性。由于资源需求高和推断目标的不同,当前的设计在样本量、测量广度和纵向评估方面存在差异。在本篇概述和观点中,我们针对这一科学目标和资源限制的平衡,提供了一份关于精神神经影像学研究设计现状的指南。通过对比关键设计特征的启发式数据立方体,我们讨论了在小样本、高精度纵向研究(例如,个体化研究和队列研究)和大样本、最小纵向、人群研究之间的权衡取舍。精度研究通过干预和跟踪纵向过程,支持对个体内部机制的测试。人群研究支持跨多方面个体差异进行推广的测试。提出的相互验证模型(RVM)旨在递归地利用这些互补设计,按顺序积累证据,优化相对优势,并朝着提高长期临床实用性的方向发展。