Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA; Department of Psychology, Stanford University, Stanford, CA, USA.
Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands; Netherlands Institute for Neuroscience, Royal Netherlands Academy of Sciences, Amsterdam, the Netherlands; Cognitive Psychology, Faculty of Behavioural and Movement Sciences, Vrije Universiteit, Amsterdam, the Netherlands.
Trends Neurosci. 2024 Nov;47(11):856-864. doi: 10.1016/j.tins.2024.09.011. Epub 2024 Oct 24.
The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in human neuroscience has focused on acquiring either a few hours of data on many individuals ('wide' fMRI) or many hours of data on a few individuals ('deep' fMRI). In this opinion article, we highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: one that strives for extensive sampling of cognitive phenomena to support computational modeling and detailed investigation of brain function at the single voxel level. We discuss the fundamental principles, trade-offs, and practical considerations of intensive fMRI. We also emphasize that intensive fMRI does not simply mean collecting more data: it requires careful design of experiments to enable a rich hypothesis space, optimizing data quality, and strategically curating public resources to maximize community impact.
在人类神经科学中，大型公共共享功能磁共振成像（fMRI）数据集的兴起，侧重于获取许多个体的数小时数据（“宽” fMRI）或少数个体的数小时数据（“深” fMRI）。在这篇观点文章中，我们强调了深 fMRI 中的一种新兴方法，我们称之为“密集” fMRI：一种努力广泛采样认知现象以支持计算模型，并在单像素水平上详细研究大脑功能的方法。我们讨论了密集 fMRI 的基本原则、权衡取舍和实际考虑因素。我们还强调，密集 fMRI 不仅仅意味着收集更多的数据：它需要精心设计实验，以实现丰富的假设空间，优化数据质量，并战略性地管理公共资源，以最大限度地提高社区影响力。
