Principles of intensive human neuroimaging

A growing number of publicly available human functional magnetic resonance imaging (fMRI) data sets use a ‘deep’ sampling approach, where many hours of data are acquired from a few individuals. We highlight an emerging approach within deep fMRI, which we refer to as ‘intensive’ fMRI: the creation of large-scale, publicly shared data sets that extensively sample cognitive phenomena to support the investigation of brain function at the single voxel level.We discuss key principles of intensive fMRI, its benefits and challenges, and practical considerations in creating intensive fMRI data sets.We discuss how intensive fMRI can advance scientific discovery in systems neuroscience, machine learning, and artificial intelligence. 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. 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 m