Principles of intensive human neuroimaging

Abstract

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.

Keywords: big data; brain imaging; cognition; data quality; deep fMRI; functional MRI.

Figure 1.. How intensive fMRI datasets span the space of possible scientific inferences in cognitive neuroscience research.

Consider the space of possible inferences that one might make from experimental data (colored dots). A conventional hypothesis-driven fMRI dataset has a small hypothesis space (dashed circle) and allows for a small number of inferences. An intensive fMRI dataset has a large hypothesis space (dashed rectangle) and allows for a multitude of different inferences. With inferential overlap across multiple intensive datasets (gray shaded regions), there is the potential to integrate datasets and enable more far-reaching inferences about brain structure and function.