Principles of intensive human neuroimaging

Area of Science:

• Neuroscience
• Cognitive Science
• Neuroimaging

Background:

• Large, shared functional magnetic resonance imaging (fMRI) datasets are crucial in human neuroscience.
• Current approaches include 'wide' fMRI (many individuals, less data) and 'deep' fMRI (few individuals, more data).

Purpose of the Study:

• Introduce and define 'intensive' fMRI as an emerging deep fMRI strategy.
• Highlight its potential for advanced computational modeling and single-voxel brain function analysis.
• Discuss principles, trade-offs, and practical considerations for this approach.

Main Methods:

• Focus on extensive sampling of cognitive phenomena.
• Emphasize careful experimental design for a rich hypothesis space.
• Incorporate optimization of data quality and strategic curation of public resources.

Main Results:

• Intensive fMRI enables detailed investigation of brain function at the single voxel level.
• This approach supports robust computational modeling of cognitive processes.
• It necessitates more than just increased data collection, requiring strategic design and curation.

Conclusions:

• Intensive fMRI represents a significant advancement within deep fMRI methodologies.
• It provides a powerful framework for exploring complex cognitive phenomena and neural mechanisms.
• Maximizing community impact through curated resources is essential for the success of intensive fMRI.