Forging a path to mesoscopic imaging success with ultra-high field functional magnetic resonance imaging

Philos Trans R Soc Lond B Biol Sci. 2021 Jan 4;376(1815):20200040. doi: 10.1098/rstb.2020.0040. Epub 2020 Nov 16.


Functional magnetic resonance imaging (fMRI) studies with ultra-high field (UHF, 7+ Tesla) technology enable the acquisition of high-resolution images. In this work, we discuss recent achievements in UHF fMRI at the mesoscopic scale, on the order of cortical columns and layers, and examine approaches to addressing common challenges. As researchers push to smaller and smaller voxel sizes, acquisition and analysis decisions have greater potential to degrade spatial accuracy, and UHF fMRI data must be carefully interpreted. We consider the impact of acquisition decisions on the spatial specificity of the MR signal with a representative dataset with 0.8 mm isotropic resolution. We illustrate the trade-offs in contrast with noise ratio and spatial specificity of different acquisition techniques and show that acquisition blurring can increase the effective voxel size by as much as 50% in some dimensions. We further describe how different sources of degradations to spatial resolution in functional data may be characterized. Finally, we emphasize that progress in UHF fMRI depends not only on scientific discovery and technical advancement, but also on informal discussions and documentation of challenges researchers face and overcome in pursuit of their goals. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Keywords: cortical columns; fMRI; layer dependent; spatial specificity; ultra-high field.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Brain / physiology*
  • Functional Neuroimaging / instrumentation*
  • Humans
  • Image Processing, Computer-Assisted / instrumentation*
  • Magnetic Resonance Imaging / instrumentation*

Associated data

  • figshare/10.6084/m9.figshare.c.5144612