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Abstract

Magnetic resonance imaging can now provide human brain images of structure, function, and connectivity with
isotropic voxels smaller than one millimeter, and thus much smaller than the cortical thickness. This resolution,
achievable in a scan time of less than 1 h, enables visualization of myeloarchitectural layer structure, intracortical
variations in functional activity—recorded in changes in blood oxygenation level dependent signal or cerebral
blood volume CBV—and intracortical axonal orientational structure via diffusion-weighted magnetic resonance
imaging. While recent improvements in radiofrequency receiver coils now enable excellent image data to be
obtained at 3T, scanning at the ultra-high field of 7T offers further gains in signal-to-noise ratio and speed of
image acquisition, with a structural image resolution of about 300 lm. These improvements throw into sharp
question the strategies that have become conventional for the analysis of functional imaging data, especially
the practice of spatial smoothing of raw functional data before further analysis. Creation of a native cortical
map for each human subject that provides a reliable individual parcellation into cortical areas related to Brodmann
Areas enables a strikingly different approach to functional image analysis. This proposed approach involves
surface registration of the cortices of groups of subjects using maps of the longitudinal relaxation time
T1 as an index of myelination, and methods for inferring statistical significance that do not entail spatial smoothing.
The outcome should be a far more precise comparison of like-with-like cortical areas across subjects, with
the potential to greatly increase experimental power, to discriminate activity in neighboring cortical areas, and
to enable correlation of function and connectivity with specific cytoarchitecture. Such analyses should enable a
far more convincing modeling of brain mechanisms than current graph-based methods that require gross oversimplification
of brain activity patterns in order to be computationally tractable.