Comparing like with like: the power of knowing where you are.

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 7 T offers further gains in signal-to-noise ratio and speed of image acquisition, with a structural image resolution of about 300 μm. 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 over-simplification of brain activity patterns in order to be computationally tractable.