High resolution data analysis strategies for mesoscale human functional MRI at 7 and 9.4T

The advent of ultra-high field functional magnetic resonance imaging (fMRI) has greatly facilitated submillimeter resolution acquisitions (voxel volume below (1mm³)), allowing the investigation of cortical columns and cortical depth dependent (i.e. laminar) structures in the human brain. Advanced data analysis techniques are essential to exploit the information in high resolution functional measures. In this article, we use recent, exemplary 9.4T human functional and anatomical data to review the advantages and disadvantages of (1) pooling high resolution data across regions of interest for cortical depth profile analysis, (2) pooling across cortical depths for mapping patches of cortex while discarding depth-dependent (i.e. columnar) effects, and (3) isotropic sampling without pooling to assess individual voxel's responses. A set of cortical depth meshes may be a solution to sampling information tangentially while keeping correspondence across depths. For quantitative analysis of the spatial organization in fine-grained structures, a cortical grid approach is advantageous. We further extend this general framework by combining it with a previously introduced cortical layer volume-preserving (equi-volume) approach. This framework can readily accommodate the research questions which allow for spatial smoothing within or across layers. We demonstrate and discuss that equi-volume sampling yields a slight advantage over equidistant sampling given the current limitations of fMRI voxel size, participant motion, coregistration and segmentation. Our 9.4T human anatomical and functional data indicate the advantage over lower fields including 7T and demonstrate the practical applicability of T₂^* and T₂-weighted fMRI acquisitions.