Gray Matter Surface Based Spatial Statistics (GS-BSS) in Diffusion Microstructure

Tract-based spatial statistics (TBSS) has proven to be a popular technique for performing voxel-wise statistical analysis that aims to improve sensitivity and interpretability of analysis of multi-subject diffusion imaging studies in white matter. With the advent of advanced diffusion MRI models - e.g., the neurite orientation dispersion density imaging (NODDI), it is of interest to analyze microstructural changes within gray matter (GM). A recent study has proposed using NODDI in gray matter based spatial statistics (N-GBSS) to perform voxel-wise statistical analysis on GM microstructure. N-GBSS adapts TBSS by skeletonizing the GM and projecting diffusion metrics to a cortical ribbon. In this study, we propose an alternate approach, known as gray matter surface based spatial statistics (GS-BSS), to perform statistical analysis using gray matter surfaces by incorporating established methods of registration techniques of GM surface segmentation on structural images. Diffusion microstructure features from NODDI and GM surfaces are transferred to standard space. All the surfaces are then projected onto a common GM surface non-linearly using diffeomorphic spectral matching on cortical surfaces. Prior post-mortem studies have shown reduced dendritic length in prefrontal cortex region in schizophrenia and bipolar disorder population. To validate the results, statistical tests are compared between GS-BSS and N-GBSS to study the differences between healthy and psychosis population. Significant results confirming the microstructural changes are presented. GS-BSS results show higher sensitivity to group differences between healthy and psychosis population in previously known regions.