Group-wise automatic mesh-based analysis of cortical thickness

摘要: The analysis of neuroimaging data from pediatric populations presents several challenges. There are normal variations in brain shape infancy to adulthood and developmental changes related tissue maturation. Measurement cortical thickness is one important way analyze such changes. We developed a novel framework that allows group-wise automatic mesh-based thickness. Our approach divided into four main parts. First an individual pre-processing pipeline applied on each subject to create genus-zero inflated white matter surfaces with measurements. second part performs entropy-based correspondence these meshes using particle system, which establishes trade-off between even sampling the similarity corresponding points across population sulcal depth information spatial proximity. A initial particle sampling performed matched 98-lobe parcellation map prior particle-splitting phase. Third, corresponding re-sampled computed interpolated measurements, which are finally analyzed via statistical vertex-wise module. This consists automated 3D Slicer compatible modules. It has been tested on a small dataset incorporated in open-source C++ based high-level module called GAMBIT. GAMBIT's setup efficient batch processing, grid computing quality control. current research focuses use average template for surface re-sampling, as well thorough validation its application clinical studies.