Analysis of local gyrification index using a novel shape-adaptive kernel and the standard FreeSurfer spherical kernel : evidence from chronic schizophrenia outpatients

Schizophrenia can be considered a brain disconnectivity condition related to aberrant neurodevelopment that causes alterations in the brain structure, including gyrification of the cortex. Literature findings on cortical folding are incoherent: they report hypogyria in the frontal, superior-parietal and temporal cortices, but also frontal hypergyria. This discrepancy in local gyrification index (LGI) results could be due to the commonly used spherical kernel (Freesurfer), which is a method of analysis that is still not spatially precise enough. In this study we would like to test the spatial accuracy of a novel method based on a shape-adaptive kernel (Cmorph). The analysis of differences in gyrification between chronic schizophrenia outpatients (n = 30) and healthy controls (n = 30) was conducted with two methods: Freesurfer LGI and Cmorph LGI. Widespread differences in the LGI between schizophrenia outpatients and healthy controls were found using both methods. Freesurfer showed hypogyria in the superior temporal gyrus and the right temporal pole; it also showed hypergyria in the rostral-middle-frontal cortex in schizophrenia outpatients. In comparison, Cmorph revealed that hypergyria is equally represented as hypogyria in orbitofrontal and central brain regions. The clusters from Cmorph were smaller and distributed more broadly, covering all lobes of the brain. The presented evidence from disrupted cortical folding in schizophrenia indicates that the shape-adaptive kernel approach has a potential to improve the knowledge on the disrupted cortical folding in schizophrenia; therefore, it could be a valuable tool for further investigation on big sample size

Edge-Preserving Smoothing Using a Similarity Measure in Adaptive Geodesic Neighbourhoods

This paper introduces a novel image-dependent filtering approach derived from concepts known in mathematical morphology and aiming at edge-preserving smoothing natural images. Like other adaptive methods, it assumes that the neighbourhood of a pixel contains the essential information required for the estimation of local features. The proposed strategy does not require the definition of any spatial operator as it determines automatically, from the unfiltered input data, a weighting neighbourhood and a weighting kernel for each pixel location. It essentially consists in a weighted averaging combining both spatial and tonal information, for which a twofold similarity measure has to be calculated from local geodesic time functions. By designing relevant geodesic masks, two adaptive filtering algorithms are derived, that are particularly efficient at smoothing heterogeneous areas while preserving relevant structures in greyscale and multichannel images.JRC.DDG.H.6-Spatial data infrastructure