Sulcal depth-based cortical shape analysis in normal healthy control and schizophrenia groups

Sulcal depth is an important marker of brain anatomy in neuroscience/neurological function. Previously, sulcal depth has been explored at the region-of-interest (ROI) level to increase statistical sensitivity to group differences. In this paper, we present a fully automated method that enables inferences of ROI properties from a sulcal region- focused perspective consisting of two main components: 1) sulcal depth computation and 2) sulcal curve-based refined ROIs. In conventional statistical analysis, the average sulcal depth measurements are employed in several ROIs of the cortical surface. However, taking the average sulcal depth over the full ROI blurs overall sulcal depth measurements which may result in reduced sensitivity to detect sulcal depth changes in neurological and psychiatric disorders. To overcome such a blurring effect, we focus on sulcal fundic regions in each ROI by filtering out other gyral regions. Consequently, the proposed method results in more sensitive to group differences than a traditional ROI approach. In the experiment, we focused on a cortical morphological analysis to sulcal depth reduction in schizophrenia with a comparison to the normal healthy control group. We show that the proposed method is more sensitivity to abnormalities of sulcal depth in schizophrenia; sulcal depth is significantly smaller in most cortical lobes in schizophrenia compared to healthy controls (p < 0.05).

[1]  Kanti V. Mardia,et al.  Ridge Curves and Shape Analysis , 1996, BMVC.

[2]  John H. Gilmore,et al.  Novel Local Shape-Adaptive Gyrification Index with Application to Brain Development , 2017, MICCAI.

[3]  Sung Yong Shin,et al.  Spectral-based automatic labeling and refining of human cortical sulcal curves using expert-provided examples , 2010, NeuroImage.

[4]  Andrea J. van Doorn,et al.  Surface shape and curvature scales , 1992, Image Vis. Comput..

[5]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[6]  Jong-Min Lee,et al.  Automated Sulcal Depth Measurement on Cortical Surface Reflecting Geometrical Properties of Sulci , 2013, PloS one.

[7]  Alexander Vladimirsky,et al.  Ordered Upwind Methods for Static Hamilton-Jacobi Equations: Theory and Algorithms , 2003, SIAM J. Numer. Anal..

[8]  Arno Klein,et al.  A reproducible evaluation of ANTs similarity metric performance in brain image registration , 2011, NeuroImage.

[9]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[10]  Meritxell Bach Cuadra,et al.  A Surface-Based Approach to Quantify Local Cortical Gyrification , 2008, IEEE Transactions on Medical Imaging.

[11]  Hassaan Tohid,et al.  Alterations of the occipital lobe in schizophrenia , 2015, Neurosciences.

[12]  Bennett A. Landman,et al.  Non-local STAPLE: An Intensity-Driven Multi-atlas Rater Model , 2012, MICCAI.

[13]  Aaron Carass,et al.  Consistent cortical reconstruction and multi-atlas brain segmentation , 2016, NeuroImage.

[14]  Paul M. Thompson,et al.  Joint Sulcal Detection on Cortical Surfaces With Graphical Models and Boosted Priors , 2009, IEEE Transactions on Medical Imaging.

[15]  Sang Won Seo,et al.  Automatic extraction of sulcal lines on cortical surfaces based on anisotropic geodesic distance , 2010, NeuroImage.

[16]  A. Vita,et al.  Progressive loss of cortical gray matter in schizophrenia: a meta-analysis and meta-regression of longitudinal MRI studies , 2012, Translational Psychiatry.

[17]  Guillermo Sapiro,et al.  A geometric method for automatic extraction of sulcal fundi , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[18]  Martin Styner,et al.  Automatic sulcal curve extraction on the human cortical surface , 2015, Medical Imaging.

[19]  Anders M. Dale,et al.  Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature , 2010, NeuroImage.

[20]  Paul M. Thompson,et al.  Hamilton–Jacobi Skeleton on Cortical Surfaces , 2008, IEEE Transactions on Medical Imaging.

[21]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[22]  Lei Wang,et al.  Symmetric abnormalities in sulcal patterning in schizophrenia , 2008, NeuroImage.

[23]  J. Rapoport,et al.  Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[25]  Guido Gerig,et al.  Development of cortical shape in the human brain from 6 to 24months of age via a novel measure of shape complexity , 2016, NeuroImage.