Paper Info
Title
Sulcal depth-based cortical shape analysis in normal healthy control and schizophrenia groups.
Abstract
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 gyral regions. Consequently, the proposed method is more sensitive to group differences than a traditional ROI approach. In the experiment, we focused on a cortical morphological analysis of sulcal depth reduction in schizophrenia with a comparison to a normal healthy control group. We show that the proposed method is more sensitivity to abnormalities of sulcal depth in schizophrenia, and sulcal depth is significantly smaller in most cortical lobes in schizophrenia compared to healthy controls (p < 0.05).
Year
DOI
Venue
2018
10.1117/12.2293275
Proceedings of SPIE
Field
DocType
Volume
Healthy control,Pattern recognition,Brain anatomy,Artificial intelligence,Statistical sensitivity,Mathematics,Schizophrenia,Shape analysis (digital geometry),Statistical analysis
Conference
10574
ISSN
Citations 
PageRank 
0277-786X
0
0.34
References 
Authors
7
4
Name
Order
Citations
PageRank
Ilwoo Lyu14211.53
Hakmook Kang2114.41
Neil D. Woodward300.68
Bennett A. Landman470074.20