title: Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development.
abstract: This paper presents a novel statistical framework for human cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial normalization-based approaches stemming from the deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in a high-dimensional space (hundreds/thousands of dimensions). In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. This paper presents new mathematical insights into the relationship of cortical complexity with intra-cranial volume (ICV). It shows that conventional complexity descriptors implicitly handle ICV differences in different ways, thereby lending different meanings to "complexity". The paper proposes a new application of a nonparametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. The paper presents two cross-sectional studies applying the proposed framework to study folding differences between genders and in neonates with complex congenital heart disease. Both studies lead to novel interesting results.
authors.affiliation: Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, USA. suyash.awate@gmail.com
authors.forename: Suyash P
authors.initials: SP
authors.lastname: Awate
authors.forename: Paul A
authors.initials: PA
authors.lastname: Yushkevich
authors.forename: Zhuang
authors.initials: Z
authors.lastname: Song
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Licht
authors.forename: James C
authors.initials: JC
authors.lastname: Gee
mesh_terms: D000328:Adult; D000465:Algorithms; D019412:Anatomy, Cross-Sectional; D002540:Cerebral Cortex / Q000033:anatomy & histology* / Q000254:growth & development / Q000502:physiology*; D015331:Cohort Studies; D005260:Female; D006330:Heart Defects, Congenital / Q000150:complications; D006801:Humans; D018636:Hypoplastic Left Heart Syndrome / Q000473:pathology; D007091:Image Processing, Computer-Assisted; D007231:Infant, Newborn; D007969:Leukomalacia, Periventricular / Q000473:pathology; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D015999:Multivariate Analysis; D012727:Sex Characteristics; D014188:Transposition of Great Vessels / Q000473:pathology