Cerebral cortical folding analysis with multivariate modeling and testing: Studies on gender differences and neonatal development

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.

Research highlights
► This paper presents a novel statistical framework for cerebral cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest. Unlike typical ROI-based methods that summarize folding by a single number, the proposed descriptor unifies multiple characteristics of surface geometry in high-dimensional space (hundreds or thousands of dimensions). Furthermore, the paper proposes a new application of a nonparametric permutation-based approach for rigorous statistical hypothesis testing with multivariate cortical descriptors. We believe that the proposed framework is a significant improvement over the current of state of the art in folding analysis.
► The paper presents two cross-sectional clinical studies applying the proposed framework to study folding differences (i) between genders and (ii) in neonates with complex congenital heart disease. Both studies lead to novel interesting results that shed new light on previous studies in the literature as well as present first-time analyses.