Novel local shape-adaptive gyrification index with application to brain development

Abstract

Conventional approaches to quantification of the cortical folding employ a simple circular kernel. Such a kernel commonly covers multiple cortical gyral/sulcal regions that may be functionally unrelated and also often blurs local gyrification measurements. We propose a novel adaptive kernel for quantification of the local cortical folding, which incorporates neighboring gyral crowns and sulcal fundi. The proposed kernel is adaptively elongated to cover regions along the cortical folding patterns. The experimental results showed that the proposed kernel-based gyrification measure achieved a higher reproducibility in a multi-scan human phantom dataset and captured the cortical folding in a more shape-adaptive way than the conventional method. In early human brain development, we found positive correlations with age over most cortical regions as previously found as well as novel, refined regions of both positive and negative correlations undetectable by the conventional method. © 2017, Springer International Publishing AG.