Fusion of white and gray matter geometry: A framework for investigating brain development

• Published in: Medical image analysis 2014-12, Vol.18 (8), p.1349-1360
• Main Authors: Savadjiev, Peter, Rathi, Yogesh, Bouix, Sylvain, Smith, Alex R., Schultz, Robert T., Verma, Ragini, Westin, Carl-Fredrik
• Format: Article
• Language: English
• Subjects: Autism; Cortex; Diffusion Tensor Imaging - methods; Geometry; Gray Matter - anatomy & histology; Gray Matter - growth & development; Humans; Image Interpretation, Computer-Assisted - methods; Neurodevelopment; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; White matter; White Matter - anatomy & histology; White Matter - growth & development
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2014.06.013

[Display omitted] •We introduce a novel method for computing multi-scale white matter tract geometry.•We use it to perform a joint analysis of gray and white matter morphology.•Gray and white matter geometry information is combined via Mutual Information.•Preliminary results show a possible change in developmental trajectory in autism.•We discuss the connection between our analysis and neurodevelopmental biology. Current neuroimaging investigation of the white matter typically focuses on measurements derived from diffusion tensor imaging, such as fractional anisotropy (FA). In contrast, imaging studies of the gray matter oftentimes focus on morphological features such as cortical thickness, folding and surface curvature. As a result, it is not clear how to combine findings from these two types of approaches in order to obtain a consistent picture of morphological changes in both gray and white matter. In this paper, we propose a joint investigation of gray and white matter morphology by combining geometrical information from white and the gray matter. To achieve this, we first introduce a novel method for computing multi-scale white matter tract geometry. Its formulation is based on the differential geometry of curve sets and is easily incorporated into a continuous scale-space framework. We then incorporate this method into a novel framework for “fusing” white and gray matter geometrical information. Given a set of fiber tracts originating in a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. A quantitative marker is created by combining the distributions of these scalar values using Mutual Information. This marker can be then used in the study of normal and pathological brain structure and development. We apply this framework to a study on autism spectrum disorder in children. Our preliminary results support the view that autism may be characterized by early brain overgrowth, followed by reduced or arrested growth (Courchesne, 2004).