Cortical cartography using the discrete conformal approach of circle packings

• Published in: NeuroImage (Orlando, Fla.) Vol. 23; pp. S119 - S128
• Main Authors: Hurdal, Monica K., Stephenson, Ken
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 2004; Elsevier Limited
• Subjects: Acquisitions & mergers; Algorithms; Brain; Cartography; Cerebral Cortex - anatomy & histology; Circle packing; Computational anatomy; Conformal map; Cortical flat map; Humans; Image Processing, Computer-Assisted - statistics & numerical data; Mapping; Methods; Models, Statistical; NMR; Nuclear magnetic resonance; Riemann Mapping Theorem; Software; Surface flattening; Circle packing; Riemann Mapping Theorem; Conformal map; Mapping; Surface flattening; Computational anatomy; Cortical flat map
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2004.07.018

Cortical flattening algorithms are becoming more widely used to assist in visualizing the convoluted cortical gray matter sheet of the brain. Metric-based approaches are the most common but suffer from high distortions. Conformal, or angle-based algorithms, are supported by a comprehensive mathematical theory. The conformal approach that uses circle packings is versatile in the manipulation and display of results. In addition, it offers some new and interesting metrics that may be useful in neuroscientific analysis and are not available through numerical partial differential equation conformal methods. In this paper, we begin with a brief description of cortical “flat” mapping, from data acquisition to map displays, including a brief review of past flat mapping approaches. We then describe the mathematics of conformal geometry and key elements of conformal mapping. We introduce the mechanics of circle packing and discuss its connections with conformal geometry. Using a triangulated surface representing a cortical hemisphere, we illustrate several manipulations available using circle packing methods and describe the associated “ensemble conformal features” (ECFs). We conclude by discussing current and potential uses of conformal methods in neuroscience and computational anatomy.