MICCAI 2013 - LNCS 8149-8151

Anatomy-Guided Discovery of Large-Scale Consistent Connectivity-Based Cortical Landmarks

Xi Jiang¹, Tuo Zhang¹, Dajiang Zhu¹, Kaiming Li³, Jinglei Lv^{2, 1}, Lei Guo², and Tianming Liu¹

¹Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA

²School of Automation, Northwestern Polytechnical University, Xi’an, China

³Biomedical Imaging Technology Center, Emory University/Georgia Institute of Technology, Atlanta, GA, USA

Abstract. Establishment of structural and functional correspondences across different brains is one of the most fundamental issues in the human brain mapping field. Recently, several multimodal DTI/fMRI studies have demonstrated that consistent white matter fiber connection patterns can predict brain function and represent common brain architectures across individuals and populations, and along this direction, several approaches have been proposed to discover large-scale cortical landmarks with common structural connection profiles. However, an important limitation of previous approaches is that the rich anatomical information such as gyral/sulcal folding patterns has not been incorporated into the landmark discovery procedure yet. In this paper, we present a novel anatomy-guided discovery framework that defines and optimizes a dense map of cortical landmarks that possess group-wise consistent anatomical and fiber connectional profiles. This framework effectively integrates reliable and rich anatomical, morphological, and fiber connectional information for landmark initialization, optimization and prediction, which are formulated and solved as an energy minimization problem. Validation results based on fMRI data demonstrate that the identified 555 cortical landmarks are producible, predictable and exhibit accurate structural and functional correspondences across individuals and populations, offering a universal and individualized brain reference system for neuroimaging research.

Keywords: DTI, fMRI, anatomical, connectivity, cortical landmarks

LNCS 8151, p. 617 ff.

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