Automatic clustering and population analysis of white matter tracts using maximum density paths

• DOI: 10.1016/j.neuroimage.2014.04.033
• Publication Date: 2014-04-18T14:28:17Z
• Authors: Gautam Prasad; Shantanu H. Joshi; Neda Jahanshad; Julio Villalon-Reina; Iman Aganj; Christophe Lenglet; Guillermo Sapiro; Katie L. McMahon; Greig I. de Zubic...; Nicholas G. Martin; Margaret J. Wright; Arthur W. Toga; Paul M. Thompson
• Keywords: 2805 Cognitive Neuroscience; Male; Biomedical and clinical sciences; Shortest path; Cognitive Neuroscience; Image Processing; 610; Bioengineering; Medical and Health Sciences; Clustering; Young Adult; 03 medical and health sciences; Computer-Assisted; Nerve Fibers; 0302 clinical medicine; Dijkstra; Health Sciences; HARDI; Neural Pathways; Image Processing, Computer-Assisted; Cluster Analysis; Humans; Longest path; Geodesic distance; Connectivity; Maximum density path; Neurology & Neurosurgery; Biomedical and Clinical Sciences; Psychology and Cognitive Sciences; Brain; Health sciences; Reproducibility of Results; 006; White Matter; Diffusion Tensor Imaging; Neurology; 2808 Neurology; Anisotropy; Female; Atlas; Curve registration; Tractography; Algorithms; MRI; Hough

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.