A DTI-derived measure of cortico-cortical connectivity.
We arm researchers with a simple method to chart a macroscopic cortico-cortical connectivity network in living human subjects. The researcher provides a diffusion-magnetic resonance imaging (MRI) data set and N cortical regions of interest. In return, we provide an N xN structural adjacency matrix (SAM) quantifying the relative connectivity between all cortical region pairs. We also return a connectivity map for each pair to enable visualization of interconnecting fiber bundles. The measure of connectivity we devise is: 1) free of length bias, 2) proportional to fiber bundle cross-sectional area, and 3) invariant to an exchange of seed and target. We construct a 3-D lattice scaffolding (graph) for white-matter by drawing a link between each pair of voxels in a 26-voxel neighborhood for which their two respective principal eigenvectors form a sufficiently small angle. The connectivity between a cortical region pair is then measured as the maximum number of link-disjoint paths that can be established between them in the white-matter graph. We devise an efficient Edmonds-Karp-like algorithm to compute a conservative bound on the maximum number of link-disjoint paths. Using both simulated and authentic diffusion-tensor imaging data, we demonstrate that the number of link-disjoint paths as a measure of connectivity satisfies properties 1)-3), unlike the fraction of intersecting streamlines-the measure intrinsic to most existing probabilistic tracking algorithms. Finally, we present connectivity maps of some notoriously difficult to track longitudinal and contralateral fasciculi.
Zalesky
Andrew
A
Melbourne Neuropsychiatry Centre, University ofMelbourne, 3053 Carlton South, Australia. azalesky@unimelb.edu.au
Fornito
Alex
A
D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008875:Middle Aged; D008959:Models, Neurological; D009412:Nerve Fibers / Q000502:physiology; D009415:Nerve Net / Q000502:physiology*; D009431:Neural Conduction / Q000502:physiology; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric