Connecting and merging fibres: Pathway extraction by combining probability maps

B. W. Kreher*, S. Schnell, I. Mader, K. A. Il'yasov, J. Hennig, V. G. Kiselev, D. Saur

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Probability mapping of connectivity is a powerful tool to determine the fibre structure of white matter in the brain. Probability maps are related to the degree of connectivity to a chosen seed area. In many applications, however, it is necessary to isolate a fibre bundle that connects two areas. A frequently suggested solution is to select curves, which pass only through two or more areas. This is very inefficient, especially for long-distance pathways and small areas. In this paper, a novel probability-based method is presented that is capable of extracting neuronal pathways defined by two seed points. A Monte Carlo simulation based tracking method, similar to the Probabilistic Index of Connectivity (PICo) approach, was extended to preserve the directional information of the main fibre bundles passing a voxel. By combining two of these extended visiting maps arising from different seed points, two independent parameters are determined for each voxel: the first quantifies the uncertainty that a voxel is connected to both seed points; the second represents the directional information and estimates the proportion of fibres running in the direction of the other seed point (connecting fibre) or face a third area (merging fibre). Both parameters are used to calculate the probability that a voxel is part of the bundle connecting both seed points. The performance and limitations of this DTI-based method are demonstrated using simulations as well as in vivo measurements.

Original languageEnglish (US)
Pages (from-to)81-89
Number of pages9
Issue number1
StatePublished - Oct 15 2008


  • Anisotropic diffusion
  • Connectivity
  • DTI
  • MRI
  • WM

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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