A Framework to Compare Tractography Algorithms Based on Their Performance in Predicting Functional Networks

  • Fani Deligianni
  • Chris A. Clark
  • Jonathan D. Clayden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8159)

Abstract

Understanding the link between brain function and structure is of paramount importance in neuroimaging and psychology. In practice, inaccuracies in recovering brain networks may confound neurophysiological factors and reduce the sensitivity in detecting statistically robust links. Hence, reproducibility and inter-subject variability of tractography approaches is currently under extensive investigation. However, a reproducible network is not necessarily more accurate. Here, we build a statistical framework to compare the performance of local and global tractograpy in predicting functional brain networks. We use a model selection framework based on sparse canonical correlation analysis and an appropriate metric to evaluate the similarity between the predicted and the observed functional networks. We demonstrate compelling evidence that global tractography outperforms local tractography in a cohort of healthy adults.

Keywords

structural connectivity global tractography prediction 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Fani Deligianni
    • 1
  • Chris A. Clark
    • 1
  • Jonathan D. Clayden
    • 1
  1. 1.Imaging and Biophysics Unit, Institute of Child HealthUniversity College LondonLondonUnited Kingdom

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