Exploiting Machine Learning Principles for Assessing the Fingerprinting Potential of Connectivity Features

  • Silvia Obertino
  • Sofía Jiménez Hernández
  • Ilaria Boscolo Galazzo
  • Francesca Benedetta Pizzini
  • Mauro Zucchelli
  • Gloria Menegaz
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

To which extent connectivity measures are able to characterize subjective features? The pipeline leading from the signal acquisition to the connectivity matrix allows numerous degrees of freedom each having an impact on the final result. In this paper, we investigated the sensitivity and specificity of the connectivity models within a machine learning framework through the assessment of the detectability of repeated measures of the same subject versus other subjects. Two fiber Orientation Distribution Function (fODF) reconstruction methods, one of which firstly proposed in this paper, three tractography algorithms and four connectivity features were considered and performance was expressed in terms of Area Under the Curve of the test-retest recognition task. Results suggest that there is a trade-off between the selectivity of the fODF reconstruction methods and the conservativeness of the fiber tracking algorithms across all microstructural indices . The best solution was provided by using an high angular resolution fODF estimation method and the most restrictive deterministic tractography algorithm.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Silvia Obertino
    • 1
  • Sofía Jiménez Hernández
    • 2
  • Ilaria Boscolo Galazzo
    • 1
  • Francesca Benedetta Pizzini
    • 3
  • Mauro Zucchelli
    • 1
  • Gloria Menegaz
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaVeronaItaly
  2. 2.Universidad Politécnica de ValenciaValènciaSpain
  3. 3.Department of Diagnostics and PathologyVerona University HospitalVeronaItaly

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