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

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 nal result. In this paper, we investigated the sensitivity and specicity 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 ber Orientation Distribution Function (fODF) reconstruction methods, one of which rstly 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-o between the selectivity of the fODF reconstruction methods and the conservativeness of the ber 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.