Simon Lévy^{1}, Ali Khatibi^{2,3,4,5}, Gabriel Mangeat^{1}, Jen-I Chen^{2,6}, Kristina Martinu^{2}, Pierre Rainville^{2,6}, Nikola Stikov^{1,7}, and Julien Cohen-Adad^{1,8}

Several quantitative MRI metrics have been proposed to quantify myelin in the central nervous system but each of them includes confounding factors that impair their sensitivity and specificity. Because these factors are different across metrics, data driven approaches developed for blind source separation problems to extract the common component between recordings of the same sources seem appropriate. This study compares linear and nonlinear methods to combine myelin-sensitive metrics: T1, MTR, MTsat, MTV (1 – PD). The repeatability of the resulting combined metrics as well as their sensitivity to different microstructural features are tested. A higher sensitivity is achieved with linear combinations.

**Methods**

* Data acquisition & processing.* The cohort and acquisition
protocol are described in Fig.1, along with the methods used to produce original
metrics maps

* Metric combination.* Four methods were
compared. For each one, the combining transformation was estimated on all
subjects’ metrics value quantified in the main WM tracts by slice, using a

- a linear Independent Component
Analysis (ICA) based on the *FastICA*
algorithm^{23,24}, which consists in a
linear separation of the data into statistically independent components by
non-Gaussianity maximization,

- a
linear Principal Component Analysis (PCA), which consists in linearly
separating the data into orthogonal components by maximizing their variance^{25},

- a nonlinear ICA based on the *MISEP* method^{26}, which still maximizes the
components independence but also integrates nonlinear mixtures of the components
through a neural network,

- a
nonlinear PCA minimizing the mean square error to fit a linear set of
hyperbolic tangent functions through a neural network^{27}.

Assuming that the common component between these metrics is related to myelin, only the component explaining the most variance in the original space is retained.

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