Distribution modeling

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Key Investigators

Anuja Sharma, Guido Gerig

Evaluate the ability of regression methods utilizing distribution-valued measurements to differentiate between healthy controls and patients with pathology.

Model probability distributions along DTI fiber tracts instead of scalar summary measurements to create a tract profile (e.g. cross-sectional averages).
Our spatiotemporal modeling method utilizes these distribution-valued data to create growth trajectories such that the complete probability distribution is estimated continuously in space and time.
Using these methods, we can estimate a spatiotemporal growth trajectory for the healthy infant population. We then compare our method's ability to detect clinical differences in infants with Krabbe's disease in reference to a control population.

We found that our method had better sensitivity towards finding differences in growth trajectories.
We could find statistically significant differences in scenarios where the differences were small enough to be missed by conventional methods.