View source for Distribution modeling

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Image:PW-2015SLC.png|[[2015_Winter_Project_Week#Projects|Projects List]]
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==Key Investigators==
Anuja Sharma, Guido Gerig

==Project Description==
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<h3>Objective</h3>
* Evaluate the ability of regression methods utilizing distribution-valued measurements to differentiate between healthy controls and patients with pathology.
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<h3>Approach, Plan</h3>
* 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. 
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<h3>Progress</h3>
* 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.
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