Difference between revisions of "Distribution modeling"
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==Key Investigators== | ==Key Investigators== | ||
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<h3>Objective</h3> | <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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</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Approach, Plan</h3> | <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. | ||
</div> | </div> | ||
<div style="width: 27%; float: left; padding-right: 3%;"> | <div style="width: 27%; float: left; padding-right: 3%;"> | ||
<h3>Progress</h3> | <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. | ||
</div> | </div> | ||
</div> | </div> | ||
Latest revision as of 16:13, 9 January 2015
Home < Distribution modeling
Key Investigators
Anuja Sharma, Guido Gerig
Project Description
Objective
- Evaluate the ability of regression methods utilizing distribution-valued measurements to differentiate between healthy controls and patients with pathology.
Approach, Plan
- 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.
Progress
- 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.
