Thalassemia is a congenital disorder of hemoglobin synthesis which can lead to thromboembolic events and stroke in the brain. In this work we propose to use a functional connectivity model to discriminate between control and diseased subjects. Our connectivity measure is based on functional magnetic resonance imaging, and hence common variations of the blood oxygenation level in spatially distant areas. Analyzing this connectivity could highlight abnormal neuronal activation and provide us with a descriptor (bio-marker) of the disease. To estimate the connectivity, we propose a robust learning scheme based on the graphical lasso model, whose hyperparameter is validated within a cross-validation scheme. To analyze model fit, we transfer the mean connectivity from the control group to the thalassemic patient group. Our null hypothesis is that the model learned on control subjects is perfectly adequate (in the maximum likelihood sense) to describe the patients. The results of the permutation test suggest that the some patients with thalassemia do not have the same connectivity structure as the control.