Combined analysis of multimodal brain imaging data for the study and prevention of major depressive disorders in high risk offspring
Francesca Zanderigo has always enjoyed applying her mathematical skills to define and solve problems related to human health care. As a graduate student at the Department of Information Engineering, University of Padova, Italy, she focused on the quantitative analysis of cerebral hemodynamics in patients with carotid artery stenosis using Magnetic Resonance Imaging (MRI) (Zanderigo et al., IEEE Transaction on Biomedical Engineering 56(5), 2009, 1287-97), and the hypo/ hyperglycemia prevention in diabetics by on-line monitoring and prediction of blood glucose concentration (Sparacino et al., Diabetes Research and Clinical Practice 74, 2006, S160-S163; Sparacino et al., IEEE Transactions on Biomedical Engineering 54(5), 2007, 931- 937; Zanderigo et al., Journal of Diabetes Science and Technology 1(5), 2007, 645-651). As a Research Scientist for the interdisciplinary Division of Molecular Imaging and Neuropathology, Department of Psychiatry and New York State Psychiatric Institute, Columbia University, she implemented mathematical models to analyze Positron Emission Tomography (PET) data in neuroreceptors system investigations. Specifically, Dr. Zanderigo developed a Bayesian approach to reduce the estimates variability in parametric images generation (Zanderigo et al., Nuclear Medicine and Biology 37, 2010, 443-51) and the application of alternative fitting methods to improve the sensitivity in occupancy studies (Zanderigo et al., Journal of Cerebral Blood Flow and Metabolism 30(7), 2010, 1366-72), and collaborated in the assessment of a minimally invasive approach for PET data analysis (Ogden et al., Journal of Cerebral Blood Flow and Metabolism 30(4), 2010, 816-26), the investigation of new radioligands (DeLorenzo et al., Journal of Cerebral Blood Flow and Metabolism 29(7), 2009, 1332-45; Milak et al., Journal of Nuclear Medicine 51(12), 2010, 1892-900), and their use in the study of the serotonin neurotransmitter system (Parsey et al., Biological Psychiatry 68(2), 2010, 170-8). She recently began working on the combined analysis of multimodal brain images (i.e. PET, MRI, Diffusion Tensor Imaging, functional MRI) to provide biomarkers for personalized treatment of Major Depressive Disorders in high risk offspring by predicting and identifying individual factors that may favor certain treatments over others. She is also investigating and developing a novel unified automated approach for PET non-invasive full quantification to promote the use of PET in clinical practice.