Sara Garbarino

Journal papers

1. A. L. Young, N. P. Oxtoby,  S. Garbarino,  N. C. Fox,  F. M. Barkhof,  D. C. Alexander,  Data-driven modelling of neurodegenerative disease progression: thinking outside the black box, Nature Reviews Neuroscience, (2024).

2. P. Massa, G. Hurford, A. Volpara, M. Kuhar, A. F. Battaglia, et al, STIX imaging I - concept, Solar Physics, 298, 114, 2023.

3. S. Grisanti, M. Bellucci, F. Germano, C. Schenone, E. Barisione, S. Garbarino, et al, Response to the letter of Gemignani et al., Journal of the Neurological Sciences, 444, 2023.

4. A. Volpara, P. Massa, E. Perracchione, A. F. Battaglia, S. Garbarino, et al, Forward-fitting STIX visibilities, Astronomy & Astrophysics 668, A145, 2022.

5. P. Massa, S. Garbarino, F. Benvenuto, Approximation of discontinuous inverse operators with neural networks, Inverse problems,  38(10); 2022.

6. P. Massa, A. F. Battaglia, A. Volpara, H. Collier, G. J Hurford, et al, First hard X-ray imaging results by Solar Orbiter STIX, Solar Physics, 93; 2022.

7. S. Grisanti, S. Garbarino, E. Barisione, T. Aloe, M. Grosso, et al, Neurological long-COVID in the outpatient clinic: Two subtypes, two courses, Journal of the Neurological Sciences, 52(1); 2022.

8. F. Carbone, S. Ministrini, S. Garbarino, G. Vischi, V. Carpaneto, et al, Clinical predictors of late SARS-CoV-2 positivity in Italian internal medicine wards, European journal of clinical investigation, 120315; 2022.

9. S. Garbarino, M. Lorenzi, Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain, Neuroimage 235, 117980; 2021.

10. P. A. Wijeratne, S. Garbarino, S. Gregory, E. B. Johnson, R. I. Scahill, J, et al, Revealing the timeline of structural MRI changes in premanifest to manifest Huntington disease, Neurology Genetics, 7(5); 2021.

11. M. Bellio, D. Furniss, N. Oxtoby, S. Garbarino, N. Firth, et al, Opportunities and barriers for adoption of a decision support tool for Alzheimer’s Disease, ACM Transactions on Computing for Healthcare, 2(4); 2021.

12. P. Massa, E. Perracchione, S. Garbarino, A.F. Battaglia, F. Benvenuto, et al, Imaging from STIX visibility amplitudes, Astronomy & Astrophysics, 656(A25); 2021.

13. E.S. Dorraji, A. Oteiza, S. Kuttner, M. Martin-Armas, P. Kanapathippillai, et al, Positron emission tomography and single photon emission computed tomography imaging of tertiary lymphoid structures during the development of lupus nephritis, International Journal of Immunopathology and Pharmacology, 35(2); 2021.

14. R. Pascuzzo, N. Oxtoby, A. Young, J. Blevins, G. Castelli, et al, Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt–Jakob disease, Acta Neuropathologica , 140(2); 2020.

15. S. Garbarino, M. Lorenzi, N. Oxtoby, E. Vinke, R. Marinescu, et al, Differences in topological progression profile among neurodegenerative diseases from imaging data, eLife 2019(8), e49298; 2019.

16. S. Garbarino and G. Caviglia, Multivariate Regularized Newton method for tumor hypoxia in kinetic framework, Comm. App. Ind. Math. 10(2), 47-53; 2019.

17. R. Marinescu, A. Eshaghi, M. Lorenzi, A. Young, N. Oxtoby, S. Garbarino, S. Crutch, D. Alexander, DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders, NeuroImage 4(192), 166-177; 2019.

18. F. Delbary and S. Garbarino, Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology, Inverse Problems in Science and Engineering; 2018.

19. M. Scussolini, S. Garbarino, M. Piana, G. Sambuceti and G. Caviglia, Reference Tissue Models for FDG-PET Data: Identifiability and Solvability, IEEE Trans. Rad. Plasma Med. Sciences, 1-10; 2018.

20. M. Scussolini, S. Garbarino, G. Sambuceti, G. Caviglia and M. Piana, A physiology–based parametric imaging method for FDG–PET data, Inverse Problems 33, 125010; 2017.

21. N. Oxtoby, S. Garbarino, N. Firth, J. Warren, M. Schott, D. Alexander, Data driven model of structural brain connectivity changes in sporadic Alzheimer’s Disease, Frontiers in Neurology 8, 580; 2017.

22. G. Denevi, S. Garbarino and A. Sorrentino, Iterative algorithms for a non–linear inverse problem in atmospheric lidar, Inverse Problems 33, 085010. DOI: 10.1088/1361-6420/aa7904 

23. F. Delbary, S. Garbarino, V. Vivaldi, Compartmental analysis of dynamic nuclear medicine data: models and identifiability, Inverse Problems 32, 125010; 2016. 

24. S. Garbarino, A. Sorrentino, A. M. Massone, A. Sannino, A. Boselli, et al, Expectation Maximization and the retrieval of the atmospheric extinction coefficients by inversion of Raman LIDAR data, Optics Express, 24(19), 21497–21511; 2016.

25. S. Garbarino, V. Vivaldi, F. Delbary, G. Caviglia, M. Piana, et al, A new compartmental method for the analysis of liver FDG kinetics, EJNMMI Res. 2015, 5–35; 29 2015.

26. S. Garbarino, G. Caviglia, G. Sambuceti, F. Benvenuto and M. Piana, A novel description of FDG excretion in the renal system: application to metformin treated models, Phys. Med. Biol. 59, 2469–2484; 2014. 

27. S. Garbarino, G. Caviglia, M. Brignone, M. Massollo, G. Sambuceti and M. Piana, Estimate of FDG excretion by means of compartmental analysis and Ant Colony Optimization of nuclear medicine data, Comput. Math. Method M. 2013, 793142; 2013.

Chapters and  proceedings 

1. D. Parodi, F. Benvenuto, M. Piana, S. Garbarino, ODE Learning Problem with Adjoint State Method, Proceedings of SIMAI 2023, MS24, 2023.

2. S. Garbarino, C. Campi, A. Uccelli, M. Pardini, Tau propagation out of the MTL areas is facilitated by the presence of amyloid. Alzheimer's and Dementia, 2023.

3. S. Garbarino, M. Lorenzi, Modeling and inference of spatio-temporal protein dynamics across brain networks, Information Processing in Medical Imaging. Lecture Notes in Computer Science 11492, 37-69; 2019.

4. S. Garbarino, M. Lorenzi, N. Oxtoby, E. Vinke, R. Marinescu, et al, Mechanistic profiles of neurodegeneration: a study in Alzheimer’s disease, healthy ageing and primary progressive multiple sclerosis, Alzheimer’s and Dementia 14(7), P1280-P1281; 2018.

5. R. Marinescu, S. Primativo, A. Young, N. Oxtoby, N. Firth, et al, Analysis of the heterogeneity of Posterior Cortical Atrophy: data-driven model predicts distinct atrophy patterns for three different cognitive subgroups, Alzheimer’s & Dementia 13(7), P1379-P1380; 2017.

6. R. Marinescu, A. Eshaghi, M. Lorenzi, A.Young, N. Oxtoby, et al, A vertex clustering model for disease progression: Application to cortical thickness images, Information Processing in Medical Imaging. Lecture Notes in Computer Science 10265, 134-145; 2017.

7. A. Buschiazzo, G. Sambuceti, A. Orengo, S. Ravera, F. Fais, et al, Effect of Metformin on Cancer Glucose Metabolism: Correlation Between FDG Escape and Glucose-6-Phosphatase Activity in the Endoplasmatic Reticulum, Eur. J Nucl. Med. Mol. Imag. 42, S454–S454; 2015.

8. F. Bongioanni, F. Fiz, R. Piva, S. Garbarino, G. Bottoni, et al, Compact bone erosion and bone marrow metabolic stunning in multiple myeloma treated by transplantation of autologous hematopoietic stem cells, Eur. J Nucl. Med. Mol. Imag. 41, S183–S184; 2014.