Towards a New Personalized In-Vivo MRI Biomarker of Myelin Integrity and Damage in MS Patients – PHRT
Towards a New Personalized In-Vivo MRI Biomarker of Myelin Integrity and Damage in MS Patients
Multiple Sclerosis (MS) is a chronic disease characterized by brain inflammation, demyelination, axonal loss but also by spontaneous brain reparative mechanisms such as remyelination of the axons and gliosis. Due to this heterogenicity, the successful management and monitoring of treatment response calls for markers with an increased sensitivity and specificity to MS-related pathological processes. Our goal will be to combine the recent advances in multi-echo spin echo and diffusion MRI acquisition and reconstruction algorithms with newly developed methods of microstructure imaging, to provide robust in-vivo biomarkers of myelin integrity.
The goal of this project is to obtain a new sensitive and specific biomarker of myelin/axonal integrity, valid in both inflammatory and non-inflammatory settings. We will do this by combining the myelin-related properties (as obtained by modeling the relaxometry MRI data ) and the axonal structure and orientation-related information derived by modeling the diffusion MRI signal. The ultimate aim of this project is to provide personalized measures of myelin and axonal integrity, to develop a new patient-tailored way to optimize individual patient treatment strategy.
MRI biomarkers of myelin integrity in both WM and GM may help understanding how demyelination and remyelination occur and develop across all MS stage in living patients. Hence, MRI provides potential in-vivo biomarkers for stratifying MS patients in order to offer them a personalized treatment, adapted to their expression of the disease. As step towards personalized medicine in MS, these in-vivo non-invasive biomarkers may provide new therapeutic targets for treatments decision and development, which will ultimately benefit patients with MS.
To date, MRI is the reference neuroimaging modality used in MS assessment. Yet, due to its intrinsic disseminated essence, the processes linked to MS pathology have been difficult to visualize using conventional in-vivo imaging techniques, which are limited by low pathological specificity and low sensitivity to diffuse damage. Quantitative MRI techniques offer complementary information about the different components of brain tissue architecture. All of them have proven to be extremely sensitive to specific tissue abnormalities, albeit at the price of poor specificity. Combining the recent advances in qMRI using multimodal approaches may provide new biomarkers of disease severity and help to improve the clinical–radiological mismatch in MS treatment.
Patents / Startups
Fischi-Gomez E, Bonnier G, Li P-J, Maggi P, Le Geoff G, Kappos L, Granziera C (2019). Neurite orientation dispersion and density imaging predicts disability at 8 years follow-up in relapsing remitting MS patients. Proceedings of the Intl. Soc. Mag. Reson. Med. 27
Fischi-Gomez E, Rafael-Patiño J, Pizzolato M, Piredda GF, Hilbert T, Kober T, Canales-Rodriguez EJ, Thiran J-P (2021). Multi-compartment diffusion MRI, T2 relaxometry and myelin water imaging as neuroimaging descriptors for anomalous tissue detection. In proceedings of the IEEE International Societies for Biomedical Imaging (ISBI) 2021.
Rafael-Patiño J, Girard G, Fischi-Gomez E, Romascano D, Yu T, Pizzolato M, Ramirez-Manzanares A, Canales-Rodríguez EJ, Thiran J-P (2020). Multi-diffusion and multi-T2 weighted Monte-Carlo simulations. In Proceedings of OHBM Annual Meeting. Abstract 2158.
Schilling KG, Nath V, Hansen C, Parvathaneni P, Blaber J, Gao Y, Neher P, Aydogan DB, Shi Y, Ocampo- Pineda M, Schiavi S, Daducci A, Girard G, Barakovic M, Rafael-Patino J, Romascano D, Rensonnet G, Pizzolato M, Bates A, Fischi-Gomez E, Thiran J-P, Canales-Rodríguez EJ, Huang C, Zhu H, Zhong L, Cabeen R, Toga AW, Rheault F, Theaud G, Houde J-C, Sidhu J, Chamberland M, Westin C-F, Dyrby TB, Verma R, Rathi Y, Irfanoglu MO, Thomas C, Pierpaoli C, Descoteaux M, Anderson AW, Landman BA (2019). Limits to anatomical accuracy of diffusion tractography using modern approaches. NeuroImage, vol. 185, pp. 1-11.
Sokolov AA, Granziera C, Fischi-Gomez E, et al (2019). Brain network analyses in clinical neurosciences. Swiss Arch Neurol Psychiatr Psychother;170:w03074
Yu T, Canales-Rodríguez EJ,Pizzolato M, Piredda GF, Hilbert T, Fischi-Gomez E, Weigel M, Barakovic M, Bach-Cuadra M, Granziera C, Kober T, Thiran JP (2021). Model-Informed Machine Learning for multi-component T2 Relaxometry. Medical Imaging analysis,69:101940
Patents / Startups
Transition Postdoc Fellowship Project
Dr. Elda Fischi-Gomez
Signal Processing laboratory 5, EPFL and Translational Imaging in Neurology ThINK, Department of Biomedical Engineering. Neurological Clinic, University Hospital of Basel
Boston Children’s Hospital
Harvard Medical School
Martinos Center for Biomedical Imaging, Massachusetts General Hospital