Dear all, Please find below the description for an available position at INS in Marseille, France: Main contact : Dr Martin Breyton martin.breyton@univ-amu.fr<mailto:martin.breyton@univ-amu.fr> Additional contacts : romain.guilhaumou@ap-hm.fr<mailto:romain.guilhaumou@ap-hm.fr>, spase.petkoski@univ-amu.fr<mailto:spase.petkoski@univ-amu.fr> REPORTS (bRain modEling and PharmacOclinical Response To Schizophrenia) Description: Schizophrenia affects approximately 0.7 to 1% of the world's population. Although there is a large available therapeutic panel, the clinical effectiveness of the antipsychotics used remains limited with 30 to 50% of schizophrenic patients showing an insufficient response to treatment. At the pharmacological level, drug response results from the interaction of genetic (e.g. drug-metabolizing enzymes, drug transporters, drug targets), personal (e.g. age, sex, disease states, treatment adherence) and environmental factors (e.g. smoking, diet, alcohol habits, drug-drug interactions) that produce interindividual differences in terms of pharmacokinetics and pharmacodynamics (de Leon et al., 2009). Currently, only fluid biomarkers are available to optimize exposure to antipsychotics (Therapeutic drug monitoring, TDM and Pharmacogenetics, PGx), but no biomarker is available to optimize the next steps, i.e, the interaction with the receptors, the signal transduction and finally the translation to clinical effect. To fill this void, neuroimaging is a strong candidate and a number of plausible neuroimaging biomarkers have been identified (Kraguljac et al. 2021). Indeed, a modulation of brain connectivity related to response to antipsychotic medication has been described (Mehta et al. 2021). Understanding this modulation thanks to modeling will allow an early stratification of patients before treatment and then an optimization of the treatment for each individual patient (allowing more reliable as well as earlier optimized management). Available data: 100 schizophrenic patients, pharmaco-clinical data, neuroimaging (T1, T2, DTI, rs-fMRI) We are looking for a young researcher (engineer, PhD) from Neurosciences to contribute to this project by performing/participating in the following tasks: * Management, preprocessing and analysis of the clinical database to identify sub-groups of patients at high risk of inadequate antipsychotic drug exposure * Preprocessing and analysis of the neuroimaging data: extraction of biomarkers and comparison between groups * Personalized brain modeling using The Virtual Brain (Sanz-Leon et al. 2015) The ideal candidate should be: * Proficient in Python programming * Familiar with neuro-imaging data * Familiar with computational neurosciences concepts and/or machine learning * Curious about schizophrenia and pharmacology * Have some prior experience in research (internship, publication…) The candidate would work in the Theoretical Neuroscience Group at the Institut de Neurosciences des Systèmes (INS, UMR1106, Marseille) under a research engineer contract or similar for 20 months. They will be supervised by a team of pharmacologists and computational neuroscientists. Depending on the quality of the collaboration, and on the advancement of the project, there will be possibilities to extend the collaboration and work on other related projects at INS. References: Kraguljac, Nina V., William M. McDonald, Alik S. Widge, Carolyn I. Rodriguez, Mauricio Tohen, et Charles B. Nemeroff. 2021. « Neuroimaging Biomarkers in Schizophrenia ». The American Journal of Psychiatry 178 (6): 509‑21. https://doi.org/10.1176/appi.ajp.2020.20030340. Leon, Jose de. 2009. « The Future (or Lack of Future) of Personalized Prescription in Psychiatry ». Pharmacological Research 59 (2): 81‑89. https://doi.org/10.1016/j.phrs.2008.10.002. Mehta, Urvakhsh Meherwan, Ferose Azeez Ibrahim, Manu S. Sharma, Ganesan Venkatasubramanian, Jagadisha Thirthalli, Rose Dawn Bharath, Nicolas R. Bolo, Bangalore N. Gangadhar, et Matcheri S. Keshavan. 2021. « Resting-State Functional Connectivity Predictors of Treatment Response in Schizophrenia – A Systematic Review and Meta-Analysis ». Schizophrenia Research 237 (novembre): 153‑65. https://doi.org/10.1016/j.schres.2021.09.004. Sanz-Leon, Paula, Stuart A. Knock, Andreas Spiegler, et Viktor K. Jirsa. 2015. « Mathematical framework for large-scale brain network modeling in The Virtual Brain ». NeuroImage 111 (mai): 385‑430. https://doi.org/10.1016/j.neuroimage.2015.01.002.