This project aims to investigate the dynamic functional connectivity between the cerebellum and the hippocampus in patients with schizophrenia. The student will understand and explore cerebellum-hippocampus dynamic functional connectivity, and how it relates to the distinct symptoms of schizophrenia (negative, positive and cognitive) in a longitudinal cohort working closely with experts in dynamic functional connectivity methods. Additionally, the student will assist in data acquisition for a randomized controlled trial, which includes neuroimaging data, clinical assessment, and transcranial magnetic stimulation (TMS), a non-invasive neurostimulation technique that has shown promise in improving symptoms of neuropsychiatric disorders.
Required Skills and Background
Preferred background in clinical fields (e.g., psychology, medicine or equivalent).
Applicants from other disciplines with relevant skills and interests are also welcome.
Strong interest in clinical neurosciences and psychiatry, particularly schizophrenia
Experience with statistics and programming language (MATLAB, Python, or R), or willingness to learn
Ideally, prior experience with clinical work and data acquisition in human populations
Proficiency in French is required, especially for data acquisition
Familiarity with neuroimaging techniques is a plus Proactive attitude
Transcranial Magnetic Stimulation (TMS) is a non-invasive neurostimulation technique that has shown promise in improving symptoms of neuropsychiatric disorders. Cortical dosing of TMS is an emerging field with immense potential for personalization of non-invasive stimulation protocols. In the proposed project, we aim to optimize TMS stimulation protocols by investigating different approaches to personalized electrical field (E-field) dosing computation for the cerebellum.
The student will work closely with neuroimaging and computational scientists to apply computational methods for electrical field dosing using brain models derived from MRI scans in schizophrenia. He/she will adopt a proactive approach to conducting thorough literature reviews, aiming to comprehend current research and identify gaps in the field. He/she will use statistical techniques for data analysis for brain circuit therapy in schizophrenia.
Required Skills and Background
Preferred background: Mathematics, Computer Science, Medicine, Psychology
Good programing skills in Python and MATLAB or willingness to learn
Interest in neuropsychiatric disorders, particularly schizophrenia
Good understanding of brain anatomy and MRI data processing or willingness to learn
Good knowledge of statistical methods for data analysis
Proficiency in French is a plus
Proactive attitude
In neuroimaging, magnetic resonance imaging (MRI) acquisitions increasingly comprise diffusion MRI (dMRI), to infer the strength of physical wiring between brain regions, and functional MRI (fMRI), to track regional brain activity over time. Graph signal processing (GSP) is an emerging data science field, drawing on linear algebra and graph theory, with potential to jointly study brain structure and function.
This project seeks to develop novel clinically relevant GSP concepts by focusing on the cerebellum, a hallmark brain structure with deep implications for many brain disorders. The student will first gain familiarity with existing GSP notions, primarily devised to operate at the level of the cortex. Second, he/she will propose novel theoretical concepts to leverage GSP to cerebellar data. Third, he/she will benchmark them on a combined dMRI/fMRI dataset and apply them to contrast healthy controls and patients with schizophrenia in a separate dataset. Supervision will be provided by a GSP expert, also with input from clinical neuroscientists.
Required Skills and Background
Strong interest in mathematics (especially linear algebra and/or graph theory) and data science
Curiosity towards clinical neurosciences and psychiatry, particularly schizophrenia
Experience with statistics and programming languages (MATLAB, Python, or R)
Familiarity with neuroimaging techniques is appreciated but not mandatory
Proactivity, willingness to learn and “try again, fail again, fail better” attitude