LONGIDEP is a routine care cohort of patients suffering from mood depressive disorder (MDD) who underwent clinical, neuropsychological and imaging. The aims of this study are 1/ to identify clinical and imaging markers (morphological and arterial spin labeling-resting state perfusion) which are predictive of pejorative outcome in MDD and 2/ to identify pathophysiological processes involved in MDD at different resistance stages in order to better characterize them.
The objective of this post-doc position will be to use available and develop new image processing methods in order to identify imaging biomarkers that can correlate indices coming from the imaging data with clinical scores. The post-doc will work on the LONGIDEP clinical protocol that measures both structural state of the brain using a combination of MRI sequences such as Arterial Spin Labeling and high resolution diffusion MRI (for multi-compartment diffusion imaging).
This will require software integration for:
1. Image processing of morphological data (voxel-based morphometry, anatomical connectivity based on DTI), arterial spin labeling (pulsed and pseudo-continuous), individual imaging patterns. This part will be driven by the daily close collaboration between clinicians and post-doctoral researcher.
2. Data analysis for the study of functional ASL-based connectivity.
From a methodological perspective, this work will deal with
– registration between modalities, (intra- and inter-subjects)
– segmentation of the brain compartments
– quantification of brain perfusion and image artefacts correction
– modeling of diffusion MRI data
– statistical comparisons between imaging and clinical scores
The post-doc will work in close collaboration with PhD students, already working on the project and in charge of recruiting the control subjects and patients. At least two articles are expected from this research program.
Duration: 1 year with possible renewal
Keywords: Medical Image Processing, Neuroimaging, Statistical Analysis
This work will require strong knowledge in the fields of information sciences (statistics, optimization), and image processing (image segmentation, registration…). A PhD thesis in one of those fields will thus be required.
A good knowledge of computer science tools will also be required, especially in object oriented programming (C++), Matlab or python.
around 2650 € / month according to experience
This postdoc position is open in a unique research environment where engineers in MR Physics, researchers in medical image processing and clinicians in psychiatry and neuro-imaging work closely together and share an MRI research facility.
Depression is a debilitating illness which course is frequently recurrent. This disease affects more than 350 million people around the world (World Health Organization, 2012) with a lifetime prevalence in the range of 10% to 15%. It is identified as a leading cause of burden with high disability in every day life. The risk of recurrence in specialized medical care after 15 years is estimated around 85%. Two major factors are involved in the risk of recurrence: the number of previous episodes and the persistence of inter-critical residual symptoms. The most frequents ones are anxiety, sadness, psychiatric co-morbidities. It is now well known that residual symptoms are associated with increased disability (job loss, social impairment) and a higher rate of relapse/hospitalization. Nevertheless, it is still challenging to detect such clinical profiles at risk of treatment-resistant depression (TRD).
During the last decade, the field of neuroscience has investigated the function and the morphology of the brain of patients suffering from depression and specifically TRD. Some brain networks (fronto-striato-limbic) have been identified as key structures that could be involved in therapeutic resistance.
