Postdoctoral position – 2024 Microstructural analysis in late-life depression using diffusion MRI multi-compartments models and tractometry
Background and mission :
To advance in the understanding of apathy physiopathology in LLD, we conducted a study which
evaluated the relationship between patterns of motor activity measured by actigraphy, and brain modifications of white matter microstructure.
This study found two patterns of motor activity associated with apathy: a reduced diurnal mean activity, and an early chronotype pattern.
These patterns of motor activity were associated with modified intra-network resting-state functional connectivity in key regions associated with the default-mode, the cingulo-opercular and the frontoparietal network. However, our preliminary work on microstructure metrics estimated from diffusion weighted imaging did not find significant associations between microstructural metrics of white matter and patterns of motor activity after adjustment for multiple. To detect more subtle links such as those between patterns of motor activity and microstructure, our approach needs to be improved.
This project will focus on two major subjects:
– Developing a more accurate estimation and projection of microstructure metrics along the
fiber as well as a new statistical method taking into account the shape complexity of the fibers.
– Extracting more accurate markers of patterns of motor activity measured by actigraphy
The developed approach will be tested on a cohort of patients suffering from late-life depression, with the aim of better estimating the microstructure and thus better understanding the neuronal modifications caused by this disease and apathy.
Diplomas required :
A good background in neuroimaging analysis and signal processing, good knowledge of computer science aspects, especially in Python
Skills required:
Candidates strongly motivated by challenging research topics in Neuroimaging and clinical projects. The applicant should present a good background in neuroimaging analysis and signal processing, good knowledge of computer science aspects, especially in Python.
Structure :
Inria/IRISA, UMR CNRS 6074, Empenn U1228