Sleep/wake cycle and glymphatic system in early aging : an actigraphic and multimodal MRI study

Human neuroimaging studies of the last decades have revealed that the accumulation of metabolic waste in the brain begins around the age of 50, is highly heterogeneous from one subject to another and could be at the root of neurodegenerative diseases. Sleep/wake cycle quality during this critical period could be major determinants of this heterogeneity. Importantly, there is mounting evidence that waste clearance of the brain follows a sleep/wake cycle. Sleep/wake cycle disturbances in early old age could be associated with a disruption in the brain's clearance system, leading to an accumulation of waste products such as amyloid and tau proteins, thereby increasing the risk of developing Alzheimer's disease. Using innovative MRI sequences coupled with deep learning post-processing methods, recently described MRI markers for estimating both the anatomical and functional integrity of the brain's clearance system will be examined: anatomical features include choroid plexus volumes, arachnoid granulations in the dural parasagittal space, and perivascular space burden. The glymphatic functions are examined with two novel measures, DTI-ALPS from diffusion MRI and the coupling of CSF-BOLD oscillations based on resting-state fMRI. This comprehensive assessment will be analysed in relation to sleep/wake cycle disturbances objectively assessed in the context of daily life using actimetry within a unique cohort (Bcube, 500 volunteers of 55 years old) designed to capture the early stages of the brain ageing continuum. More specifically, we hypothesise that a disturbed sleep/wake cycle in early older age may be associated with an impairment of glymphatic system activity at this critical period, that could contribute to a higher risk of developing AD later on.

A core objective of our research is to examine the impact of sleep/wake disturbances on daytime functioning to elucidate the etiology and pathophysiology of age-related CNS disorders. Cognitive symptoms and physiological malfunctioning are assessed in clinical studies but also in population-based cohorts. We apply advanced neuroimaging techniques using multimodal approach coupling morphological and functional MRI techniques and dailylife assessment based on connected devices for sleep/wake cycle (actigraphic watch) and cognition (EMA on smart phone). More specifically, daily life evaluations unravel the intra-individual variability of physiological and cognitive functioning; we attempt to understand its anatomical and functional bases by analysing it in relation to brain networks.

A Master’s degree (or equivalent) in Biology, Neuroscience, or a related field. Some computer science aptitude combined with human neuroanatomy needed for preprocessing and analysis of MRI data. Candidates should have an interest in using programming langage (MATLAB and/or Python) and some interest in interdisciplinary research at the interface of computer science, cognitive science and medicine. Basic knowledge in standard linear statistical models and machine learning is preferred.