Interactive effects of affective experiences and ageing processes on brain networks over the life course: a neuroimaging informatics approach

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Background: Stressful experiences are considered the main risk factor for most non-communicable diseases and are suspected to accelerate ageing. However, how different affective experiences affect the brain, how these effects accumulate over lifetime and how they interact with ageing processes is largely unknown. The goal of this PhD studentship is to answer these questions. Human studies are limited by self-report, retrospective assessment of past experiences, and the difficulty of running randomised longitudinal studies determining whether brain characteristics identified are the cause or the consequence of the exposure to stress. The similarity of non-human primates to humans in terms of brain connectivity, social behaviour, and hypothalamic-pituitary-adrenal (HPA) axis development make them the model of choice to overcome the limitations of human studies, and answer the fundamental questions about stressful experiences. Objectives and Experimental Approach: This studentship will take advantage of our on-going longitudinal database unique in the world that combines biannual structural (T1, T2 and DTI) and functional (resting-state) MRI scans of healthy macaque brains, video recordings of home-cage behaviour, blood and hair samples and detailed records of health and exposures to scientific and husbandry procedures. The student will first characterise along two orthogonal dimensions, arousal and valence (positive versus negative), the affective experiences of macaques induced by procedures randomly assigned to animals, using complementary approaches based on blood and MRI biomarkers, behaviour, and cortisol levels. The PhD student will then assess how both ageing and different affective experiences modify structural and functional brain networks over time, and whether affective experiences accelerate or decelerate ageing effects. This will be done using a within-subject longitudinal approach, by applying cutting-edge statistical and computational methods to our MRI datasets, combining approached from fractal geometry, topology, graph theory, and predictive modelling. Finally, these results will also be compared to brain networks of human patients suffering from affective disorders, identified from large publicly available databases. Impact: This project will identify brain networks modified by affective experiences and will test the hypothesis that experiences can accelerate or decelerate brain ageing. This knowledge can then be used to assess the efficiency of new interventions at normalizing these networks in affective disorders where these networks are abnormal.