Publications

Sleep quality and cognition vary as functions of lifestyle, genetics, and health. However, poor sleep quality is prevalent, reportedly affecting approximately 38% of the adult population. Poor sleep quality is a significant risk factor for mood disorders, a predictive factor in cognitive decline in later life, and sleep disturbance is a putative precursor to severe cognitive impairment and is predictive of dementia onset. The direct relationship between these factors and intrinsic neural function is poorly understood. Integrating neurophysiology, cognition, and sleep quality to reveal latent factors would help understand the neurophysiology of sleep disturbances and their relation to cognitive performance. Here, we used data from the Cam-CAN dataset with a partial least squares (PLS) approach, producing a multivariate cross-decomposition model to map resting-state magnetoencephalography (MEG) data and cognitive/sleep scores of healthy controls (n = 490, age 18-86).

Parkinson’s disease (PD) is a progressive neurodegenerative disorder which causes debilitating symptoms in both the motor and cognitive domains. The neurophysiological markers of PD include ‘oscillopathies’ such as diffuse neural oscillatory slowing, dysregulated beta band activity, and changes in interhemispheric functional connectivity; however, the relative importance of these markers as determinants of disease status is not clear. In this study, we used resting state magnetoencephalography data (n = 199 participants, 78 PD, 121 controls) from the open OMEGA repository to investigate changes in spectral power and functional networks in PD.

The functional repertoire of the human brain changes dramatically throughout the developmental trajectories of early life and even all the way throughout the adult lifespan into older age. Capturing this arc is important to understand healthy brain ageing, and conversely, how injury and diseased states can lead to accelerated brain ageing. Regression modelling using lifespan imaging data can reliably predict an individual’s brain age based on expected arcs of ageing.