Longitudinal neuroimaging study of accelerated brain ageing in patients with post-operative delirium.

Abstract

Delirium is described as one of the mental manifestations of a pathophysiological condition called acute encephalopathy. It can take form in changes in mental abilities, such as lack of awareness, attention, and disorientation. It is prevalent in more older age individuals and is a major risk factor for a long-term cognitive impairment such as dementia and worsening existing dementia. A longitudinal pattern recognition neuroimaging study, aided with machine learning predictive tools, would capture, if any, the direct correlation between the apparent, biological age (brain age) of an individual and delirium. This form of data analysis could be conducted using patient data of non-delirium individuals and those afflicted with delirium to encapsulate the causal/ effect change in brain age with delirium. Thereby, investigation of the longitudinal brain change that is associated with delirium could be utilised to provide a better understanding of the mental disability and its long-term adverse effects. This study aims to generate a predictive brain age model using machine learning tools to elucidate accelerated brain ageing associated with delirium.