Screening for Cognitive Impairment by Model Assisted Cerebral Blood Flow Estimation

Objective: Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disease; a major health concern in the ageing population with an estimated prevalence of 46 million dementia cases worldwide. Early diagnosis is therefore crucial so mitigating treatments can be initiated at an early stage. Cerebral hypoperfusion has been linked with blood-brain barrier dysfunction in the early stages of AD, and screening for chronic cerebral hypoperfusion in individuals has been proposed for improving the early diagnosis of AD. However, ambulatory measurements of cerebral blood flow are not routinely carried out in the clinical setting. In this study, we combine physiological modelling with Holter blood pressure monitoring and carotid ultrasound imaging to predict 24-hour cerebral blood flow (CBF) profiles in individuals. One hundred and three participants (53 with mild cognitive impairment (MCI), 50 healthy controls) underwent modelassisted prediction of 24-hour CBF. Model-predicted CBF and neuropsychological tests were features in lasso regression models for MCI diagnosis. Results: A CBF-enhanced classifier for diagnosing MCI performed better, area-under-the-curve (AUC) = 0.889 (95%-CI: 0.800 to 0.978), than a classifier based only on the neuropsychological test scores, AUC = 0.818 (95%- CI: 0.643 to 0.992). An additional cohort of 25 participants (11 MCI, 14 healthy) was recruited to perform model validation by arterial spin-labelling magnetic resonance imaging and to establish a link between measured CBF and that predicted by the model. Conclusion: Ultrasound imaging and ambulatory blood pressure measurements enhanced with physiological modelling can improve MCI diagnosis accuracy.