Dynamic Cerebral Autoregulation, Neurovascular Coupling and Cerebral Haemodynamic Parameters: Clinical Applications

Background: Dynamic cerebral autoregulation (dCA) and neurovascular coupling (NVC) are the main mechanisms that regulate cerebral blood flow. There is a need to improve methods of assessment and also to study their interaction.

Objectives: This thesis aimed to advance methods for assessing autoregulatory and neuro-vascular processes and study the interaction of these regulatory mechanisms in health and disease. For this purpose, both time- and frequency-domain methods were explored aiming to improve their sensitivity to detect changes in cognitive load and autoregulatory performance.

Methods: Data comprised retrospective and prospective studies involving non-invasive measurements of cerebral blood flow velocity, blood pressure and end-tidal carbon dioxide in healthy subjects and patients with mild-to-moderate ischaemic stroke, mid cognitive impairment, and Alzheimer’s disease.

Results: i) in 16 healthy subjects (8 males, age 31.6±11.6 years), similar CBFV temporal patterns were seen with varying complexity and duration of cognitive paradigms, but a trend for critical closing pressure to alter in respect of task complexity; ii) a shorter 3-minute duration recording analysis can detect cerebral-autoregulatory impairment in 79 ischaemic strokes (55 males, age 67.5±12.2 years), compared with 78 healthy controls (41 males, age 52.0±16.3 years); iii) cerebral autoregulation was depressed by neural stimulation with fluency and attention domain tasks in younger adults and memory and language tasks in older adults, but not additionally affected in 22 patients with mild cognitive impairment (18 males, age 72.3±8.5 years) and 35 with Alzheimer’s disease (23 males, age 72.2±8.9 years) compared to 30 healthy older adults (17 males, age 67.0±8.6 years).

Conclusion: Modifying common cognitive paradigms with varying complexity and duration could not provide scalability in neurovascular coupling responses, but analysis of subcomponents potentially reflects metabolic demand. Many future avenues of research follow from this work, including different study populations, types of cognitive paradigms and modelling approaches.