Cerebrovascular responses to somatomotor stimulation in Parkinson's disease: A multivariate analysis.
Samuel C BarnesRonney B PaneraiLucy BeishonMartha HanbyThompson G RobinsonVictoria J HauntonPublished in: Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism (2022)
Parkinson's disease (PD) is a common neurodegenerative disorder, yet little is known about cerebral haemodynamics in this patient population. Previous studies assessing dynamic cerebral autoregulation (dCA), neurovascular coupling (NVC) and vasomotor reactivity (VMR) have yielded conflicting findings. By using multi-variate modelling, we aimed to determine whether cerebral blood flow (CBF) regulation is impaired in PD patients.55 healthy controls (HC) and 49 PD patients were recruited. PD subjects underwent a second recording following a period of abstinence from their anti-Parkinsonian medication. Continuous bilateral transcranial Doppler in the middle cerebral arteries, beat-to-beat mean arterial blood pressure (MAP; Finapres), heart rate (HR; electrocardiogram), and end-tidal CO 2 (EtCO 2 ; capnography) were measured. After a 5-min baseline period, a passive motor paradigm comprising 60 s of elbow flexion was performed. Multi-variate modelling quantified the contributions of MAP, ETCO 2 and neural stimulation to changes in CBF velocity (CBFV). dCA, VMR and NVC were quantified to assess the integrity of CBF regulation.Neural stimulation was the dominant input. dCA, NVC and VMR were all found to be impaired in the PD population relative to HC (p < 0.01, p = 0.04, p < 0.01, respectively). Our data suggest PD may be associated with depressed CBF regulation. This warrants further assessment using different neural stimuli.
Keyphrases
- heart rate
- cerebral blood flow
- blood pressure
- end stage renal disease
- ejection fraction
- newly diagnosed
- heart rate variability
- subarachnoid hemorrhage
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- healthcare
- type diabetes
- blood flow
- emergency department
- case report
- brain injury
- skeletal muscle
- machine learning
- hypertensive patients
- patient reported outcomes
- big data