ArticleScientific reports2020
Assessment of cerebral autoregulation indices - a modelling perspective.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reliability and validity of the mean flow index (Mx) for assessing cerebral autoregulation in humans: A systematic review of the methodology.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2022Pooled it
- Longitudinal Kinetics of Cerebrospinal Fluid and Serum S100B in Patients with Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage.International journal of molecular sciences · 2026Observational
- Toward comprehensive multimodal neuromonitoring of surrogates of cerebral autoregulation in traumatic brain injury: insights from a hyperventilation protocol.Neurophotonics · 2026Article
- Bilateral dynamic cerebral autoregulation assessment during endovascular treatment in large-vessel occlusion stroke.Experimental physiology · 2026Article
- Spinal cord vascular autoregulation: key concepts and opportunities to improve management.Spinal cord · 2026Review
- Dynamic cerebral autoregulation during and 3 months after endovascular treatment in large vessel occlusion stroke.Scientific reports · 2025Observational
- Impaired Cerebral Autoregulation in Children.Pediatric neurology · 2025Review
- Cerebral autoregulation in patients with acute lacunar infarction: a reliable predictor of outcome.Annals of clinical and translational neurology · 2025Article
- Payload State Prediction Based on Real-Time IoT Network Traffic Using Hierarchical Clustering with Iterative Optimization.Sensors (Basel, Switzerland) · 2024Article
- Brain Ultrasonography in Critically Ill Septic Patients: A Scoping Review.Journal of clinical medicine · 2024Article
- Myths and methodologies: Assessment of dynamic cerebral autoregulation by the mean flow index.Experimental physiology · 2024Article
- Mathematical modelling of cerebral haemodynamics and their effects on ICP.Brain & spine · 2024Article
- Cerebral autoregulation: every step counts.Critical care (London, England) · 2023Article
- Reduced-order modeling and analysis of dynamic cerebral autoregulation via diffusion maps.Physiological measurement · 2023Article
- Venous dynamics in anesthetized sheep govern postural-induced changes in cerebrospinal fluid pressure comparable to those in humans.Physiological reports · 2022Article
- Reliability of cerebral autoregulation using different measures of perfusion pressure in patients with subarachnoid hemorrhage.Physiological reports · 2022Article
- Impaired dynamic cerebral autoregulation is associated with the severity of neuroimaging features of cerebral small vessel disease.CNS neuroscience & therapeutics · 2022Article
- Cerebral Autoregulation Indices Are Not Interchangeable in Patients With Sepsis.Frontiers in neurology · 2022Article
- Real-Time Evaluation of Cerebral Autoregulation Based on Near-Infrared Spectroscopy to Predict Clinical Outcome after Bypass Surgery in Moyamoya Disease.BioMed research international · 2022Article
- Reliability, reproducibility and validity of dynamic cerebral autoregulation in a large cohort with transient ischaemic attack or minor stroke.Physiological measurement · 2020Article
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9 authors.
Funding
Abstract
Various methodologies to assess cerebral autoregulation (CA) have been developed, including model - based methods (e.g. autoregulation index, ARI), correlation coefficient - based methods (e.g. mean flow index, Mx), and frequency domain - based methods (e.g. transfer function analysis, TF). Our understanding of relationships among CA indices remains limited, partly due to disagreement of different studies by using real physiological signals, which introduce confounding factors. The influence of exogenous noise on CA parameters needs further investigation. Using a set of artificial cerebral blood flow velocities (CBFV) generated from a well-known CA model, this study aims to cross-validate the relationship among CA indices in a more controlled environment. Real arterial blood pressure (ABP) measurements from 34 traumatic brain injury patients were applied to create artificial CBFVs. Each ABP recording was used to create 10 CBFVs corresponding to 10 CA levels (ARI from 0 to 9). Mx, TF phase, gain and coherence in low frequency (LF) and very low frequency (VLF) were calculated. The influence of exogenous noise was investigated by adding three levels of colored noise to the artificial CBFVs. The result showed a significant negative relationship between Mx and ARI (r = -0.95, p < 0.001), and it became almost purely linear when ARI is between 3 to 6. For transfer function parameters, ARI positively related with phase (r = 0.99 at VLF and 0.93 at LF, p < 0.001) and negatively related with gain_VLF(r = -0.98, p < 0.001). Exogenous noise changed the actual values of the CA parameters and increased the standard deviation. Our results show that different methods can lead to poor correlation between some of the autoregulation parameters even under well controlled situations, undisturbed by unknown confounding factors. They also highlighted the importance of exogenous noise, showing that even the same CA value might correspond to different CA levels under different 'noise' conditions.
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