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The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

The classic dynamic autoregulatory index (ARI), proposed by Aaslid and Tiecks, is one of the most widely used methods to assess the efficiency of dynamic cerebral autoregulation. Although this index is often used in clinical research and is also included in some commercial equipment, it exhibits considerable intra-subject variability, and has the tendency to produce false positive results in clinical applications. An alternative index of dynamic cerebral autoregulation is proposed, which overcomes most of the limitations of the classic method and also has the advantage of being model-free. This new index uses two parameters that are obtained directly from the response signal of the cerebral blood flow velocity to a transient decrease in arterial blood pressure provoked by the sudden release of bilateral thigh cuffs, and a third parameter measuring the difference in slope of this response and the change in arterial blood pressure achieved. With the values of these parameters, a corresponding classic autoregulatory index value could be calculated by using a linear regression model built from theoretical curves generated with the Aaslid-Tiecks model. In 16 healthy subjects who underwent repeated thigh-cuff manoeuvres, the model-free approach exhibited significantly lower intra-subject variability, as measured by the unbiased coefficient of variation, than the classic autoregulatory index (p = 0.032) and the Rate of Return (p

A novel method is described for mapping dynamic cerebral blood flow autoregulation to assess autoregulatory efficiency throughout the brain, using magnetic resonance imaging (MRI). Global abnormalities in autoregulation occur in clinical conditions, including stroke and head injury, and are of prognostic significance. However, there is limited information about regional variations. A gradient-echo echo-planar pulse sequence was used to scan the brains of healthy subjects at a rate of 1 scan/second during a transient decrease in arterial blood pressure provoked by a sudden release of pressure in bilateral inflated thigh cuffs. The signal decrease and subsequent recovery were analyzed to provide an index of autoregulatory efficiency (MRARI). MRI time-series were successfully acquired and analyzed in eleven subjects. Autoregulatory efficiency was not uniform throughout the brain: white matter exhibited faster recovery than gray (MRARI = 0.702 vs. 0.672, p = 0.009) and the cerebral cortex exhibited faster recovery than the cerebellum (MRARI = 0.669 vs. 0.645, p = 0.016). However, there was no evidence for differences between different cortical regions. Differences in autoregulatory efficiency between white matter, gray matter and the cerebellum may be a result of differences in vessel density and vasodilation. The techniques described may have practical importance in detecting regional changes in autoregulation consequent to disease.