Dynamic cerebral autoregulation (CA) is the transient response of cerebral blood flow (CBF) to rapid blood pressure changes: it improves in hypocapnia and becomes impaired during hypercapnia. Batch-processing techniques have mostly been used to measure CA, providing a single estimate for an entire recording. A new approach to increase the temporal resolution of dynamic CA parameters was applied to transient hypercapnia and hypocapnia to describe the time-varying properties of dynamic CA during these conditions. Thirty healthy subjects (mean +/- SD: 25 +/- 6 yr, 9 men) were recruited. CBF velocity was recorded in both middle cerebral arteries (MCAs) with transcranial Doppler ultrasound. Arterial blood pressure (Finapres), end-tidal CO(2) (ET(CO(2)); infrared capnograph), and a three-lead ECG were also measured at rest and during repeated breath hold and hyperventilation. A moving window autoregressive moving average model provided continuous values of the dynamic CA index [autoregulation index (ARI)] and unconstrained gain. Breath hold led to significant increase in ET(CO(2)) (+5.4 +/- 6.1 mmHg), with concomitant increase in CBF velocity in both MCAs. Continuous dynamic CA parameters showed highly significant changes (P < 0.001), with a temporal pattern reflecting a delayed dynamic response of CA to changes in arterial Pco(2) and a maximal reduction in ARI of -5.1 +/- 2.4 and -5.1 +/- 2.3 for the right and left MCA, respectively. Hyperventilation led to a marked decrease in ET(CO(2)) (-7.2 +/- 4.1 mmHg, P < 0.001). Unexpectedly, CA efficiency dropped significantly with the inception of the metronome-controlled hyperventilation, but, after approximately 30 s, the ARI increased gradually to show a maximum change of 5.7 +/- 2.9 and 5.3 +/- 3.0 for the right and left MCA, respectively (P < 0.001). These results confirm the potential of continuous estimates of dynamic CA to improve our understanding of human cerebrovascular physiology and represent a promising new approach to improve the sensitivity of clinical applications of dynamic CA modeling.