The validity of ARI to mirror dynamic changes in cerebral autoregulation was further examined by Panerai etal. [18] via Monte Carlo simulations that mimed random input and output signals of both CBFV and ABP over a 5-minute interval. As transfer function analysis is crucial to the calculation of ARI, the strength of the index is tied to its spectral components [18, 19]. ARI’s utility to gauge patient out­come is limited if the recorded signals have a low signal-to-noise ratio. For each harmonic, the amount of output power that can be linearly explained by the input power is expressed by the squared coherence function. A coherence of 1 for pure, univariate systems is indicative of high signal-to-noise ratio, whereas a coherence at or near 0 represents the latter [18, 19]. The phase shift between the Fourier compo­nents of both the input and the output signals reects the “interdependence” of CBFV and ABP, with a positive phase shift (optimally 90°) revealing the presence of an intact, non-passive autoregulatory reserve [19–21]. When applied to the Glasgow Outcome Score (GOS), a higher ARI is compatible with GOS 1 or 2 (favorable outcome), whereas a lower ARI implies the converse, GOS 3–5 (unfavor­able outcome) [19]. However, ARI is less sensitive when discriminating scores along the lower end of its 0–9 scale and is largely dependent on how accurately the template model [17, 22] matches the individual physiological events captured by TCD and ABP monitors.