  Heart rate variability (HRV), defined as the variability between consecutive
heartbeats, is a surrogate measure of cardiac vagal tone. It is widely accepted
that a decreased HRV is associated to several risk factors and cardiovascular
diseases. However, a possible association between HRV and altered cerebral
hemodynamics is still debated, suffering from HRV short-term measures and the
paucity of high-resolution deep cerebral data. We propose a computational
approach to evaluate the deep cerebral and central hemodynamics subject to
physiological alterations of HRV in an ideal young healthy patient at rest. The
cardiovascular-cerebral model was validated and recently exploited to
understand the hemodynamic mechanisms between cardiac arrythmia and cognitive
deficit. Three configurations (baseline, increased HRV, and decreased HRV) are
built based on the standard deviation (SDNN) of RR beats. In the cerebral
circulation, our results show that HRV has overall a stronger impact on
pressure than flow rate mean values but similarly alters pressure and flow rate
in terms of extreme events. By comparing reduced and increased HRV, this latter
induces a higher probability of altered mean and extreme values, and is
therefore more detrimental at distal cerebral level. On the contrary, at
central level a decreased HRV induces a higher cardiac effort without improving
the mechano-contractile performance, thus overall reducing the heart
efficiency. Present results suggest that: (i) the increase of HRV per se does
not seem to be sufficient to trigger a better cerebral hemodynamic response;
(ii) by accounting for both central and cerebral circulations, the optimal HRV
configuration is found at baseline. Given the relation inversely linking HRV
and HR, the presence of this optimal condition can contribute to explain why
the mean HR of the general population settles around the baseline value (70
bpm).
