|| Multimodal Pressure-Flow
(MMPF) Analysis of
Dynamic Cerebral Autoregulation
Cerebral autoregulation reflects the ability of the cerebral
microvasculature to adapt to systemic blood pressure (BP) changes by
modulating the small vessel resistance to maintain relatively
stable blood flow. Noninvasive assessment of cerebral
vasoregulation is important for medical diagnostics and acute
care. Recent studies have demonstrated that beat-to-beat measurements
of BP and cerebral blood flow velocities (BFV) measured by
transcranial Doppler ultrasound (TCD) during the Valsalva maneuver and
head-up tilt can identify impairment of cerebral vasoreactivity in
various medical conditions; indicating that a reliable, non-invasive index for
dynamic cerebral autoregulation may be extracted from the BP and BFV
Conventional approaches model autoregulation with BP as input and
blood flow as output (e.g., Windkessel models) and assume that signals
are composed of superimposed sinusoidal oscillations of constant
amplitude and period over a pre-selected frequency range. A transfer
function is typically used to explore the relationship between BP and
BFV by calculating gain and phase shift between their
spectra. However, BP and BFV signals are often nonstationary, and are
modulated by nonlinearly interacting processes at multiple time-scales
corresponding to the beat-to-beat systolic pressure, respiration,
spontaneous BP fluctuations, and those induced by interventions such
as the Valsalva maneuver and postural changes. To overcome problems
related to nonstationarity and nonlinearity, Novak et al. 
recently developed a novel computational method, the
Multimodal Pressure-Flow (MMPF) technique to analyze the
BP-BFV relationships during the Valsalva maneuver. Unlike conventional
approaches that are based on the Fourier transform and thus require
linearity and stationarity of the signals, the MMPF analysis does not
rely on these assumptions.
Instead, the MMPF technique evaluates autoregulatory dynamics based
on instantaneous phase analysis of BP and BFV oscillations. The MMPF
analysis applies the Empirical Mode Decomposition (EMD) algorithm
proposed by Huang et al.  to decompose complex BP and BFV
signals into multiple empirical modes. Each mode represents a
frequency-amplitude modulation in a narrow frequency band that can be
related to a specific physiologic process. As a result, MMPF analysis
not only can be applied to protocols such as the Valsalva maneuver and
sit-to-stand conditions, where large BP and BFV oscillations were
induced by the interventions, but can also be used to study
spontaneous oscillations of BP and BFV under resting (baseline)
How does the MMPF Algorithm Work?
The above figure is an illustration of the MMPF procedure. Continuous BP and BFV
signals (panels in the 1st row) of a healthy subject collected under
baseline supine conditions are used for this example. The dominant
oscillations of BP and BPV, due to physiologic breathing can be
extracted by the EMD algorithm (panels in the 2nd row). These two
oscillatory modes can then be compared with each other as shown in the
3rd row panel. Note that the oscillatory component of the BFV (blue
curve) consistently leads that of the BP (red curve). This phase
relationship is an important marker of healthy autoregulation. The
instantaneous phases of these two oscillations can be calculated by
the Hilbert transform, and their difference is shown in the bottom
panel. As apparent in this 2-minutes period, the phase shift between
these two oscillations varies around an average value of 67 degrees
(indicated by a dark green dashed line). Note that the example shown
here reveals a relatively slow respiratory period (cycle length ~ 7
sec.). Typical subjects have a faster breathing rate; however, similar
BFV/BP phase shift behavior is observed. In contrast, pathologic impairments
of dynamic cerebral autoregulation can significantly reduce this phase shift.
Therefore, the phase shift index may serve as a sensitive biomarker of autoregulation.
Obtaining a reliable index of dynamic cerebral autoregulation is
useful under many clinical conditions. Impairment of vascular
reactivity to regulate cerebral perfusion has been found in many
syndromes associated with aging, hypertension, stroke, diabetes,
dementia and traumatic brain injury (TBI). For example, autoregulation
estimates based on BFV and direct measurements of cerebral perfusion
pressure (CPP) have shown predictive value for determining outcomes in
TBI patients. Therefore, the MMPF analysis may play an important role
to assess and monitor dynamic cerebral autoregulation in a wide range
of clinical settings [1,3,4].
||DynaDx provides an online demonstration of the MMPF data analysis. Please try it out. If you have any questions or would like to have batch data processing service, please contact firstname.lastname@example.org.
There are many research papers on dynamic cerebral autoregulation.
Here we provide links to some key articles related to the MMPF
1. Novak V, Yang ACC, Lepicovsky L, Goldberger AL, Lipsitz LA,
Peng C-K. Multimodal pressure-flow method to assess dynamics of
cerebral autoregulation in stroke and hypertension.
Biomed. Eng. Online 2004;3:39.
2. Huang NE, Shen Z, Long SR, et al. The
empirical mode decomposition method and the Hilbert spectrum for
non-stationary time series analysis. Proc. Roy. Soc. London
3. Hu K, Peng C-K, Huang NE, Wu Z, Lipsitz LA, Cavallerano J,
Novak V. Altered phase interactions between spontaneous blood
pressure and flow fluctuations in type 2 diabetes mellitus: Nonlinear
assessment of cerebral autoregulation. Physica A
4. Hu K, Peng C-K, Czosnyka M, Zhao P, Novak V. Nonlinear assessment of cerebral autoregulation from
spontaneous blood pressure and cerebral perfusion pressure
fluctuations. Cardiovasc. Eng. 2008;8:60-71.
5. Peng C-K, Lo M-T, Hu K, Liu YH, Novak V. Multimodal Pressure Flow Analysis Enables Assessment of Cerebral Autoregulation Dynamics During Baseline Resting Conditions. EURASIP Journal on Advances in Signal Processing. 2008 (in press).