Changes in Linear Dynamics of Cerebrovascular System After Severe Traumatic Brain Injury

Abstract

Background and Purpose-We sought to describe the dynamic changes in the cerebrovascular system after traumatic brain injury by transfer function estimation and coherence. Methods-In 42 healthy volunteers (meanϮSD age, 37Ϯ17 years; range, 17 to 65 years), spontaneous fluctuations of middle cerebral artery blood flow velocity and of finger blood pressure (BP) were simultaneously recorded over a period of 10 minutes under normocapnic and hypocapnic conditions to generate normative spectra of coherence, phase shift, and gain over the frequency range of 0 to 0.25 Hz. Similar recordings were performed in 24 patients with severe traumatic brain injury (Glasgow Coma Scale score Յ8; meanϮSD age, 50Ϯ20 years) serially on days 1, 3, 5, and 8 after trauma. Cranial perfusion pressure was kept at Ͼ70 mm Hg. Each blood flow velocity/BP recording was related to the presence or absence of middle cerebral artery territory brain parenchyma lesions on cranial CT performed within a close time frame. Results-In

Dynamic cerebral autoregulation during brain activation paradigms

“…As dynamic CA becomes less efficient, the return of CBFV slows and often does not reach the original baseline level. Clinical conditions where CA could be expected to be impaired, such as carotid artery disease (14,35), subarachnoid hemorrhage (18), severe head injury (22,33), ischemic stroke (17), MCA stenosis (13), autonomic failure (3,42), malignant hypertension (16), and neonatal prematurity (25,39), support the predicted changes in coherence, gain, phase, and CBFV step responses described above. Similar behavior was also observed during hypercapnia, which is known to reduce the efficiency of static and dynamic CA (1,11,26,34).…”

Asymmetric Dynamic Cerebral Autoregulatory Response to Cyclic Stimuli

“…Two previous studies have suggested that there are nonlinear factors in cerebral hemodynamic. 4,5 In the first article, 4 nonlinearity was assumed attributable to poor fit of linear models to the observed data. In the second article, 5 the nonlinear behavior was inferred from the shape of the coherence, phase, and amplitude characteristics.…”

Response of Blood Perfusion at ST 36 Acupoint after Drinking Cold Glucose or Saline Injection

“…To calculate the coherence between left and the right, the coherence value was estimated by [24, 25]$\begin{array}{c}T1C_{xy}\left(f\right)=\frac{{\left|P_{xy}\left(f\right)\right|}^{\mathrm{2}}}{P_{xx}\left(f\right)P_{yy}\left(f\right)},\end{array}$where P xy ( f ) = ∫ − ∞ ∞ R xy ( t ) e − jft dt ; thus P xy ( f ) is the Fourier transform of the R xy ( R xy is the cross-correlation of x and y ). Equation (2) obtains the magnitude squared coherence estimate C xy ( f ) of the input signals x and y using Welch's averaged, modified periodogram method.…”