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Register a new userSimple exercises that significantly increase cerebral blood flow and cerebral oxygenation
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We tested the hypothesis that simple exercises may significantly increase cerebral blood flow (CBF) and/or cerebral oxygenation. Eighteen subjects ranging in age from nineteen to thirty nine participated in a four-stage study during which measurements of end tidal CO_2 (EtCO2 - by capnometer) and local brain oxygenation (by near-infrared spectroscopy (NIRS) sensor) were taken. The four stages were 1) baseline, 2) breathing exercises, 3) solving an arithmetic problem, and 4) biofeedback. During the breathing exercises there was a significant increase in EtCO2 indicating a significant increase in global CBF. The increase in global CBF was estimated on the basis of a theoretical model. During the arithmetic and biofeedback tasks there was a significant increase in the local (Fp1) oxygenation, but it varied between the different participants. The results may lead to new clinical applications of CBF and brain oxygenation monitoring and behavioral control. We foresee future more detailed investigations in the control of CO2 in brain circulation in specific regions of the brain involved in cognition and memory.
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Infrared light scattering methods have been developed and employed to non-invasively monitor human cerebral blood flow (CBF). However, the number of reflected photons that interact with the brain is low when detecting blood flow in deep tissue. To tackle this photon-starved problem, we present and demonstrate the idea of interferometric speckle visibility spectroscopy (ISVS). In ISVS, an interferometric detection scheme is used to boost the weak signal light. The blood flow dynamics are inferred from the speckle statistics of a single frame speckle pattern. We experimentally demonstrated the improvement of measurement fidelity by introducing interferometric detection when the signal photon number is insufficient. We apply the ISVS system to monitor the human CBF in situations where the light intensity is $sim$100-fold less than that in common diffuse correlation spectroscopy (DCS) implementations. Due to the large number of pixels ($sim 2times 10^5$) used to capture light in the ISVS system, we are able to collect a similar number of photons within one exposure time as in normal DCS implementations. Our system operates at a sampling rate of 100 Hz. At the exposure time of 2 ms, the average signal photon electron number is $sim$0.95 count/pixel, yielding a single pixel interferometric measurement signal-to-noise ratio (SNR) of $sim$0.97. The total $sim 2times 10^5$ pixels provide an expected overall SNR of 436. We successfully demonstrate that the ISVS system is able to monitor the human brain pulsatile blood flow, as well as the blood flow change when a human subject is doing a breath holding task.
Bubbles introduced to the arterial circulation during invasive medical procedures can have devastating consequences for brain function but their effects are currently difficult to quantify. Here we present a Monte-Carlo simulation investigating the impact of gas bubbles on cerebral blood flow. For the first time, this model includes realistic adhesion forces, bubble deformation, fluid dynamical considerations, and bubble dissolution. This allows investigation of the effects of buoyancy, solubility, and blood pressure on embolus clearance. Our results illustrate that blockages depend on several factors, including the number and size distribution of incident emboli, dissolution time and blood pressure. We found it essential to model the deformation of bubbles to avoid overestimation of arterial obstruction. Incorporation of buoyancy effects within our model slightly reduced the overall level of obstruction but did not decrease embolus clearance times. We found that higher blood pressures generate lower levels of obstruction and improve embolus clearance. Finally, we demonstrate the effects of gas solubility and discuss potential clinical applications of the model.
The impulse response function (IRF) of a localized bolus in cerebral blood flow codes important information on the tissue type. It is indirectly accessible both from MR- and CT-imaging methods, at least in principle. In practice, however, noise and limited signal resolution render standard deconvolution techniques almost useless. Parametric signal descriptions look more promising, and it is the aim of this contribution to develop some improvements along this line.
Preterm infants are at high risk of developing brain injury in the first days of life as a consequence of poor cerebral oxygen delivery. Near-infrared spectroscopy (NIRS) is an established technology developed to monitor regional tissue oxygenation. Detailed waveform analysis of the cerebral NIRS signal could improve the clinical utility of this method in accurately predicting brain injury. Frequent transient cerebral oxygen desaturations are commonly observed in extremely preterm infants, yet their clinical significance remains unclear. The aim of this study was to examine and compare the performance of two distinct approaches in isolating and extracting transient deflections within NIRS signals. We optimized three different simultaneous low-pass filtering and total variation denoising (LPF_TVD) methods and compared their performance with a recently proposed method that uses singular-spectrum analysis and the discrete cosine transform (SSA_DCT). Parameters for the LPF_TVD methods were optimized over a grid search using synthetic NIRS-like signals. The SSA_DCT method was modified with a post-processing procedure to increase sparsity in the extracted components. Our analysis, using a synthetic NIRS-like dataset, showed that a LPF_TVD method outperformed the modified SSA_DCT method: median mean-squared error of 0.97 (95% CI: 0.86 to 1.07) was lower for the LPF_TVD method compared to the modified SSA_DCT method of 1.48 (95% CI: 1.33 to 1.63), P<0.001. The dual low-pass filter and total variation denoising methods are considerably more computational efficient, by 3 to 4 orders of magnitude, than the SSA_DCT method. More research is needed to examine the efficacy of these methods in extracting oxygen desaturation in real NIRS signals.
We model the elasticity of the cerebral cortex as a layered material with bending energy along the layers and elastic energy between them in both planar and polar geometries. The cortex is also subjected to axons pulling from the underlying white matter. Above a critical threshold force, a flat cortex configuration becomes unstable and periodic unduluations emerge, i.e. a buckling instability occurs. These undulations may indeed initiate folds in the cortex. We identify analytically the critical force and the critical wavelength of the undulations. Both quantities are physiologically relevant values. Our model is a revised version of the axonal tension model for cortex folding, with our version taking into account the layered structure of the cortex. Moreover, our model draws a connection with another competing model for cortex folding, namely the differential growth-induced buckling model. For the polar geometry, we study the relationship between brain size and the critical force and wavelength to understand why small mice brains exhibit no folds, while larger human brains do, for example. Finally, an estimate of the bending rigidity constant for the cortex can be made based on the critical wavelength.
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