The maintenance of adequate blood flow to the brain is critical for normal brain function; cerebral blood flow, its regulation and the effect of alteration in this flow with disease have been studied extensively and are very well understood. This flow is not steady, however; the systolic increase in blood pressure over the cardiac cycle causes regular variations in blood flow into and throughout the brain that are synchronous with the heart beat. Because the brain is contained within the fixed skull, these pulsations in flow and pressure are in turn transferred into brain tissue and all of the fluids contained therein including cerebrospinal fluid. While intracranial pulsatility has not been a primary focus of the clinical community, considerable data have accrued over the last sixty years and new applications are emerging to this day. Investigators have found it a useful marker in certain diseases, particularly in hydrocephalus and traumatic brain injury where large changes in intracranial pressure and in the biomechanical properties of the brain can lead to significant changes in pressure and flow pulsatility. In this work, we review the history of intracranial pulsatility beginning with its discovery and early characterization, consider the specific technologies such as transcranial Doppler and phase contrast MRI used to assess various aspects of brain pulsations, and examine the experimental and clinical studies which have used pulsatility to better understand brain function in health and with disease.
The output of a TCD measurement is a velocity waveform as a function of time, for the entire recording period which is typically many cardiac cycles. This waveform can then be quantified in terms of the amplitude of the waveform, which is generally expressed as the PI, calculated as (peak systolic velocity - peak diastolic velocity)/mean velocity. Because it is normalized to the mean velocity, this is a measure of relative vascular pulsatility. A relative measure is used because of the difficulty of quantifying absolute velocity in a vessel; the velocity measured can vary dramatically depending on the size of the vessel, and the angle between the transducer and the vessel. PI, however, is insensitive to these experimental details and is a good gauge of changes in arterial pulsatility. One potential issue with PI measures, as compared to absolute pulsatility measures, is the dependence on both pulsatility and mean velocity; an increase in PI may not be strictly due to an increase in pulsatility but may also arise due to a decrease in mean velocity (e.g., decreased blood flow). Another measure frequently used clinically which is related to the PI is the resistive index (RI), defined as (peak systolic velocity - peak diastolic velocity)/peak systolic velocity. The advantage of RI is that it does not require integration of the flow parameter to determine mean velocity. RI has been associated with the probability of requiring a shunt in neonates with post-hemorrhagic hydrocephalus [41], although it is virtually certain that the PI would have made similar predictions in this setting.
Lax: Experiment 285 Sub Download
DOWNLOAD: https://tweeat.com/2vJWDg
Systems analysis of the intracranial pulse pressure and the concept of transfer function. Because the intracranial pressure wave is a complex result of both the shape of the incoming arterial pressure wave, as well as the biomechanics of the intracranial compartment, additional analysis is needed to extract information about the biomechanics of the intracranial system independent of pressure waveform morphology. In systems analysis, the concept of transfer function is used to accomplish this. In these experiments, both arterial and intraparenchymal pressure were measured. The frequency-domain transfer function relates these two waveforms, i.e. how does the system (the cranium) transform the input (arterial pressure) into the output (parenchymal pressure)? This work showed the existence of a "notch" in the transfer function specifically in the vicinity of the heart rate (dip in signal seen in the lower right-hand corner) indicating minimal transmission of the fundamental cardiac frequency from the arterial pressure into the parenchymal pressure. However, under conditions of raised ICP through CSF volume loading, this notch disappears (reddish area just above the lower right corner, coincident with the increase in ICP seen in the blue curve) because of the increase in the fundamental cardiac frequency component of the intracranial pressure wave (figure reproduced with permission, with modifications, from Zou et al [73]).
It is argued that even for a linear system of ODEs with constant coefficients, stiffness cannot properly be characterized in terms of the eigenvalues of the Jacobian, because stiffness is a transient phenomenon whereas the significance of eigenvalues is asymptotic. Recent theory from the numerical solution of PDEs is adapted to show that a more appropriate characterization can be based upon pseudospectra instead of spectra. Numerical experiments with an adaptive ODE solver illustrate these findings.
Another obvious advantage of MCC over PCC is that it provides two components: the fraction of A with B and the fraction of B with A. This is important when the probes distribute to different kinds of compartments, as for example, in the case in which all of A is found in compartments containing B, but B is also found in additional compartments lacking A. Consider the example of a study in which an investigator has fluorescently labeled a protein that associates with vesicles and would like to know if these vesicles associate with microtubules. If one numerically simulates this situation such that all of the vesicular fluorescence overlaps that of the microtubules but only 20% of the microtubule fluorescence overlaps that of vesicles, one obtains a depressingly low PCC of 0.2. However, this same value is obtained if only 20% of the vesicular fluorescence overlaps that of microtubules, if 100% of the microtubule fluorescence overlaps that of the vesicles. Thus, with respect to the question of whether the vesicular protein associates with microtubules, the same low PCC value is obtained for essentially opposite experimental outcomes. This problem is realized in the analysis of diI-LDL and GFP-Rab7 in the image shown in Fig. 7K. Although MCC analysis indicates that 71% of the internalized LDL localizes to compartments associated with GFP-Rab7, the PCC value obtained for this cell is very low (0.32) because of the large number of GFP-Rab7 compartments in excess of those labeled with diI-LDL (80% of the GFP-Rab7 occurs in regions lacking diI-LDL). This is another example of complex data in which pixel intensities of the two probes are not related by a simple, linear relationship. For these kinds of data, PCC yields ambiguous results, whereas MCC more directly measures the quantity of interest.
MCC provides a measure of colocalization that is much more meaningful to most investigators: the fraction of each probe that is colocalized with the other. By providing two separate measures, MCC is also independent of differences in the number of structures labeled by each probe. Finally, MCC does not depend on a linear relationship between the signal levels of the two probes and is less finicky with respect to defining the ROI, making it simpler to implement for measurements of volumes. The major drawback of MCC is that measured values are very sensitive to the estimated level of background, the threshold value used to distinguish labeled structures from unlabeled background. In general, accurate estimation of background is subjectively evaluated via visual inspection of the thresholded images. Given this subjectivity, it is important to avoid bias by consistently applying the same thresholding technique to all experimental samples. For some images, effective thresholding can be accomplished by relatively standard methods. However, many images obtained in biological microscopy are challenging for standard thresholding techniques and may require more elaborate methods of image segmentation, which is itself a distinct field of research for which most cell biologists have neither the time, the training, nor the inclination.
As with any metric, the significance of a difference between two groups can be evaluated statistically. For example, our group and others have used Student's t-tests to test the significance of differences in PCCs (4, 46) and MCCs (26, 42). As long as none of the confounding variables described above differ between experimental groups (e.g., differences in signal level, noise or the relative amount of labeling between the two probes for PCC, differences in the accuracy of background estimation for MCC), statistical comparison of populations is straightforward.
An alternative approach for evaluating the significance of a colocalization measurement is to compare the mean of measured values to the mean obtained from pairs of images that are out of registration with one another, a condition that should yield random colocalization. This can be accomplished by rotating the image of one channel relative to the other (4), shifting the image of one channel relative to the other (19, 45) or selecting different regions of the two images (27). This situation is essentially identical to a comparison of two experimental groups, except that in this case one statistically compares the mean of a set of experimental values to the mean of a set of values obtained from misregistered data.
In summary, whereas the idea of directly estimating the probability that a given measurement of colocalization could be obtained by chance is very attractive, it is not simple to implement in practice. The process of generating randomized data is complicated by the difficulty of reproducing the autocorrelation present in the original data and by the difficulty of identifying the region of potential interaction of the two probes. Failure to appreciate these factors will generally lead to systematic overestimation of the significance of colocalization measurements. These problems may underlie the fact that, in a survey of applications of this approach, we find an inordinately large number of studies in which the experimentally measured values fall outside the entire range of randomized values, indicating for each the statistically unlikely random probability of zero. While extreme values are possible, they should be regarded critically and ideally compared with values obtained for images of probes with unrelated distributions. 2ff7e9595c
コメント