This research was supported by National Heart, Lung, and Blood Institute Grant HL-093113.

1. Oscillatory Lower Body Negative Pressure (OLBNP)
<ol>
	<li>Equipment Setup
	<ol>
		<li>Electrocardiogram Lead II (ECG): Affix the three (or more) electrodes to the subject&#8217;s torso for the monitoring of heart rate throughout the study.</li>
		<li>Neoprene Skirt: Use a custom made neoprene skirt that seals the subject into lower body negative pressure chamber up to the iliac crest. Put it around the subject&#8217;s chest before they are placed supine in the tank and ensure that the ECG signal is still adequate. Ensure that it is snug but not so tight as to restrict breathing.</li>
		<li>Lower Body Negative Pressure Chamber: Have the subject lie supine on the bed and maneuver the LBNP chamber underneath them. If the LBNP chamber has an adjustable bicycle seat (to minimize movement artifact without counteracting the effect of the suction), make sure the subject is comfortably seated upon it. Use a custom made Plexiglas spacer cut to the subject&#8217;s waist size to help seal the chamber. Seal the neoprene skirt around the LBNP chamber with duct tape.</li>
		<li>LBNP Chamber Pressure: Connect the LBNP chamber to a standard pressure transducer. Calibrate the pressure transducer to mmHg.</li>
		<li>Repeat Cycle Timer Attached to Mechanical Valve: Attach the custom built mechanical valve and repeat cycle timer to the LBNP chamber. 
		NOTE: A time delay relay attached to two motors that control a mechanical valve is used to alternate between negative pressure and ambient pressure. The time delay relay alternates voltage to the motors at a fixed interval to open and close a valve between the chamber and the vacuum. This creates an LBNP chamber pressure waveform that is roughly square wave in shape. Adjust the cycle time to the desired OLBNP frequency.</li>
		<li>Variable Transformer and Vacuum: Attach a standard household vacuum cleaner to the mechanical valve. Plug the vacuum into a variable transformer that allows the voltage to the vacuum to be controlled. Turn on the vacuum cleaner and adjust the variable transformer until the target LBNP pressure (e.g., 30 mmHg) is achieved.</li>
		<li>Arterial Blood Pressure: Attach non-invasive photoplethysmographic arterial pressure cuffs (e.g., Portapres, Finapres) to the finger(s) of one hand. Ensure accuracy by comparing pressure to oscillometric pressures from the brachial artery of the opposite arm.</li>
		<li>2 MHz Transcranial Doppler and Probe Fixation Device
		<ol>
			<li>Use a 2 MHz pulse wave Doppler probe to insonate the M1 segment of the middle cerebral artery at the temple (i.e., the transtemporal window).</li>
			<li>Alter probe angle, insonation depth (~55 mm), gain, and transmission power to maximize the spectral intensity of the signal.</li>
			<li>Fix the Doppler probe in place using a fixation device that has no back (i.e., not a headband) so that movement artifact is not introduced into the signal as the volunteer moves with negative pressure oscillations. 
			NOTE: Cerebral blood flow can be measured unilaterally or bilaterally, but no difference in cerebral autoregulation is expected between hemispheres unless a localized injury like stroke or traumatic brain injury is present.20</li>
		</ol>
		</li>
		<li>Expired CO2: Use a nasal cannula attached to an infrared CO2 analyzer to monitor expired CO2 and instruct the subject to breathe only through their nose. Given the profound effect arterial CO2 has on cerebral blood flow,21 monitor CO2 throughout every study.</li>
	</ol>
	</li>
	<li>Data Acquisition
	<ol>
		<li>Set up the analog to digital conversion of arterial pressure, cerebral blood flow, LBNP chamber pressure, and expired CO2 to acquire at a minimum of 50 Hz per channel. Acquire ECG at 1 kHz. 
		NOTE: While subsequent analysis deals with much lower frequency information (&#8804;0.07 Hz), it is critical to monitor the quality of the signals being acquired during a study. A sampling rate of 50 Hz will allow accurate visualization of blood pressure and cerebral blood flow for the detection of artifact.</li>
	</ol>
	</li>
	<li>Oscillatory LBNP Protocol
	<ol>
		<li>Turn on vacuum and ensure tank pressure is stable at &#8211;30 mmHg.</li>
		<li>Set repeat cycle timer to 33 sec for 0.03 Hz OLBNP.</li>
		<li>Adjust Doppler probe(s) to ensure optimal signal.</li>
		<li>Acquire data for at least 15 cycles (500 sec at 0.03 Hz) to ensure sufficient confidence in PPR estimates. If time permits, collect more data than this as it will further improve the signal-to-noise ratio.</li>
		<li>Repeat the above steps for any frequencies between 0.03 Hz-0.08 Hz by changing the repeat cycle timer duration. 
		NOTE: Apply frequencies in order but randomly vary the starting frequency between subjects.</li>
	</ol>
	</li>
</ol>
2. Projection Pursuit Regression (PPR)
<ol>
	<li>Data Preprocessing
	<ol>
		<li>Decimation and Low-pass Filtering
		<ol>
			<li>Open Matlab. Type the command &#8220;data = resample(data, 1, SR/5)&#8221; (where SR is the original sampling rate) to decimate the arterial pressure and cerebral blood flow to 5 Hz. 
			NOTE: Optionally, low-pass filter (19th order Chebyshev Type II) with a cutoff of 0.4 Hz. The filtering is redundant, given subsequent processing, but creates mean waveforms that don&#8217;t rely on peak detection of the sometimes noisy arterial pressure and cerebral blood flow signals.</li>
		</ol>
		</li>
		<li>Artifact Removal
		<ol>
			<li>Using the original non-decimated waveforms as a guide, remove any sections of the signals with artifacts and linearly interpolate. If these sections account for more than 10% of the recording period, discard the recording entirely. 
			NOTE: At this point, the waveforms are suitably processed for traditional linear approaches such as transfer function analysis.</li>
		</ol>
		</li>
		<li>Band-pass Filtering
		<ol>
			<li>In Matlab, type: [B, A] = cheby1(1,1, [F &#8211; 0.005 F+0.005]/(SRd/2))&#160; data = filtfilt(B, A, detrend(data, &#8216;linear&#8217;) to band-pass filter the pressure and flow in a &#177; 0.005 Hz band (1st order Chebyshev Type I with 1 dB of pass band ripple) around the frequency of OLBNP (Figure 2) where F is the dominant OLBNP frequency, SRd is the decimated sampling rate (5 Hz after step 2.1.1), and &#8220;data&#8221; is the decimated signal (arterial pressure or flow). 
			NOTE: This minimizes potential interference and increases the signal-to-noise ratio in subsequent PPR analysis. Although the dominant arterial pressure fluctuation occurs at the oscillatory frequency of lower body negative pressure, random noise in the signals may interfere with the derivation of pressure-flow relationships. Results without band-pass filtering will be qualitatively similar but the percent variance explained (i.e., R2) will be lower.19</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Projection Pursuit Regression Estimation 
	NOTE: Using the built-in function &#8216;ppr&#8217; in R Language and Environment for Statistical Computing, and/or via custom-written functions in other platforms, generate a single ridge function (M=1) for the arterial pressure-cerebral flow relation.
	<ol>
		<li>In Matlab, enter the command &#8220;CVLabPPR(pressure,flow)&#8221;. Enter the Study ID as XXXYYY, where XXX is the 3-letter study code and YYY is the three numeric characters for subject ID. Enter the Study Date in the following format: YYYY-MM-DD. Enter the numeric measurement # (e.g., &#8220;1&#8221; for day 1).</li>
		<li>Enter the APM (enter FP for finapress or AL for art-line). Enter the Vessel (MCA, ACA, or PCA). Enter &#8220;y&#8221; or &#8220;n&#8221; to the query &#8220;Do you have right MCA measurements?&#8221; Enter &#8220;y&#8221; or &#8220;n&#8221; to the query &#8220;Do you have left MCA measurements?&#8221; 
		NOTE: 
		<img alt="Equation 1" fo:content-width="3in" src="/files/ftp_upload/51082/51082eq1.jpg" width="300" /> 
		For each input (xt &#8212; arterial blood pressure) and output (yt &#8212; cerebral blood flow) a linear autoregressive transfer function (Eq 1. &#8212; the term within the parentheses) is passed through nonparametric kernel functions (km; called &#39;ridge functions&#39;) that are determined by minimizing the mean squared error. Projection pursuit regression can include more than one ridge function (i.e., M &#62; 1). However, though it will reduce the mean squared error, it may obscure the interpretation of ridge functions due to potential interactions between them. Because the primary purpose is to obtain a relation between arterial pressure and cerebral blood flow that can be interpreted physiologically, PPR should be limited to only one ridge function (M = 1).</li>
		<li>Piecewise Linear Parameterization. Parameterize the ridge function as a piecewise linear function for subsequent statistical analysis (Figure 3). For Matlab, use Bruno Luong&#8217;s Free-knot spline approximation. Enter the command &#8220;BSFK(x, y, k, nknots)&#8221; where k=2 for a linear fit and nknots=3 for three regions. 
		NOTE: This identifies those points where the arterial pressure-cerebral flow relationship changes, and the ranges wherein the relationship is approximately linear. Figure 3 shows a schematic of the results. The gain (i.e., the linear slope) of the pressure&#8211;flow relation within each region provides a measure of the effectiveness of cerebral autoregulation within that region. A lower gain indicates more effective counter-regulation of pressure fluctuations whereas higher gains indicate more passive flow responses to pressure changes.</li>
	</ol>
	</li>
</ol>

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The authors declare that they have no competing financial interests.

Precisely defining input-output relations can require that the input (in this case, pressure) actively changes across a sufficiently wide range to observe the output response. However, spontaneously occurring pressure fluctuations are extremely inconsistent and small in amplitude within the frequencies of cerebral autoregulation.27 This is the reason that spontaneous changes in pressure and flow show a relationship with periods of high correlation and periods of extremely low correlation and that oscillations in cerebral blood flow seemingly appear with no apparent arterial pressure drive.28 OLBNP22 provides a critical technique to create consistent arterial pressure oscillations of varying frequency and amplitude to assess cerebral blood flow responses. Although there may be other approaches that might provide a similar probe, this approach allows for rigorous testing of the frequency- and/or amplitude-dependent relationship between arterial pressure and cerebral blood flow velocity.
Prior research exploring potential measurement tools for cerebral autoregulation have used linear models of the relation between arterial pressure and cerebral blood flow (e.g., transfer function analysis). A close linear relation between pressure and flow changes with no dampening is observed when pressure oscillations are relatively fast, i.e., &#62; ~10 sec. However, slower oscillations (&#62; ~20 sec) engender a relation between pressure and flow that becomes progressively less linearly related.8,24 If the relation is not highly linearly related (low R2, low cross-spectral coherence) one cannot have any confidence in the accuracy of linear measures such as transfer function gain and phase. The lack of linear relation indicates the presence of important nonlinearities that are characteristic of cerebral autoregulation. In fact, by its very nature, autoregulation is not amenable to characterization via linear approaches; linear approaches can indicate presence or absence of autoregulation, but cannot describe its characteristics and its effectiveness.
There are methods that are comparable to linear methods in their simplicity but that can assess nonlinear relationships between input (pressure) and output (flow) variables. Projection pursuit regression is simply a nonparametric, atheoretical, multiple regression method29,30 that does not posit an a priori model or assume linearity in the input-output relation. These are clear advantages for characterizing a system that is incompletely understood. However, it should be noted that using more than one ridge function will increase the percent variance explained but at the expense of obscuring physiologic interpretation of the characteristic relationships. Therefore, it is recommended that projection pursuit regression be limited to only one ridge function. Nonetheless, the PPR approach outlined with a single ridge function can explain a significant portion of the variance in the relation between arterial pressure and cerebral blood flow and reveal the characteristic nonlinear relation that is consistent across individuals.
Limitations and Possible Modifications
Oscillatory lower body negative pressure requires specific and obtrusive equipment and procedures and so is not appropriate for clinic based assessments. It is possible that resting recordings of sufficient length could provide adequate data for PPR analysis of cerebral autoregulation. However, previous work showed that projection pursuit regression of resting data performs significantly worse than analysis of 0.03 Hz OLBNP data. Although pressure-flow relationships quantified at rest and during 0.03 Hz OLBNP are related,19 the modest correspondence simply suggests that the pressure-flow relationships estimated at rest may not reliably reflect those derived from 0.03 Hz OLBNP. One solution may be to generate consistent and larger amplitude pressure fluctuations within the frequencies of autoregulation via slow, deep eucapnic breathing or repeated squat-stand maneuvers. These methods have been shown to generate reliably large pressure fluctuations that may provide changes across a sufficiently wide range to observe cerebral blood flow responses. 31,32
Though on average, projection pursuit regression can explain a significant amount of the relationship between arterial pressure and cerebral flow fluctuations, explained variance may be low in a few cases (~6%19). Low performance could derive, for example, from breathing patterns if frequency and tidal volume are not controlled. However, every physiological test has some aberrant observations, and this approach is not an exception. Poor measurements in ~1 of 20 observations should not undermine the potential utility of the approach.
Future Applications / Conclusions
The characteristic pressure-flow relationship may be altered in some pathophysiologic conditions, such as stroke33 and traumatic brain injury.34 If accurate relations could be acquired in the clinical setting, projection pursuit regression of cerebral autoregulation may have broader application and be useful as an assessment tool where OLBNP is not available. It is possible that simple maneuvers (e.g., deep breathing, thigh cuff, sit-to-stand) and/or longer duration resting recordings could result in pressure-flow relationship that can be sued to derive cerebral autoregulation comparable to OLBNP data. Nonetheless, laboratory-based determination of different regulatory systems and their contribution to the nonlinearities of autoregulation could provide unique insight to cerebrovascular control, and allow diagnosis of pathophysiological alterations in cerebral autoregulation (e.g., after traumatic brain injury).

The process by which cerebral perfusion is maintained constant over a wide range of systemic pressures is known as &#8220;cerebral autoregulation.&#8221; Original observations of cerebral flow responses1 supported a counter-regulation against changes in arterial pressure that is of major importance for the daily regulation of cerebral perfusion. Although characterization of autoregulation was based on studies of sustained, controlled hypo- and hypertension,2,3 it was recognized that pressure-induced changes in resistance are &#39;an oscillatory process&#39;3 encompassing changes from 10 to 90 sec.4 Moreover, within the past two decades, measurement of cerebral blood flow velocity on a beat-by-beat basis5 has shown that cerebral flow is regulated over periods as short as just a few heart beats.6,7 These beat-by-beat data suggest that effective dampening of flow against pressure changes occurs over periods as short as ~15 sec and it becomes progressively greater over longer time periods.8 Thus, the relationship between pressure and flow functions as a high pass filter7,9-12 wherein slower changes in blood pressure are effectively blunted and faster oscillations pass through relatively unaffected.
The primary difficulty in characterizing the frequency dependence of cerebral autoregulation is the lack of prominent spontaneous fluctuations in arterial pressure around the frequencies of interest (less than ~0.07 Hz or ~15 sec). Without sufficiently large pressure oscillations, one cannot accurately quantify the cerebral blood flow response. Our laboratory has dealt with this constraint by using a technique known as oscillatory lower body negative pressure (OLBNP). This creates caudal venous blood volume shifts proportional to the level of negative pressure in the tank due to reduced venous transmural pressure. When the negative pressure is applied at set intervals, the oscillations in central venous return result in arterial pressure fluctuations at the frequency of OLBNP. This approach has been used in several studies across different laboratories.8,14-17 This creates caudal venous blood volume shifts proportional to the level of negative pressure in the tank due to reduced venous transmural pressure. When the negative pressure is applied at set intervals, the oscillations in central venous return result in arterial pressure fluctuations at the frequency of OLBNP. This approach has been used in several studies across different laboratories.8,15-18
Even with an approach that can generate prominent fluctuations in arterial pressure around the frequencies of interest, there is a complicating factor: there is significant evidence of nonlinearity in cerebral autoregulation, especially at the lowest frequencies.8 Moreover, there is no strong theoretical guide as to the nature of nonlinearities present in cerebral autoregulation. Hence, we use an atheoretical, data driven method known as Projection Pursuit Regression (PPR) in our analysis.19 PPR is a nonparametric method to characterize nonlinear relations inherent in a system without any a priori assumptions as to the nature of these nonlinearities. This is a decided advantage for capturing a system whose physiology is not yet defined by explicit nonlinear models. PPR reveals that the characteristic non-linearity of cerebral autoregulation resembles the &#8220;classic autoregulatory curve&#8221; first described by Lassen in 1959 (Figure 1).2,19 That is, cerebral blood flow remains relatively constant within a certain range of arterial pressure, but passively tracks in a linear fashion outside of this range. This shape becomes more apparent as arterial pressure fluctuation become slower. Hence, linear analysis is insufficient to fully interrogate cerebral autoregulation and reliance on linear techniques likely misses important information.
In this article we detail the approach to both data acquisition (laboratory use of OLBNP) and analysis (PPR) we use to characterize cerebral autoregulation in health and disease.

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			<td height="43" style="height:43px;width:216px;">Transcranial Doppler Ultrasound</td>
			<td style="width:185px;">Compumedics DWL</td>
			<td style="width:119px;">Multi-Dop X digital</td>
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			<td height="22" style="height:22px;width:216px;">ECG and Brachial BP</td>
			<td style="width:185px;">GE</td>
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			<td height="22" style="height:22px;width:216px;">LBNP Tank</td>
			<td style="width:185px;">U. of Iowa Bioengineering</td>
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			<td>Custom Built</td>
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			<td height="22" style="height:22px;width:216px;">Mechanical Valve</td>
			<td style="width:185px;">U. of Iowa Bioengineering</td>
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			<td height="22" style="height:22px;width:216px;">Repeat Cycle Timer</td>
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			<td height="22" style="height:22px;width:216px;">Pressure Transducer</td>
			<td style="width:185px;">Gould</td>
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			<td height="43" style="height:43px;width:216px;">Photoplethysmographic finger pressure monitor</td>
			<td style="width:185px;">Finapres Medical Systems</td>
			<td style="width:119px;">Finometer PRO</td>
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			<td height="64" style="height:64px;width:216px;">CO2 gas analyzer</td>
			<td style="width:185px;">VacuMed</td>
			<td style="width:119px;">#17515 CO2 Analyzer, Gold Edition</td>
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			<td height="64" style="height:64px;width:216px;">Data acquisition system</td>
			<td style="width:185px;">AD Instruments</td>
			<td style="width:119px;">Data Acquisition Systems - PowerLab</td>
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assessing cerebral autoregulation via oscillatory lower body negative pressure and projection pursuit regression

The process by which cerebral perfusion is maintained constant over a wide range of systemic pressures is known as &#8220;cerebral autoregulation.&#8221; Effective dampening of flow against pressure changes occurs over periods as short as ~15 sec and becomes progressively greater over longer time periods. Thus, slower changes in blood pressure are effectively blunted and faster changes or fluctuations pass through to cerebral blood flow relatively unaffected. The primary difficulty in characterizing the frequency dependence of cerebral autoregulation is the lack of prominent spontaneous fluctuations in arterial pressure around the frequencies of interest (less than ~0.07 Hz or ~15 sec). Oscillatory lower body negative pressure (OLBNP) can be employed to generate oscillations in central venous return that result in arterial pressure fluctuations at the frequency of OLBNP. Moreover, Projection Pursuit Regression (PPR) provides a nonparametric method to characterize nonlinear relations inherent in the system without a priori assumptions and reveals the characteristic non-linearity of cerebral autoregulation. OLBNP generates larger fluctuations in arterial pressure as the frequency of negative pressure oscillations become slower; however, fluctuations in cerebral blood flow become progressively lesser. Hence, the PPR shows an increasingly more prominent autoregulatory region at OLBNP frequencies of 0.05 Hz and below (20 sec cycles). The goal of this approach it to allow laboratory-based determination of the characteristic nonlinear relationship between pressure and cerebral flow and could provide unique insight to integrated cerebrovascular control as well as to physiological alterations underlying impaired cerebral autoregulation (e.g., after traumatic brain injury, stroke, etc.).

OLBNP amplitudes from 10 mmHg22 up to 120 mmHg17 have been used to augment arterial pressure fluctuations, but 30 mmHg OLBNP is sufficient23,24 and not beyond the regulatory capacity of the cerebrovasculature.17 This level of OLBNP results in blood pressure oscillations that are about 15-20 mmHg in magnitude at 0.03 Hz, which is not greater than the blood pressure changes occurring when going from seated to standing.25 There are some limitations to the range within which OLBNP can generate arterial pressure fluctuations. Autoregulation is only active at ~0.07 Hz and slower, so the upper limit is not an issue. However the difficulty in generating low frequency oscillations below 0.03 Hz is that the cardiovascular system counter-regulates against the LBNP-induced arterial pressure changes before the cycle is finished. As Figure 4 shows, at 0.025 Hz OLBNP we actually see the largest peak in the arterial pressure oscillations at 0.05 Hz. While the frequency response of cerebral autoregulation can be characterized from 0.03 Hz-0.08 Hz to define the time scales within which autoregulation is active,23,24 0.03 Hz and 0.08 Hz OLBNP are sufficient since they represent a range of autoregulatory function (i.e., a pronounced autoregulatory region to none or a modest one).
OLBNP generates larger fluctuations in arterial pressure as the frequency of negative pressure oscillations become slower. Figure 5 shows the arterial pressure and consequent cerebral blood flow fluctuations with OLBNP from 0.08 Hz (12.5 sec cycles) to 0.03 Hz (33 sec cycles). At the higher frequencies, cerebral blood flow fluctuates in concert with arterial pressure. The PPR demonstrates this; there is a proportional linear relation between arterial pressure and cerebral blood flow at the higher frequencies of 0.08 Hz, 0.07 Hz (14 sec cycles), and 0.06 Hz (16.6 sec cycles). At slower frequencies of OLBNP, though arterial pressure fluctuations become larger, fluctuations in cerebral blood flow are progressively more effectively dampened. Hence, the PPR shows an increasingly more prominent autoregulatory region at OLBNP frequencies from 0.05 Hz (20 sec cycles), to 0.04 Hz (25 sec cycles), to 0.03 Hz. In the example shown, at 0.03 Hz, the PPR curve clearly resembles the &#8220;classic autoregulatory curve&#8221; described by Lassen (Figure 1). We have previously shown that this observation cannot be explained simply by the increase in magnitude of arterial pressure fluctuations as the frequency of oscillations become slower. We have previously applied PPR to data from 48 individuals during different magnitudes of OLBNP (thus, different magnitude of pressure fluctuations).19 While we did not explicitly explore a potential relation between autoregulatory range and the magnitude of pressure fluctuations, we reported that the variation in autoregulatory range was only ~6%. Thus, our prior results clearly show that the change in PPR curve with frequency cannot be fully explained by a change in the magnitude of pressure fluctuations. In the same study, we assessed whether the PPR characterization of autoregulation is reproducible across separate sessions. This analysis showed that the slope of the autoregulatory range during 0.03 Hz OLBNP did not change (Lin&#8217;s Concordance = 0.96, p &#60; 0.001) and thus the nonlinear pressure-flow relation is consistent across study days.
Although the cerebrovascular bed is well innervated by sympathetic nerve fibers, their role in autoregulation has not been widely accepted.26 Therefore, some of our previous work explored the potential role of the sympathetic nervous system in cerebrovascular autoregulation.24 We found a clear role for the sympathetic system in regulating cerebral flow, but we were not able to characterize how the relationship changed with removal of sympathetic effects due to the limitations of linear methods for characterizing autoregulation. Figure 6 shows the results from PPR application to data before (baseline) and after sympathetic blockade during 0.05 Hz. The overall curve becomes markedly more linear. Moreover, PPR analysis of 0.03 Hz data where autoregulation is most apparent showed that the range of the autoregulatory region remains unchanged, but the slope within that region increases, reflecting less effective autoregulation (Figure 7).
<img alt="Figure 1" src="/files/ftp_upload/51082/51082fig1highres.jpg" /> 
Figure 1. The &#39;classic&#39; autoregulatory curve derived from the relationship between static increases and decreases in pressure and steady state cerebral blood flow. A region of unchanging flow despite changing pressure (i.e., slope = 0) is bounded by regions wherein increasing and decreasing pressures result in proportional cerebral blood flow changes.
<img alt="Figure 2" src="/files/ftp_upload/51082/51082fig2highres.jpg" /> 
Figure 2. The preprocessing necessary to perform PPR analysis. Signals are first decimated to 5 Hz and then band-pass filtered at the frequency of OLBNP (&#177;0.005 Hz).
<img alt="Figure 3" src="/files/ftp_upload/51082/51082fig3highres.jpg" /> 
Figure 3. Parameters of the cerebral autoregulation curve derived from PPR analysis of arterial pressure and cerebral blood flow during 0.03 Hz OLBNP.
<img alt="Figure 4" src="/files/ftp_upload/51082/51082fig4highres.jpg" /> 
Figure 4. Power spectrum shows the magnitude of fluctuations in arterial pressure when OLBNP frequency is below 0.03 Hz (33-second cycle). Note that there are two large peaks in the arterial pressure spectral power at 0.025 and 0.05 Hz (40 and 20 sec cycles), however there is only a single peak in the LBNP spectral power at 0.025 Hz. Moreover, the largest fluctuation in pressure is at 0.05 Hz and would confound the interpretation of the cerebral blood flow responses.
<img alt="Figure 5" src="/files/ftp_upload/51082/51082fig5highres.jpg" /> 
Figure 5. Example of the effects of OLBNP from 0.08 to 0.03 Hz on arterial pressure and cerebral blood flow. Arterial pressure fluctuations become larger with slower OLBNP whereas cerebral blood flow fluctuations become smaller. This autoregulatory function is described by the results of the PPR analysis shown in the bottom panels. The autoregulatory region in cerebral blood flow becomes progressively more pronounced with slower OLBNP.
<img alt="Figure 6" src="/files/ftp_upload/51082/51082fig6highres.jpg" /> 
Figure 6. Individual and averaged PPR autoregulatory curves from 0.05 Hz OLBNP data in subjects before (baseline) and after sympathetic blockade. Note that the loss of the narrow autoregulatory region after sympathetic blockade.
<img alt="Figure 7" src="/files/ftp_upload/51082/51082fig7highres.jpg" /> 
Figure 7. Average of the PPR parameters from 0.03 Hz OLBNP data before and after sympathetic blockade. Sympathetic blockade had a pronounced effect on the cerebral autoregulation curve within the autoregulatory range, markedly increasing the slope (i.e., more proportional cerebral flow changes with pressure changes).

Watch this Scientific Journal Video about Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression at JoVE.com

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Cerebral perfusion is maintained across a range of pressures via cerebral autoregulation. However, characterizing autoregulation requires prominent pressure fluctuations at regulated frequencies. The described protocol will show how oscillatory lower body negative pressure can generate pressure fluctuations to provide data for projection pursuit regression for quantification of the autoregulatory curve.

The process by which cerebral perfusion is maintained constant over a wide range of systemic pressures is known as &#8220;cerebral autoregulation.&#8221; ...

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12.3K Views.  Harvard Medical School. Cerebral perfusion is maintained across a range of pressures via cerebral autoregulation. However, characterizing autoregulation requires prominent pressure fluctuations at regulated frequencies. The described protocol will show how oscillatory lower body negative pressure can generate pressure fluctuations to provide data for projection pursuit regression for quantification of the autoregulatory curve.

Video: Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Pressure-polishing Pipettes for Improved Patch-clamp Recording

Pressure-polishing is a method for shaping glass pipettes for patch-clamp recording. We first developed this method for fabricating pipettes suitable for recording from small (&lt;3 m) neuronal cell bodies. The basic principal is similar to glass-blowing and combines air pressure and heat to modify the shape of patch pipettes prepared by a conventional micropipette puller. It can be applied to so-called soft (soda lime) and hard (borosilicate) glasses. Generally speaking, pressure polishing can reduce pipette resistance by 25% without decreasing the diameter of the tip opening (Goodman and Lockery, 2000). It can be applied to virtually any type of glass and requires only the addition of a high-pressure valve and fitting to a microforge. This technique is essential for recording from ultrasmall cells (&lt;5 m) and can also improve single-channel recording by minimizing pipette resistance. The blunt shape is also useful for perforated-patch clamp recording since this tip shape results in a larger membrane bleb available for perforation.

This is a guide to modifying the shape of glass micropipettes. Specifically, by using heat and air pressure the taper is widened without increasing the tip opening, leading to lower pipette resistance. This is critical to obtain low noise recordings of small cells but is useful in many applications.

Pressure-polishing is a method for shaping glass pipettes for patch-clamp recording.  We first developed this method for fabricating pipettes suitable for ...

Measuring Blood Pressure in Mice using Volume Pressure Recording, a Tail-cuff Method

The CODA 8-Channel High Throughput Non-Invasive Blood Pressure system measures the blood pressure in up to 8 mice or rats simultaneously. The CODA tail-cuff system uses Volume Pressure Recording (VPR) to measure the blood pressure by determining the tail blood volume. A specially designed differential pressure transducer and an occlusion tail-cuff measure the total blood volume in the tail without the need to obtain the individual pulse signal. Special attention is afforded to the length of the occlusion cuff in order to derive the most accurate blood pressure readings. VPR can easily obtain readings on dark-skinned rodents, such as C57BL6 mice and is MRI compatible. The CODA system provides you with measurements of six (6) different blood pressure parameters; systolic and diastolic blood pressure, heart rate, mean blood pressure, tail blood flow, and tail blood volume. Measurements can be made on either awake or anesthetized mice or rats. The CODA system includes a controller, laptop computer, software, cuffs, animal holders, infrared warming pads, and an infrared thermometer. There are seven different holder sizes for mice as small as 8 grams to rats as large as 900 grams.

The CODA 8-Channel High Throughput Non-Invasive Blood Pressure system measures the blood pressure in up to 8 mice or rats simultaneously. This tail-cuff system uses Volume Pressure Recording (VPR) to measure the blood pressure by determining the tail blood volume.

The CODA 8-Channel High Throughput Non-Invasive Blood Pressure system measures the blood pressure in up to 8 mice or rats simultaneously. The CODA ...

Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging

Optical projection tomography is a three-dimensional imaging technique that has been recently introduced as an imaging tool primarily in developmental biology and gene expression studies. The technique renders biological sample optically transparent by first dehydrating them and then placing in a mixture of benzyl alcohol and benzyl benzoate in a 2:1 ratio (BABB or Murray s Clear solution). The technique renders biological samples optically transparent by first dehydrating them in graded ethanol solutions then placing them in a mixture of benzyl alcohol and benzyl benzoate in a 2:1 ratio (BABB or Murray s Clear solution) to clear. After the clearing process the scattering contribution in the sample can be greatly reduced and made almost negligible while the absorption contribution cannot be eliminated completely. When trying to reconstruct the fluorescence distribution within the sample under investigation, this contribution affects the reconstructions and leads, inevitably, to image artifacts and quantification errors.. While absorption could be reduced further with a permanence of weeks or months in the clearing media, this will lead to progressive loss of fluorescence and to an unrealistically long sample processing time. This is true when reconstructing both exogenous contrast agents (molecular contrast agents) as well as endogenous contrast (e.g. reconstructions of genetically expressed fluorescent proteins).

We suggest a Born normalized approach for Optical Projection Tomography (BnOPT) that accounts for the absorption properties of imaged samples to obtain accurate and quantitative fluorescence tomographic reconstructions. We use the proposed algorithm to reconstruct the fluorescence molecular probe distribution within small animal organs.

Optical projection tomography is a three-dimensional imaging technique that has been recently introduced as an imaging tool primarily in developmental ...

Long-term Culture of Human Breast Cancer Specimens and Their Analysis Using Optical Projection Tomography

Breast cancer is a leading cause of mortality in the Western world. It is well established that the spread of breast cancer, first locally and later distally, is a major factor in patient prognosis. Experimental systems of breast cancer rely on cell lines usually derived from primary tumours or pleural effusions. Two major obstacles hinder this research: (i) some known sub-types of breast cancers (notably poor prognosis luminal B tumours) are not represented within current line collections; (ii) the influence of the tumour microenvironment is not usually taken into account.
We demonstrate a technique to culture primary breast cancer specimens of all sub-types. This is achieved by using three-dimensional (3D) culture system in which small pieces of tumour are embedded in soft rat collagen I cushions. Within 2-3 weeks, the tumour cells spread into the collagen and form various structures similar to those observed in human tumours1. Viable adipocytes, epithelial cells and fibroblasts within the original core were evident on histology. Malignant epithelial cells with squamoid morphology were demonstrated invading into the surrounding collagen. Nuclear pleomorphism was evident within these cells, along with mitotic figures and apoptotic bodies.
We have employed Optical Projection Tomography (OPT), a 3D imaging technology, in order to quantify the extent of tumour spread in culture. We have used OPT to measure the bulk volume of the tumour culture, a parameter routinely measured during the neo-adjuvant treatment of breast cancer patients to assess response to drug therapy. 
Here, we present an opportunity to culture human breast tumours without sub-type bias and quantify the spread of those ex vivo. This method could be used in the future to quantify drug sensitivity in original tumour. This may provide a more predictive model than currently used cell lines.

We have developed a collagen-based in vitro assay which promotes proliferation and invasion from samples of all breast cancer subtypes. Optical Projection Tomography, a three dimensional microscopy technique was utilised to visualise and quantify tumour expansion. This assay may be used to quantify drug response of individual tumour samples.

Breast cancer is a leading cause of mortality in the Western world. It is well established that the spread of breast cancer, first locally and later ...

Collection, Isolation and Enrichment of Naturally Occurring Magnetotactic Bacteria from the Environment

Magnetotactic bacteria (MTB) are aquatic microorganisms that were first notably described in 19751 from sediment samples collected in salt marshes of Massachusetts (USA). Since then MTB have been discovered in stratified water- and sediment-columns from all over the world2. One feature common to all MTB is that they contain magnetosomes, which are intracellular, membrane-bound magnetic nanocrystals of magnetite (Fe3O4) and/or greigite (Fe3S4) or both3, 4. In the Northern hemisphere, MTB are typically attracted to the south end of a bar magnet, while in the Southern hemisphere they are usually attracted to the north end of a magnet3,5. This property can be exploited when trying to isolate MTB from environmental samples.
One of the most common ways to enrich MTB is to use a clear plastic container to collect sediment and water from a natural source, such as a freshwater pond. In the Northern hemisphere, the south end of a bar magnet is placed against the outside of the container just above the sediment at the sediment-water interface. After some time, the bacteria can be removed from the inside of the container near the magnet with a pipette and then enriched further by using a capillary racetrack6 and a magnet. Once enriched, the bacteria can be placed on a microscope slide using a hanging drop method and observed in a light microscope or deposited onto a copper grid and observed using transmission electron microscopy (TEM).
Using this method, isolated MTB may be studied microscopically to determine characteristics such as swimming behavior, type and number of flagella, cell morphology of the cells, shape of the magnetic crystals, number of magnetosomes, number of magnetosome chains in each cell, composition of the nanomineral crystals, and presence of intracellular vacuoles.

We demonstrate a method to collect magnetotactic bacteria (MTB) that can be applied to natural waters. MTB can be isolated and enriched from sediment samples using a relatively simple setup that takes advantage of the bacteria's natural magnetism. Isolated MTB can then be examined in detail using both light and electron microscopy.

Magnetotactic bacteria (MTB) are aquatic microorganisms that were first notably described in 19751 from sediment samples collected in salt marshes of ...

Whole-Body Nanoparticle Aerosol Inhalation Exposures

Inhalation is the most likely exposure route for individuals working with aerosolizable engineered nano-materials (ENM). To properly perform nanoparticle inhalation toxicology studies, the aerosols in a chamber housing the experimental animals must have: 1) a steady concentration maintained at a desired level for the entire exposure period; 2) a homogenous composition free of contaminants; and 3) a stable size distribution with a geometric mean diameter &lt; 200 nm and a geometric standard deviation &sigma;g &lt; 2.5 5. The generation of aerosols containing nanoparticles is quite challenging because nanoparticles easily agglomerate. This is largely due to very strong inter-particle forces and the formation of large fractal structures in tens or hundreds of microns in size 6, which are difficult to be broken up. Several common aerosol generators, including nebulizers, fluidized beds, Venturi aspirators and the Wright dust feed, were tested; however, none were able to produce nanoparticle aerosols which satisfy all criteria 5.
A whole-body nanoparticle aerosol inhalation exposure system was fabricated, validated and utilized for nano-TiO2 inhalation toxicology studies. Critical components: 1) novel nano-TiO2 aerosol generator; 2) 0.5 m3 whole-body inhalation exposure chamber; and 3) monitor and control system. Nano-TiO2 aerosols generated from bulk dry nano-TiO2 powders (primary diameter of 21 nm, bulk density of 3.8 g/cm3) were delivered into the exposure chamber at a flow rate of 90 LPM (10.8 air changes/hr). Particle size distribution and mass concentration profiles were measured continuously with a scanning mobility particle sizer (SMPS), and an electric low pressure impactor (ELPI). The aerosol mass concentration (C) was verified gravimetrically (mg/m3). The mass (M) of the collected particles was determined as M = (Mpost-Mpre), where Mpre and Mpost are masses of the filter before and after sampling (mg). The mass concentration was calculated as C = M/(Q*t), where Q is sampling flowrate (m3/min), and t is the sampling time (minute). The chamber pressure, temperature, relative humidity (RH), O2 and CO2 concentrations were monitored and controlled continuously. Nano-TiO2 aerosols collected on Nuclepore filters were analyzed with a scanning electron microscope (SEM) and energy dispersive X-ray (EDX) analysis.
In summary, we report that the nano-particle aerosols generated and delivered to our exposure chamber have: 1) steady mass concentration; 2) homogenous composition free of contaminants; 3) stable particle size distributions with a count-median aerodynamic diameter of 157 nm during aerosol generation. This system reliably and repeatedly creates test atmospheres that simulate occupational, environmental or domestic ENM aerosol exposures.

A whole-body nanoparticle aerosol inhalation exposure facility was constructed for nano-sized titanium dioxide (TiO2) inhalation toxicology studies. This system provides nano-TiO2 aerosol test atmospheres that have: 1) a steady mass concentration; 2) a homogenous composition free of contaminants; and 3) a stable particle size distribution during aerosol generation.

Inhalation is the most likely exposure route for individuals working with aerosolizable engineered nano-materials (ENM). To properly perform nanoparticle ...

Assessing Murine Resistance Artery Function Using Pressure Myography

Pressure myograph systems are exquisitely useful in the functional assessment of small arteries, pressurized to a suitable transmural pressure. The near physiological condition achieved in pressure myography permits in-depth characterization of intrinsic responses to pharmacological and physiological stimuli, which can be extrapolated to the in vivo behavior of the vascular bed. Pressure myograph has several advantages over conventional wire myographs. For example, smaller resistance vessels can be studied at tightly controlled and physiologically relevant intraluminal pressures. Here, we study the ability of 3rd order mesenteric arteries (3-4 mm long), preconstricted with phenylephrine, to vaso-relax in response to acetylcholine. Mesenteric arteries are mounted on two cannulas connected to a pressurized and sealed system that is maintained at constant pressure of 60 mmHg. The lumen and outer diameter of the vessel are continuously recorded using a video camera, allowing real time quantification of the vasoconstriction and vasorelaxation in response to phenylephrine and acetylcholine, respectively.
	To demonstrate the applicability of pressure myography to study the etiology of cardiovascular disease, we assessed endothelium-dependent vascular function in a murine model of systemic hypertension. Mice deficient in the &alpha;1 subunit of soluble guanylate cyclase (sGC&alpha;1-/-) are hypertensive when on a 129S6 (S6) background (sGC&alpha;1-/-S6) but not when on a C57BL/6 (B6) background (sGC&alpha;1-/-B6). Using pressure myography, we demonstrate that sGC&alpha;1-deficiency results in impaired endothelium-dependent vasorelaxation. The vascular dysfunction is more pronounced in sGC&alpha;1-/-S6 than in sGC&alpha;1-/-B6 mice, likely contributing to the higher blood pressure in sGC&alpha;1-/-S6 than in sGC&alpha;1-/-B6 mice. 
	Pressure myography is a relatively simple, but sensitive and mechanistically useful technique that can be used to assess the effect of various stimuli on vascular contraction and relaxation, thereby augmenting our insight into the mechanisms underlying cardiovascular disease.

In pressure myography, an intact small segment of a vessel is mounted onto two small cannulas and pressurized to a suitable luminal pressure. Here, we describe the method to measure vasorelaxation response of the mouse 3rd order mesenteric arteries in c57 and sGC&alpha;1-/- mice using pressure myography.

Pressure myograph systems are  exquisitely useful in the functional assessment of small arteries, pressurized  to a suitable transmural pressure. The  ...

Measuring Respiratory Function in Mice Using Unrestrained Whole-body Plethysmography

Respiratory dysfunction is one of the leading causes of morbidity and mortality in the world and the rates of mortality continue to rise. Quantitative assessment of lung function in rodent models is an important tool in the development of future therapies. Commonly used techniques for assessing respiratory function including invasive plethysmography and forced oscillation. While these techniques provide valuable information, data collection can be fraught with artefacts and experimental variability due to the need for anesthesia and/or invasive instrumentation of the animal. In contrast, unrestrained whole-body plethysmography (UWBP) offers a precise, non-invasive, quantitative way by which to analyze respiratory parameters. This technique avoids the use of anesthesia and restraints, which is common to traditional plethysmography techniques. This video will demonstrate the UWBP procedure including the equipment set up, calibration and lung function recording. It will explain how to analyze the collected data, as well as identify experimental outliers and artefacts that results from animal movement. The respiratory parameters obtained using this technique include tidal volume, minute volume, inspiratory duty cycle, inspiratory flow rate and the ratio of inspiration time to expiration time. UWBP does not rely on specialized skills and is inexpensive to perform. A key feature of UWBP, and most appealing to potential users, is the ability to perform repeated measures of lung function on the same animal.

The assessment of respiratory physiology has traditionally relied upon techniques, which require restraint or sedation of the animal. Unrestrained whole-body plethysmography, however, provides precise, non-invasive, quantitative analysis of respiratory physiology in animal models. In addition, the technique allows repeated respiratory assessment of mice allowing for longitudinal studies.

Respiratory dysfunction is one of the leading causes of morbidity and mortality in the world and the rates of mortality continue to rise. Quantitative ...

Measuring Pressure Volume Loops in the Mouse

Understanding the causes and progression of heart disease presents a significant challenge to the biomedical community. The genetic flexibility of the mouse provides great potential to explore cardiac function at the molecular level. The mouse&#39;s small size does present some challenges in regards to performing detailed cardiac phenotyping. Miniaturization and other advancements in technology have made many methods of cardiac assessment possible in the mouse. Of these, the simultaneous collection of pressure and volume data provides a detailed picture of cardiac function that is not available through any other modality. Here a detailed procedure for the collection of pressure-volume loop data is described. Included is a discussion of the principles underlying the measurements and the potential sources of error. Anesthetic management and surgical approaches are discussed in great detail as they are both critical to obtaining high quality hemodynamic measurements. The principles of hemodynamic protocol development and relevant aspects of data analysis are also addressed.

This manuscript describes a detailed protocol for the collection of pressure-volume data from the mouse.

Understanding the causes and progression of heart disease presents a significant challenge to the biomedical community. The genetic flexibility of the ...

Artificial Intelligence Approaches to Assessing Primary Cilia

Cilia are microtubule based cellular appendages that function as signaling centers for a diversity of signaling pathways in many mammalian cell types. Cilia length is highly conserved, tightly regulated, and varies between different cell types and tissues and has been implicated in directly impacting their signaling capacity. For example, cilia have been shown to alter their lengths in response to activation of ciliary G protein-coupled receptors. However, accurately and reproducibly measuring the lengths of numerous cilia is a time-consuming and labor-intensive procedure. Current approaches are also error and bias prone. Artificial intelligence (Ai) programs can be utilized to overcome many of these challenges due to capabilities that permit assimilation, manipulation, and optimization of extensive data sets. Here, we demonstrate that an Ai module can be trained to recognize cilia in images from both in vivo and in vitro samples. After using the trained Ai to identify cilia, we are able to design and rapidly utilize applications that analyze hundreds of cilia in a single sample for length, fluorescence intensity and co-localization. This unbiased approach increased our confidence and rigor when comparing samples from different primary neuronal preps in vitro as well as across different brain regions within an animal and between animals. Moreover, this technique can be used to reliably analyze cilia dynamics from any cell type and tissue in a high-throughput manner across multiple samples and treatment groups. Ultimately, Ai-based approaches will likely become standard as most fields move toward less biased and more reproducible approaches for image acquisition and analysis.

The use of artificial intelligence (Ai) to analyze images is emerging as a powerful, less biased, and rapid approach compared with commonly used methods. Here we trained Ai to recognize a cellular organelle, primary cilia, and analyze properties such as length and staining intensity in a rigorous and reproducible manner.

Cilia are microtubule based cellular appendages that function as signaling centers for a diversity of signaling pathways in many mammalian cell types. ...