Comparison of unsupervised and supervised gene selection methods

Modern machine learning methods based on matrix decomposition techniques like Independent Component Analysis (ICA) provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield informative expression modes (ICA) which are considered indicative of underlying regulatory processes. Their most strongly expressed genes represent marker genes for classification of the tissue samples under investigation. Comparison with supervised gene selection methods based on statistical scores or support vector machines corroborate these findings. The method is applied to macrophages loaded/de-loaded with chemically modified low density lipids.

Subspace techniques to remove artifacts from EEG: a quantitative analysis

In this work we discuss and apply projective subspace techniques to both multichannel as well as single channel recordings. The single-channel approach is based on singular spectrum analysis(SSA) and the multichannel approach uses the extended infomax algorithm which is implemented in the opensource toolbox EEGLAB. Both approaches will be evaluated using artificial mixtures of a set of selected EEG signals. The latter were selected visually to contain as the dominant activity one of the characteristic bands of an electroencephalogram (EEG). The evaluation is performed both in the time and frequency domain by using correlation coefficients and coherence function, respectively.

Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica

The analysis of large-scale gene expression profiles is still a demanding and extensive task. Modern machine learning and data mining techniques developed in linear algebra, like Independent Component Analysis (ICA), become increasingly popular as appropriate tools for analyzing microarray data. We applied ICA to analyze kinetic gene expression profiles of human monocyte derived macrophages (MDM) from three different donors infected with Francisella tularensis holartica and compared them to more classical methods like hierarchical clustering. Results were compared using a pathway analysis tool, based on the Gene Ontology and the MeSH database. We could show that both methods lead to time-dependent gene regulatory patterns which fit well to known TNFalpha induced immune responses. In comparison, the nonexclusive attribute of ICA results in a more detailed view and a higher resolution in time dependent behavior of the immune response genes. Additionally, we identified NFkappaB as one of the main regulatory genes during response to F. tularensis infection.

Speech enhancement in discontinuous transmission systems using the constrained-stability least-mean-squares algorithm

In this paper a novel constrained-stability least-mean-squares (LMS) algorithm for filtering speech sounds is proposed in the adaptive noise cancellation (ANC) problem. It is based on the minimization of the squared Euclidean norm of the weight vector change under a stability constraint over the a posteriori estimation errors. To this purpose, the Lagrangian methodology has been used in order to propose a nonlinear adaptation in terms of the product of differential input and error. Convergence analysis is also studied in terms of the evolution of the natural modes to the optimal Wiener-Hopf solution so that the stability performance depends exclusively on the adaptation parameter mu and the eigenvalues of the difference matrix DeltaR(1). The algorithm shows superior performance over the referenced algorithms in the ANC problem of speech discontinuous transmission systems, which are characterized by rapid transitions of the desired signal. The experimental analysis carried out on the AURORA 3 speech databases provides an extensive performance evaluation together with an exhaustive comparison to the standard LMS algorithms, i.e., the normalized LMS (NLMS), and other recently reported LMS algorithms such as the modified NLMS, the error nonlinearity LMS, or the normalized data nonlinearity LMS adaptation.

A lesson learned from jugular venous oximetry

The authors describe the case of a severely head injured patient whose profound cerebral infarction was clearly indicated by prolonged desaturation on jugular venous oximetry. Shortly thereafter jugular venous oxygen saturation returned to normal stable values and measured within normal limits for the next 24 h. As demonstrated by a computed tomography (CT) scan, these values appear to have represented a jugular mixture of significant amount of cerebral blood that had passed through infarcted tissue and remained highly saturated. This is a very graphic example of the misleading influence that regional flow-metabolic inhomogeneities can have on jugular venous saturation and it emphasizes that cerebral ischemia can be easily missed if no information on cerebral blood flow or regional metabolism is available.

Knowledge-based gene expression classification via matrix factorization

Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted features are considered indicative of underlying regulatory processes. They can as well be applied to the classification of gene expression datasets by grouping samples into different categories for diagnostic purposes or group genes into functional categories for further investigation of related metabolic pathways and regulatory networks.

Results: In this study we focus on unsupervised matrix factorization techniques and apply ICA and sparse NMF to microarray datasets. The latter monitor the gene expression levels of human peripheral blood cells during differentiation from monocytes to macrophages. We show that these tools are able to identify relevant signatures in the deduced component matrices and extract informative sets of marker genes from these gene expression profiles. The methods rely on the joint discriminative power of a set of marker genes rather than on single marker genes. With these sets of marker genes, corroborated by leave-one-out or random forest cross-validation, the datasets could easily be classified into related diagnostic categories. The latter correspond to either monocytes versus macrophages or healthy vs Niemann Pick C disease patients.

Supplementary information:Supplementary data are available at Bioinformatics online.

Contact:elmar.lang@biologie.uni-regensburg.de

Routes to identify marker genes for microarray classification

Support vector machines are applied to extract marker genes from various microarray data sets: Breast Cancer, Leukemia and Monocyte - Macrophage Differentiation to ease classification of related pathologies or characterize related gene regulation pathways.

Greedy kernel PCA applied to single-channel EEG recordings

In this work, we propose the correction of univariate single channel EEGs using a kernel technique. The EEG signal is embedded in its time-delayed coordinates obtaining a multivariate signal. A kernel subspace technique is used for denoising and artefact extraction. The proposed kernel method follows a greedy approach to use a reduced data set to compute a new basis onto which to project the mapped data in feature space. The pre-image of the reconstructed multivariate signal is computed and the embedding is reverted. The resultant signal is the high amplitude artifact which must be subtracted from the original signal to obtain a corrected version of the underlying signal.

How to extract marker genes from microarray data sets

In this study we focus on classification tasks and apply matrix factorization techniques like principal component analysis (PCA), independent component analysis (ICA) and non-negative matrix factorization (NMF) to a microarray data set. The latter monitors the gene expression levels (GEL) of mononcytes and macrophages during and after differentiation. We show that these tools are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles (GEPs) without the need for extensive data bank search for appropriate functional annotations. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories.

Phase shift and correlation coefficient measurement of cerebral autoregulation during deep breathing in traumatic brain injury (TBI)

BACKGROUND

Impairment of cerebral autoregulation is known to adversely affect outcome following traumatic brain injury (TBI). The phase shift (PS) method of cerebral autoregulation (CA) assessment describes the time lag between fluctuations in arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) in the middle cerebral artery. An alternative method (Mx-ABP) is based on the statistical correlation between ABP and CBFV waveforms over time. We compared these two indices in a cohort of severely head injured patients undergoing controlled, 6-breaths-per-minute ventilation.

METHODS

PS and Mx-ABP were calculated from 33 recordings of CBFV and MAP in 22 patients with TBI. Spearman's correlation coefficient was used to assess the agreement between PS and Mx-ABP. The relationship between ICP slow wave amplitude, MAP slow wave amplitude and mean ICP was also examined.

FINDINGS

Mean values for Mx-ABP and PS were 0.44 +/- 0.27, and 49 +/- 26 (degrees), respectively. PS correlated significantly with Mx-ABP (r = -0.648, p < 0.001). A Bland-Altman plot of normalised Mx-ABP and Phase Shift values showed no significant bias or relationship (mean difference = 0.0004, r = -0.037, p = 0.852). During the test procedure, ICP fluctuated in an approximately sinusoidal fashion, with a mean amplitude of 4.96 +/- 2.72 mmHg (peak to peak). The magnitude of ICP fluctuation during deep breathing correlated weakly but significantly with mean ICP (r = 0.391, p < 0.05) and with the amplitude of ABP fluctuations (r = 0.625, p < 0.0005).

CONCLUSIONS

Phase shift and Mx-ABP in TBI are well correlated. Deep breathing presents as an effective tool with which to assess autoregulation using the phase shift method.

ICA analysis of retina images for glaucoma classification

Glaucoma represent one of the most frequent causes of partial loss of the visual field. It comes along with an ongoing destruction of the optic nerve caused by an increased pressure of the eye liquid. The disease becomes obvious from investigations of the retina with scanning laser microscopes. In this report an image analysis and classification system based on independent component analysis and k-nearest-neighbor classification is proposed. The method is tested with 120 selected retina images collected with the Heidelberg Retina Tomograph and achieves a classification rate of 91%.

Direct cerebral oxygenation monitoring--a systematic review of recent publications

This review has been compiled to assess publications related to the clinical application of direct cerebral tissue oxygenation (PtiO2) monitoring published in international, peer-reviewed scientific journals. Its goal was to extract relevant, i.e. positive and negative information on indications, clinical application, safety issues and impact on clinical situations as well as treatment strategies in neurosurgery, neurosurgical anaesthesiology, neurosurgical intensive care, neurology and related specialties. For completeness' sake it also presents some related basic science research. PtiO2 monitoring technology is a safe and valuable cerebral monitoring device in neurocritical care. Although a randomized outcome study is not available its clinical utility has repeatedly been clearly confirmed because it adds a monitoring parameter, independent from established cerebral monitoring devices. It offers new insights into cerebral physiology and pathophysiology. Pathologic values have been established in peer-reviewed research, which are not only relevant to outcome but are treatable. The benefits clearly outweigh the risks, which remains unchallenged in all publications retrieved. It is particularly attractive because it offers continuous, real-time data and is available at the bedside.

Automatic removal of high-amplitude artefacts from single-channel electroencephalograms

In this work, we present a method to extract high-amplitude artefacts from single channel electroencephalogram (EEG) signals. The method is called local singular spectrum analysis (local SSA). It is based on a principal component analysis (PCA) applied to clusters of the multidimensional signals obtained after embedding the signals in their time-delayed coordinates. The decomposition of the multidimensional signals in each cluster is achieved by relating the largest eigenvalues with the large amplitude artefact component of the embedded signal. Then by reverting the clustering and embedding processes, the high-amplitude artefact can be extracted. Subtracting it from the original signal a corrected EEG signal results. The algorithm is applied to segments of real EEG recordings containing paroxysmal epileptiform activity contaminated by large EOG artefacts. We will show that the method can be applied also in parallel to correct all channels that present high-amplitude artefacts like ocular movement interferences or high-amplitude low frequency baseline drifts. The extracted artefacts as well as the corrected EEG will be presented.

On the use of simulated annealing to automatically assign decorrelated components in second-order blind source separation

In this paper, an automatic assignment tool, called BSS-AutoAssign, for artifact-related decorrelated components within a second-order blind source separation (BSS) is presented. The latter is based on the recently proposed algorithm dAMUSE, which provides an elegant solution to both the BSS and the denoising problem simultaneously. BSS-AutoAssign uses a local principal component analysis (PCA)to approximate the artifact signal and defines a suitable cost function which is optimized using simulated annealing. The algorithms dAMUSE plus BSS-AutoAssign are illustrated by applying them to the separation of water artifacts from two-dimensional nuclear overhauser enhancement (2-D NOESY) spectroscopy signals of proteins dissolved in water.

Cerebrovascular autoregulation as a neuroimaging tool

Functional transcranial Doppler (fTCD) sonography provides a high temporal resolution measure of blood flow and has over the years proved to be a valuable tool in the clinical evaluation of patients with cerebrovascular disorders. More recently, due to advances in physics and computing, it has become possible to derive indices of cerebrovascular autoregulation (CA) as well as cerebrovascular pressure reactivity (CR), using non-invasive techniques. These indices provide a dynamic representation of the brain's regulatory blood flow mechanisms not only in pathological states but also in health. However, whilst the temporal resolution of these regulatory indices is very good, spatially, the localization of brain regions remains very poor, thus limiting its brain mapping capacity. Functional MRI, on the contrary, is a brain-imaging technique that operates on similar blood flow principles; however, unlike fTCD, it provides high spatial resolution. Because both fTCD and fMRI determine blood flow-dependant imaging parameters, the coupling of fTCD with fMRI may provide greater insight into brain function by virtue of the combined enhanced temporal and spatial resolution that each technique affords. This review summarizes the fTCD technique with particular emphasis on the CA and CR indices and their relationship in traumatic brain injury as well as in health.

Nonaccidental head injuries in children: a Sydney experience

OBJECT

The purpose of this study was to evaluate the demographics, clinical and radiological features, and clinical outcomes of nonaccidental pediatric head injury.

METHODS

The authors reviewed 65 consecutive cases of nonaccidental head injury in a single pediatric neurosurgical unit during a period of 7 years. The mean patient age was 8.2 months (range 0.5-46 months). There were 39 boys and 26 girls. A history of abuse was present in 24% of families. There was a high incidence of family disruption, substance abuse, and premature birth. Fathers were the most common perpetrators. Fifteen patients had a Glasgow Coma Scale score of less than 10. Thirty-five patients had seizures on or preceding admission. Subdural hematoma was the most common finding (81.5%). Skull fractures were present in 36.9% of patients, skeletal injuries in 50% (of which 67% were subclinical), and retinal hemorrhages in 59%. The radiological finding of ischemia or edema had a significant correlation with a poor outcome. Magnetic resonance imaging revealed additional pathological findings not visible on computerized tomography scanning in 18 (49%) of 37 cases. Surgery was performed in 17 patients; recurrence of the subdural collection occurred in 46% of them. In this group, reevacuations were followed by further recurrences, and a subdural-peritoneal shunt was eventually required. Four patients died. Of the 56 surviving patients reviewed on a long-term basis, 19 made a full recovery, and epilepsy was reported in 17%.

CONCLUSIONS

Magnetic resonance imaging should be routinely used in depicting ischemia, which is associated with a poor outcome. The high incidence of subclinical skeletal injuries stresses the importance of assessment of suspected cases of nonaccidental trauma with skeletal surveys and bone scans. Recurrence of subdural collection following burr hole drainage is common and is best treated with a subdural-peritoneal shunt.

Cerebral autoregulation and ageing

Little is known about the effects of ageing on cerebral autoregulation (CA). To examine the relationship between age and CA in adults, we conducted a prospective study using a non-invasive protocol without external stimuli. We studied 32 subjects, aged 23-68 years. They were assigned to a young group (28+/-5 years) and an old group (54+/-8 years). The groups were sex-matched. Transcranial Doppler ultrasonography (TCD) was used to record bilateral middle cerebral artery flow velocities (CBFV, cm/sec). Noninvasive beat-to-beat tonometric arterial blood pressure (ABP) measurement of the radial artery was used to record spontaneous blood pressure fluctuations. The Mx, an index of dynamic cerebral autoregulation (dCA), was calculated from a moving correlation between ABP and CBFV. We did not find a correlation between age and Mx. No statistically significant difference in the Mx between the groups (0.27+/-0.23, young, vs. 0.37+/-0.24, old) was demonstrated. Age does not affect dynamic cerebral autoregulation assessed by the Mx index in healthy adult subjects. This study supports findings from previous papers wherein CA was measured with protocols which require external stimuli. Further studies are needed to determine CA in subjects above 70 years of age.

Multimodality Monitoring in Severe Traumatic Brain Injury: The Role of Brain Tissue Oxygenation Monitoring

Traumatic brain injury (TBI) is a major cause of morbidity and mortality with widespread social, personal, and financial implications for those who survive. TBI is caused by four main events: motor vehicle accidents, sporting injuries, falls, and assaults. Similarly to international statistics, annual incidence reports for TBI in Australia are between 100 and 288 per 100,000. Regardless of the cause of TBI, molecular and cellular derangements occur that can lead to neuronal cell death. Axonal transport disruption, ionic disruption, reduced energy formation, glutamate excitotoxicity, and free radical formation all contribute to the complex pathophysiological process of TBI-related neuronal death. Targeted pharmacological therapy has not proved beneficial in improving patient outcome, and monitoring and maintenance of various physiological parameters is the mainstay of current therapy. Parameters monitored include arterial blood pressure, blood gases, intracranial pressure, cerebral perfusion pressure, cerebral blood flow, and direct brain tissue oxygen measurement (ptiO2). Currently, indirect brain oximetry is used for cerebral oxygenation determination, which provides some information regarding global oxygenation levels. A newly developed oximetry technique, has shown promising results for the early detection of cerebral ischemia. ptiO2 monitoring provides a safe, easy, and sensitive method of regional brain oximetry, providing a greater understanding of neurophysiological derangements and the potential for correcting abnormal oxygenation earlier, thus improving patient outcome. This article reviews the current status of bedside monitoring for patients with TBI and considers whether ptiO2 has a role in the modern intensive care setting.

Differentiation of cerebral tumors using multi-section echo planar MR perfusion imaging

OBJECTIVE

We have investigated the performance of magnetic resonance (MR) perfusion imaging to differentiate between astrocytomas grade II, grade III and glioblastomas in a prospective study.

MATERIALS AND METHODS

In 33 patients with suspected supratentorial primary cerebral tumors we performed multi-section Echo Planar MR perfusion imaging. Regional cerebral blood volume (rCBV) maps were calculated and the maximum rCBV was determined from the entire lesion. This value was divided by the mean rCBV value from the contralateral side, which provided the rCBV index used in this study. The rCBV index was correlated with the histological tumor classification after stereotactic biopsy (n=7) or open resection (n=26).

RESULTS

The maximum rCBV index was 1.2+/-0.8 for grade II astrocytomas (n=3), 4.0+/-1.2 for grade III astrocytomas (n=13), and 10.3+/-3.3 for glioblastomas (n=17). The difference between grade III astrocytomas and glioblastomas was highly significant (P<0.001).

DISCUSSION AND CONCLUSION

The rCBV index measured with multi-section Echo Planar MR perfusion is capable of differentiating grade III astrocytomas from glioblastomas. It serves as an additional parameter to establish a diagnosis in cases where it is not possible to clearly differentiate between these types of tumors on the basis of conventional MR imaging. MR perfusion imaging also provides information about spatial heterogeneities within a tumor which might improve diagnostic performance. This technology may also be of interest for follow-up examinations after histological diagnosis and further treatment.

Cerebral vasomotor reactivity testing in head injury: the link between pressure and flow

BACKGROUND

It has been suggested that a moving correlation index between mean arterial blood pressure and intracranial pressure, called PRx, can be used to monitor and quantify cerebral vasomotor reactivity in patients with head injury.

OBJECTIVES

To validate this index and study its relation with cerebral blood flow velocity and cerebral autoregulation; and to identify variables associated with impairment or preservation of cerebral vasomotor reactivity.

METHODS

The PRx was validated in a prospective study of 40 head injured patients. A PRx value of less than 0.3 indicates intact cerebral vasomotor reactivity, and a value of more than 0.3, impaired reactivity. Arterial blood pressure, intracranial pressure, mean cerebral perfusion pressure, and cerebral blood flow velocity, measured bilaterally with transcranial Doppler ultrasound, were recorded. Dynamic cerebrovascular autoregulation was measured using a moving correlation coefficient between arterial blood pressure and cerebral blood flow velocity, the Mx, for each cerebral hemisphere. All variables were compared in patients with intact and impaired cerebral vasomotor reactivity.

RESULTS

No correlation between arterial blood pressure or cerebral perfusion pressure and cerebral blood flow velocity was seen in 19 patients with intact cerebral vasomotor reactivity. In contrast, the correlation between these variables was significant in 21 patients with impaired cerebral vasomotor reactivity, whose cerebral autoregulation was reduced. There was no correlation with intracranial pressure, arterial blood pressure, cerebral perfusion pressure, or interhemispheric cerebral autoregulation differences, but the values for these indices were largely within normal limits.

CONCLUSIONS

The PRx is valid for monitoring and quantifying cerebral vasomotor reactivity in patients with head injury. This intracranial pressure based index reflects changes in cerebral blood flow and cerebral autoregulatory capacity, suggesting a close link between blood flow and intracranial pressure in head injured patients. This explains why increases in arterial blood pressure and cerebral perfusion pressure may be useful for reducing intracranial pressure in selected head injured patients (those with intact cerebral vasomotor reactivity).

Cerebral edema leading to decompressive craniectomy: an assessment of the preceding clinical and neuromonitoring trends

The aim of this study was to examine the pre-operative clinical and neuromonitoring courses in patients with a decompressive craniectomy to assess and to compare clinical and neuromonitoring signs indicating extensive cerebral edema. We conducted a retrospective analysis of the clinical signs and courses of simultaneous monitoring of intracranial pressure (ICP) and cerebral oxygenation (PtiO2) in 26 consecutive patients who were sedated and treated with a decompressive craniectomy due to extensive cerebral edema after aneurysmal subarachnoid hemorrhage (SAH) (n = 20) or severe head injury (SHI) (n = 6). Pathological monitoring trends always preceded clinical deterioration. In 18 of 26 patients extensive cerebral edema was indicated solely by increasing ICP > 20 mmHg or decreasing PtiO2 < 10 mmHg or both. Anisocoria occurred in only 8 of 26 patients. As opposed to SHI patients, 9 of 20 SAH patients showed decreasing PtiO2 as first warning sign clearly before neurological deterioration or ICP increase. This series shows the utility of combined ICP and PtiO2 monitoring in patients who develop extensive cerebral edema. Pathological monitoring trends indicate deterioration prior to clinical signs which offers a wider therapeutical window. PtiO2 monitoring appears to be particularly valuable after aneurysmal SAH as adjunct to ICP monitoring and CT imaging.

Noninvasive cerebrovascular autoregulation assessment in traumatic brain injury: validation and utility

A moving correlation index (Mx-CPP) of cerebral perfusion pressure (CPP) and mean middle cerebral artery blood flow velocity (CBFV) allows continuous monitoring of dynamic cerebral autoregulation (CA) in patients with severe traumatic brain injury (TBI). In this study we validated Mx-CPP for TBI, examined its prognostic relevance, and assessed its relationship with arterial blood pressure (ABP), CPP, intracranial pressure (ICP), and CBFV. We tested whether using ABP instead of CPP for Mx calculation (Mx-ABP) produces similar results. Mx was calculated for each hemisphere in 37 TBI patients during the first 5 days of treatment. All patients received sedation and analgesia. CPP and bilateral CBFV were recorded, and GOS was estimated at discharge. Both Mx indices were calculated from 10,000 data points sampled at 57.4Hz. Mx-CPP > 0.3 indicates impaired CA; in these patients CPP had a significant positive correlation with CBFV, confirming failure of CA, while in those with Mx < 0.3, CPP was not correlated with CBFV, indicating intact CA. These findings were confirmed for Mx-ABP. We found a significant correlation between impaired CA, indicated by Mx-CPP and Mx-ABP, and poor outcome for TBI patients. ABP, CPP, ICP, and CBFV were not correlated with CA but it must be noted that our average CPP was considerably higher than in other studies. This study confirms the validity of this index to demonstrate CA preservation or failure in TBI. This index is also valid if ABP is used instead of CPP, which eliminates the need for invasive ICP measurements for CA assessment. An unfavorable outcome is associated with early CA failure. Further studies using the Mx-ABP will reveal whether CA improves along with patients' clinical improvement.

Tissue oxygen reactivity and cerebral autoregulation after severe traumatic brain injury

OBJECTIVE

To study the relationship between arterial blood pressure, intracranial pressure, directly measured brain tissue oxygenation (PtiO2), and middle cerebral artery blood flow velocity in severely head-injured patients.

DESIGN

Prospective study.

SETTING

Neurosurgical intensive care unit. PATIENTS A total of 14 patients with severe head injury.

INTERVENTIONS

Pharmacologic blood pressure manipulations using norepinephrine.

MEASUREMENTS AND MAIN RESULTS

We assessed the magnitude of PtiO2 related to changes in cerebral perfusion pressure in 12 of the patients. We calculated in all the static rate of regulation, which is an index to describe the change of cerebrovascular resistance, using cerebral artery blood flow velocity in relation to changing cerebral perfusion pressure. Finally, we calculated the rate of change in PtiO2, which quantifies the percentage of change in PtiO2 divided by the percentage of change in cerebral perfusion pressure. It is a new marker for cerebral tissue oxygen regulation based on direct measurement of PtiO2. There was a plateau phase for the cerebral perfusion pressure-PtiO2 relation that was similar to the autoregulatory plateau seen in the relationship between cerebral perfusion pressure and cerebral artery blood flow velocity. The rate of change in PtiO2 demonstrated a significant correlation with the static rate of regulation (R = -.61, <.05). A decrease in intracranial pressure when arterial blood pressure increased from 70 to 90 mm Hg was strongly correlated with static rate of regulation (R =.79, <.001). CONCLUSIONS Cerebral tissue PO2 demonstrates a plateau phase similar to what is known about cerebral blood flow velocity, which suggests a close link between cerebral blood flow and oxygenation. Static cerebral autoregulation is significantly correlated with cerebral tissue oxygen reactivity.

Noninvasive intracranial compliance monitoring. Technical note and clinical results

Although invasive measurement of intracranial pressure (ICP) involving high-resolution waveform analysis allows assessment of intracranial compliance (ICC), it is only feasible in a few selected neurosurgical conditions. Intracranial compliance can be assessed using the high-frequency centroid (HFC), which is the power-weighted mean frequency within the 4 to 15-Hz band of the ICP waveform. The authors have systematically tested the utility, performance, and reliability of a noninvasive monitor of ICC. The underlying principle of this device is that the ICP transmission and its infrasonic waves are transmitted through the inner ear toward the tympanic membrane. If the outer ear is sealed in an airtight fashion, motions of the tympanic membrane cause air pressure fluctuations that can be recorded using a special sensor. The authors compared the HFC calculated from an intraparenchymal ICP sensor with that obtained simultaneously from an ipsilaterally placed noninvasive device during half of a respiratory cycle (peak to baseline) as well as for three random samples of three heart cycles. They analyzed 32 sessions in 13 patients in whom mechanical ventilation had been established. In four (11%) of 36 sessions they could not demonstrate an adequate signal. For the peak-to-baseline cycle, the mean invasively recorded HFC was 8.05 +/- 0.55 Hz (range 6.7-9 Hz) whereas the mean noninvasively recorded HFC was 8.04 +/- 0.49 Hz (range 7-9.3 Hz). The ICP was 8.5 +/- 5 mm Hg (range 2-24 mm Hg). For the three heart cycles randomly sampled, the values were 7.73 +/- 0.51 Hz (range 6.7-8.6 Hz) and 7.76 +/- 0.56 mm Hg (range 6.5-8.8 mm Hg), respectively. This device allows noninvasive assessment of ICC based on the HFC waveform analysis that is equivalent to that obtained by invasive intraparenchymal recording. The monitoring device may become a valuable tool for monitoring parameters in patients in whom placement of an intracranial sensor is not feasible but assessment of ICC as an alternative to ICP measurement is desired.