Relationship between the shape of intracranial pressure pulse waveform and computed tomography characteristics in patients after traumatic brain injury

BACKGROUND

Midline shift and mass lesions may occur with traumatic brain injury (TBI) and are associated with higher mortality and morbidity. The shape of intracranial pressure (ICP) pulse waveform reflects the state of cerebrospinal pressure-volume compensation which may be disturbed by brain injury. We aimed to investigate the link between ICP pulse shape and pathological computed tomography (CT) features.

METHODS

ICP recordings and CT scans from 130 TBI patients from the CENTER-TBI high-resolution sub-study were analyzed retrospectively. Midline shift, lesion volume, Marshall and Rotterdam scores were assessed in the first CT scan after admission and compared with indices derived from the first 24 h of ICP recording: mean ICP, pulse amplitude of ICP (AmpICP) and pulse shape index (PSI). A neural network model was applied to automatically group ICP pulses into four classes ranging from 1 (normal) to 4 (pathological), with PSI calculated as the weighted sum of class numbers. The relationship between each metric and CT measures was assessed using Mann-Whitney U test (groups with midline shift > 5 mm or lesions > 25 cm3 present/absent) and the Spearman correlation coefficient. Performance of ICP-derived metrics in identifying patients with pathological CT findings was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

PSI was significantly higher in patients with mass lesions (with lesions: 2.4 [1.9-3.1] vs. 1.8 [1.1-2.3] in those without; p << 0.001) and those with midline shift (2.5 [1.9-3.4] vs. 1.8 [1.2-2.4]; p < 0.001), whereas mean ICP and AmpICP were comparable. PSI was significantly correlated with the extent of midline shift, total lesion volume and the Marshall and Rotterdam scores. PSI showed AUCs > 0.7 in classification of patients as presenting pathological CT features compared to AUCs ≤ 0.6 for mean ICP and AmpICP.

CONCLUSIONS

ICP pulse shape reflects the reduction in cerebrospinal compensatory reserve related to space-occupying lesions despite comparable mean ICP and AmpICP levels. Future validation of PSI is necessary to explore its association with volume imbalance in the intracranial space and a potential complementary role to the existing monitoring strategies.

Analysis of intracranial pressure pulse waveform in traumatic brain injury patients: a CENTER-TBI study

OBJECTIVE

Intracranial pressure (ICP) pulse waveform analysis may provide valuable information about cerebrospinal pressure-volume compensation in patients with traumatic brain injury (TBI). The authors applied spectral methods to analyze ICP waveforms in terms of the pulse amplitude of ICP (AMP), high frequency centroid (HFC), and higher harmonics centroid (HHC) and also used a morphological classification approach to assess changes in the shape of ICP pulse waveforms using the pulse shape index (PSI).

METHODS

The authors included 184 patients from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) High-Resolution Sub-Study in the analysis. HFC was calculated as the average power-weighted frequency within the 4- to 15-Hz frequency range of the ICP power density spectrum. HHC was defined as the center of mass of the ICP pulse waveform harmonics from the 2nd to the 10th. PSI was defined as the weighted sum of artificial intelligence-based ICP pulse class numbers from 1 (normal pulse waveform) to 4 (pathological waveform).

RESULTS

AMP and PSI increased linearly with mean ICP. HFC increased proportionally to ICP until the upper breakpoint (average ICP of 31 mm Hg), whereas HHC slightly increased with ICP and then decreased significantly when ICP exceeded 25 mm Hg. AMP (p < 0.001), HFC (p = 0.003), and PSI (p < 0.001) were significantly greater in patients who died than in patients who survived. Among those patients with low ICP (< 15 mm Hg), AMP, PSI, and HFC were greater in those with poor outcome than in those with good outcome (all p < 0.001).

CONCLUSIONS

Whereas HFC, AMP, and PSI could be used as predictors of mortality, HHC may potentially serve as an early warning sign of intracranial hypertension. Elevated HFC, AMP, and PSI were associated with poor outcome in TBI patients with low ICP.

A graph neural network framework for causal inference in brain networks

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model's accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

Three-dimensional Monte Carlo-based voxel-wise tumor dosimetry in patients with neuroendocrine tumors who underwent 177Lu-DOTATOC therapy

Background

Patients with advanced neuroendocrine tumors (NETs) of the midgut are suitable candidates for ¹⁷⁷Lu-DOTATOC therapy. Integrated SPECT/CT systems have the potential to help improve the accuracy of patient-specific tumor dosimetry. Dose estimations to target organs are generally performed using the Medical Internal Radiation Dose scheme. We present a novel Monte Carlo-based voxel-wise dosimetry approach to determine organ- and tumor-specific total tumor doses (TTD).

Methods

A cohort of 14 patients with histologically confirmed metastasized NETs of the midgut (11 men, 3 women, 62.3 ± 11.0 years of age) underwent a total of 39 cycles of ¹⁷⁷Lu-DOTATOC therapy (mean 2.8 cycles, SD ± 1 cycle). After the first cycle of therapy, regions of interest were defined manually on the SPECT/CT images for the kidneys, the spleen, and all 198 tracer-positive tumor lesions in the field of view. Four SPECT images, taken at 4 h, 24 h, 48 h and 72 h after injection of the radiopharmaceutical, were used to determine their effective half-lives in the structures of interest. The absorbed doses were calculated by a three-dimensional dosimetry method based on Monte Carlo simulations. TTD was calculated as the sum of all products of single tumor doses with single tumor volumes divided by the sum of all tumor volumes.

Results

The average dose values per cycle were 3.41 ± 1.28 Gy (1.91–6.22 Gy) for the kidneys, 4.40 ± 2.90 Gy (1.14–11.22 Gy) for the spleen, and 9.70 ± 8.96 Gy (1.47–39.49 Gy) for all ¹⁷⁷Lu-DOTATOC-positive tumor lesions. Low- and intermediate-grade tumors (G 1–2) absorbed a higher TTD compared to high-grade tumors (G 3) (signed-rank test, p =  < 0.05). The pre-therapeutic chromogranin A (CgA) value and the TTD correlated significantly (Pearson correlation:  = 0.67, p = 0.01). Higher TTD resulted in a significant decrease of CgA after therapy.

Conclusion

These results suggest that Monte Carlo-based voxel-wise dosimetry is a very promising tool for predicting the absorbed TTD based on histological and clinical parameters.

Hybridizing EMD with cICA for fMRI Analysis of Patient Groups

Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.

A deep learning approach to radiation dose estimation

Currently methods for predicting absorbed dose after administering a radiopharmaceutical are rather crude in daily clinical practice. Most importantly, individual tissue density distributions as well as local variations of the concentration of the radiopharmaceutical are commonly neglected. The current study proposes machine learning techniques like Green's function-based empirical mode decomposition and deep learning methods on U-net architectures in conjunction with soft tissue kernel Monte Carlo (MC) simulations to overcome current limitations in precision and reliability of dose estimations for clinical dosimetric applications. We present a hybrid method (DNN-EMD) based on deep neural networks (DNN) in combination with empirical mode decomposition (EMD) techniques. The algorithm receives x-ray computed tomography (CT) tissue density maps and dose maps, estimated according to the MIRD protocol, i.e. employing whole organ S-values and related time-integrated activities (TIAs), and from measured SPECT distributions of ¹⁷⁷Lu radionuclei, and learns to predict individual absorbed dose distributions. In a second step, density maps are replaced by their intrinsic modes as deduced from an EMD analysis. The system is trained using individual full MC simulation results as reference. Data from a patient cohort of 26 subjects are reported in this study. The proposed methods were validated employing a leave-one-out cross-validation technique. Deviations of estimated dose from corresponding MC results corroborate a superior performance of the newly proposed hybrid DNN-EMD method compared to its related MIRD DVK dose calculation. Not only are the mean deviations much smaller with the new method, but also the related variances are much reduced. If intrinsic modes of the tissue density maps are input to the algorithm, variances become even further reduced though the mean deviations are less affected. The newly proposed hybrid DNN-EMD method for individualized radiation dose prediction outperforms the MIRD DVK dose calculation method. It is fast enough to be of use in daily clinical practice.

Mining EEG scalp maps of independent components related to HCT tasks

This work presents an unsupervised mining strategy, applied to an independent component analysis (ICA) of segments of data collected while participants are answering to the items of the Halstead Category Test (HCT). This new methodology was developed to achieve signal components at trial level and therefore to study signal dynamics which are not available within participants' ensemble average signals. The study will be focused on the signal component that can be elicited by the binary visual feedback which is part of the HCT protocol. The experimental study is conducted using a cohort of 58 participants.

Technical Note: A comparison of point set registration methods for electromagnetic tracking

PURPOSE

High dose rate brachytherapy applies intense and destructive radiation. A treatment plan defines radiation source dwell positions to avoid irradiating healthy tissue. The study discusses methods to quantify any positional changes of source locations along the various treatment sessions.

METHODS

Electromagnetic tracking (EMT) localizes the radiation source during the treatment sessions. But in each session the relative position of the patient relative to the filed generator is changed. Hence, the measured dwell point sets need to be registered onto each other to render them comparable. Two point set registration techniques are compared: a probabilistic method called coherent point drift (CPD) and a multidimensional scaling (MDS) technique.

RESULTS

Both enable using EMT without external registration and achieve very similar results with respect to dwell position determination of the radiation source. Still MDS achieves smaller grand average deviations (CPD-rPSR: MD = 2.55 mm, MDS-PSR: MD = 2.15 mm) between subsequent dwell position determinations, which also show less variance (CPD-rPSR: IQR = 4 mm, MDS-PSR: IQR = 3 mm). Furthermore, MDS is not based on approximations and does not need an iterative procedure to track sensor positions inside the implanted catheters.

CONCLUSION

Although both methods achieve similar results, MDS is to be preferred over rigid CPD while nonrigid CPD is unsuitable as it does not preserve topology.

A multi-variate blind source separation algorithm

BACKGROUND AND OBJECTIVE

The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation.

METHODS

The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations.

RESULTS

The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar.

CONCLUSION

The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.

On the use of multi-dimensional scaling and electromagnetic tracking in high dose rate brachytherapy

High dose rate brachytherapy affords a frequent reassurance of the precise dwell positions of the radiation source. The current investigation proposes a multi-dimensional scaling transformation of both data sets to estimate dwell positions without any external reference. Furthermore, the related distributions of dwell positions are characterized by uni-or bi-modal heavy-tailed distributions. The latter are well represented by α-stable distributions. The newly proposed data analysis provides dwell position deviations with high accuracy, and, furthermore, offers a convenient visualization of the actual shapes of the catheters which guide the radiation source during the treatment.

On the use of particle filters for electromagnetic tracking in high dose rate brachytherapy

Modern radiotherapy of female breast cancers often employs high dose rate brachytherapy, where a radioactive source is moved inside catheters, implanted in the female breast, according to a prescribed treatment plan. Source localization relative to the patient's anatomy is determined with solenoid sensors whose spatial positions are measured with an electromagnetic tracking system. Precise sensor dwell position determination is of utmost importance to assure irradiation of the cancerous tissue according to the treatment plan. We present a hybrid data analysis system which combines multi-dimensional scaling with particle filters to precisely determine sensor dwell positions in the catheters during subsequent radiation treatment sessions. Both techniques are complemented with empirical mode decomposition for the removal of superimposed breathing artifacts. We show that the hybrid model robustly and reliably determines the spatial positions of all catheters used during the treatment and precisely determines any deviations of actual sensor dwell positions from the treatment plan. The hybrid system only relies on sensor positions measured with an EMT system and relates them to the spatial positions of the implanted catheters as initially determined with a computed x-ray tomography.

A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation

OBJECTIVE

We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation.

APPROACH

EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra.

MAIN RESULTS

The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands.

SIGNIFICANCE

The proposed method provides various means to discriminate both disease pictures in a clinical environment.

Changes in Cerebral Partial Oxygen Pressure and Cerebrovascular Reactivity During Intracranial Pressure Plateau Waves

BACKGROUND

Plateau waves in intracranial pressure (ICP) are frequently recorded in neuro intensive care and are not yet fully understood. To further investigate this phenomenon, we analyzed partial pressure of cerebral oxygen (pbtO2) and a moving correlation coefficient between ICP and mean arterial blood pressure (ABP), called PRx, along with the cerebral oxygen reactivity index (ORx), which is a moving correlation coefficient between cerebral perfusion pressure (CPP) and pbtO2 in an observational study.

METHODS

We analyzed 55 plateau waves in 20 patients after severe traumatic brain injury. We calculated ABP, ABP pulse amplitude (ampABP), ICP, CPP, pbtO2, heart rate (HR), ICP pulse amplitude (ampICP), PRx, and ORx, before, during, and after each plateau wave. The analysis of variance with Bonferroni post hoc test was used to compare the differences in the variables before, during, and after the plateau wave. We considered all plateau waves, even in the same patient, independent because they are separated by long intervals.

RESULTS

We found increases for ICP and ampICP according to our operational definitions for plateau waves. PRx increased significantly (p = 0.00026), CPP (p < 0.00001) and pbtO2 (p = 0.00007) decreased significantly during the plateau waves. ABP, ampABP, and HR remained unchanged. PRx during the plateau was higher than before the onset of wave in 40 cases (73 %) with no differences in baseline parameters for those with negative and positive ΔPRx (difference during and after). ORx showed an increase during and a decrease after the plateau waves, however, not statistically significant. PbtO2 overshoot after the wave occurred in 35 times (64 %), the mean difference was 4.9 ± 4.6 Hg (mean ± SD), and we found no difference in baseline parameters between those who overshoot and those who did not overshoot.

CONCLUSIONS

Arterial blood pressure remains stable in ICP plateau waves, while cerebral autoregulatory indices show distinct changes, which indicate cerebrovascular reactivity impairment at the top of the wave. PbtO2 decreases during the waves and may show a slight overshoot after normalization. We assume that this might be due to different latencies of the cerebral blood flow and oxygen level control mechanisms. Other factors may include baseline conditions, such as pre-plateau wave cerebrovascular reactivity or pbtO2 levels, which differ between studies.

An Optimal Approach for Selecting Discriminant Regions for the Diagnosis of Alzheimer's Disease

In this work, we present a fully automatic computer-aided diagnosis method for the early diagnosis of the Alzheimer's disease. We study the distance between classes (labelled as normal controls and possible Alzheimer's disease) calculated in 116 regions of the brain using the Welchs's t-test. We select the regions with highest Welchs's t-test value as features to perform classification. Furthermore, we also study the less discriminative region according to the t-test (regions with lowest t-test absolute values) in order to use them as reference. We show that the mean and standard deviation of the intensity values in these two regions, the less and most discriminative according to the Welch's ttest, can be combined as a vector. The modulus and phase of this vector reveal statistical differences between groups which can be used to improve the classification task. We show how they can be used as input for a support vector machine classifier. The proposed methodology is tested in a SPECT brain database of 70 SPECT brain images yielding an accuracy up to 91.5% for a wide range of selected voxels.

Functional Biomedical Images of Alzheimer's Disease. A Green's Function-based Empirical Mode Decomposition Study

Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D-EMD) provides means to analyze such images. It decomposes the latter into characteristic modes which represent textures on different spatial scales. These textures provide informative features for subsequent classification purposes. The study proposes a new EMD variant which relies on a Green's function based estimation method including a tension parameter to fast and reliably estimate the envelope hypersurfaces interpolating extremal points of the two-dimensional intensity distrubution of the images. The new method represents a fast and stable bi-dimensional EMD which speeds up computations roughly 100-fold. In combination with proper classifiers these exploratory feature extraction techniques can form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from functional images alone. PET images of subjects suffering from Alzheimer's disease are taken to illustrate this ability.

Plateau Waves of Intracranial Pressure and Partial Pressure of Cerebral Oxygen

This study investigates 55 intracranial pressure (ICP) plateau waves recorded in 20 patients after severe traumatic brain injury (TBI) with a focus on a moving correlation coefficient between mean arterial pressure (ABP) and ICP, called PRx, which serves as a marker of cerebrovascular reactivity, and a moving correlation coefficient between ABP and cerebral partial pressure of oxygen (pbtO2), called ORx, which serves as a marker for cerebral oxygen reactivity. ICP and ICPamplitude increased significantly during the plateau waves, whereas CPP and pbtO2 decreased significantly. ABP, ABP amplitude, and heart rate remained unchanged. In 73 % of plateau waves PRx increased during the wave. ORx showed an increase during and a decrease after the plateau waves, which was not statistically significant. Our data show profound cerebral vasoparalysis on top of the wave and, to a lesser extent, impairment of cerebral oxygen reactivity. The different behavior of the indices may be due to the different latencies of the cerebral blood flow and oxygen level control mechanisms. While cerebrovascular reactivity is a rapidly reacting mechanism, cerebral oxygen reactivity is slower.

EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition

BACKGROUND

Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.

NEW METHOD

EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.

RESULTS

EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.

COMPARISON WITH EXISTING METHODS

EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.

CONCLUSIONS

EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.

Short pressure reactivity index versus long pressure reactivity index in the management of traumatic brain injury

OBJECT

The pressure reactivity index (PRx) correlates with outcome after traumatic brain injury (TBI) and is used to calculate optimal cerebral perfusion pressure (CPPopt). The PRx is a correlation coefficient between slow, spontaneous changes (0.003–0.05 Hz) in intracranial pressure (ICP) and arterial blood pressure (ABP). A novel index—the so-called long PRx (L-PRx)—that considers ABP and ICP changes (0.0008–0.008 Hz) was proposed.

METHODS

The authors compared PRx and L-PRx for 6-month outcome prediction and CPPopt calculation in 307 patients with TBI. The PRx- and L-PRx–based CPPopt were determined and the predictive power and discriminant abilities were compared.

RESULTS

The PRx and L-PRx correlation was good (R = 0.7, p < 0.00001; Spearman test). The PRx, age, CPP, and Glasgow Coma Scale score but not L-PRx were significant fatal outcome predictors (death and persistent vegetative state). There was a significant difference between the areas under the receiver operating characteristic curves calculated for PRx and L-PRx (0.61 ± 0.04 vs 0.51 ± 0.04; z-statistic = −3.26, p = 0.011), which indicates a better ability by PRx than L-PRx to predict fatal outcome. The CPPopt was higher for L-PRx than for PRx, without a statistical difference (median CPPopt for L-PRx: 76.9 mm Hg, interquartile range [IQR] ± 10.1 mm Hg; median CPPopt for PRx: 74.7 mm Hg, IQR ± 8.2 mm Hg). Death was associated with CPP below CPPopt for PRx (χ2 = 30.6, p < 0.00001), and severe disability was associated with CPP above CPPopt for PRx (χ2 = 7.8, p = 0.005). These relationships were not statistically significant for CPPopt for L-PRx.

CONCLUSIONS

The PRx is superior to the L-PRx for TBI outcome prediction. Individual CPPopt for L-PRx and PRx are not statistically different. Deviations between CPP and CPPopt for PRx are relevant for outcome prediction; those between CPP and CPPopt for L-PRx are not. The PRx uses the entire B-wave spectrum for index calculation, whereas the L-PRX covers only one-third of it. This may explain the performance discrepancy.

Cerebrovascular pressure reactivity and cerebral oxygen regulation after severe head injury

BACKGROUND

To investigate the relationship between cerebrovascular pressure reactivity and cerebral oxygen regulation after head injury.

METHODS

Continuous monitoring of the partial pressure of brain tissue oxygen (PbrO2), mean arterial blood pressure (MAP), and intracranial pressure (ICP) in 11 patients. The cerebrovascular pressure reactivity index (PRx) was calculated as the moving correlation coefficient between MAP and ICP. For assessment of the cerebral oxygen regulation system a brain tissue oxygen response (TOR) was calculated, where the response of PbrO2 to an increase of the arterial oxygen through ventilation with 100 % oxygen for 15 min is tested. Arterial blood gas analysis was performed before and after changing ventilator settings.

RESULTS

Arterial oxygen increased from 108 ± 6 mmHg to 494 ± 68 mmHg during ventilation with 100 % oxygen. PbrO2 increased from 28 ± 7 mmHg to 78 ± 29 mmHg, resulting in a mean TOR of 0.48 ± 0.24. Mean PRx was 0.05 ± 0.22. The correlation between PRx and TOR was r = 0.69, P = 0.019. The correlation of PRx and TOR with the Glasgow outcome scale at 6 months was r = 0.47, P = 0.142; and r = -0.33, P = 0.32, respectively.

CONCLUSIONS

The results suggest a strong link between cerebrovascular pressure reactivity and the brain's ability to control for its extracellular oxygen content. Their simultaneous impairment indicates that their common actuating element for cerebral blood flow control, the cerebral resistance vessels, are equally impaired in their ability to regulate for MAP fluctuations and changes in brain oxygen.

Exploratory matrix factorization for PET image analysis

Features are extracted from PET images employing exploratory matrix factorization techniques such as nonnegative matrix factorization (NMF). Appropriate features are fed into classifiers such as a support vector machine or a random forest tree classifier. An automatic feature extraction and classification is achieved with high classification rate which is robust and reliable and can help in an early diagnosis of Alzheimer's disease.

Hemispheric asymmetry and temporal profiles of cerebral pressure autoregulation in head injury

A moving correlation index (Mx-ABP) between arterial blood pressure (ABP) and mean middle cerebral artery blood flow velocity (CBFV) can be used to monitor dynamic cerebrovascular autoregulation (CA) after traumatic brain injury (TBI). In this study we examined hemispheric CA asymmetry and temporal CA profiles, their relationship with ABP and CBFV, and their prognostic relevance. Mx-ABP was calculated for each hemisphere in 25 TBI patients second-daily for as long as they were receiving sedation and analgesia. Forty-nine recordings were obtained, between one and six per patient. Four time periods were defined: immediate--postinjury days (PID) 0 and 1; early--PID 2 and 3; intermediate--PID 4 and 5, and late--PID 6 and later. GOS was estimated at discharge, GOS 4 and 5 were considered favorable (15 patients) and GOS 1-3 unfavorable outcome (10 patients). A Mx difference >0.2 was classified as hemispheric asymmetry (HA). HA was observed at least once in 12 of the 25 patients (48%) and in 18 of 49 recordings (37%). It was observed during all time periods: 35%, 43%, 25%, 43%, respectively, and was not related to outcome. There was no difference in mean CBFV or ABP between patients with and without HA. HA was not related to interhemispheric CBFV differences. A significant improvement in Mx was seen over time. Hemispheric CA asymmetry is common after traumatic brain injury. It does not bear significant clinical or predictive relevance, and it is unrelated to CBFV or ABP. CA is most profoundly disturbed during the immediate postinjury phase and improves gradually during the ICU course. Further studies are needed to investigate CA during post ICU recovery and rehabilitation.

Spatiotemporal group ICA applied to fMRI datasets

Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on a priori hypotheses and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. We present fMRI data of two different subject-groups (young and old), which performed a modified Wisconsin Card Sorting Test (WCST). Spatiotemporal ICA and SPM-generated brain maps of the subject data are compared. For the group analysis a singular value decomposition approach was used. Spatiotemporal ICA reveals a frontoparietal network being activated while subjects performed different variants of the WCST. Contrary to the SPM analysis, ICA analysis revealed significant differences between young and old subjects as well as significant within-group differences.While young subjects showed with increasing task demands (A>>B>>C) increasing activation of the right lateral prefrontal cortex and of the medial orbito-frontal cortex, old subjects showed no such gradient in activation pattern and appeared to be more distributed.

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.