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.