Time-variant Analysis of Fast-fMRI and Dynamic Contrast Agent MRI Sequences as Examples of 4-dimensional Image Analysis

Objectives: Image sequences with time-varying information content need appropriate analysis strategies. The exploration of directed information transfer (interactions) between neuronal assemblies is one of the most important aims of current functional MRI (fMRI) analysis. Additionally, we examined perfusion maps in dynamic contrast agent MRI sequences of stroke patients. In this investigation, the focus centers on distinguishing between brain areas with normal and reduced perfusion on the basis of the dynamics of contrast agent inflow and washout.

Methods: Fast fMRI sequences were analyzed with time-variant Granger causality (tvGC). The tvGC is based on a time-variant autoregressive model and is used for the quantification of the directed information transfer between activated brain areas. Generalized Dynamic Neural Networks (GDNN) with time-variant weights were applied on dynamic contrast agent MRI sequences as a nonlinear operator in order to enhance differences in the signal courses of pixels of normal and injured tissues.

Results: A simple motor task (self-paced finger tapping) is used in an fMRI design to investigate directed interactions between defined brain areas. A significant information transfer can be determined for the direction primary motor cortex to supplementary motor area during a short time period of about five seconds after stimulus. The analysis of dynamic contrast agent MRI sequences demonstrates that the trained GDNN enables a reliable tissue classification. Three classes are of interest: normal tissue, tissue at risk for death, and dead tissue.

Conclusions: The time-variant multivariate analysis of directed information transfer derived from fMRI sequences and the computation of perfusion maps by GDNN demonstrate that dynamic analysis methods are essential tools for 4D image analysis.

Assignment of Empirical Mode Decomposition Components and Its Application to Biomedical Signals

Objectives: Empirical mode decomposition (EMD) is a frequently used signal processing approach which adaptively decomposes a signal into a set of narrow-band components known as intrinsic mode functions (IMFs). For multi-trial, multivariate (multiple simultaneous recordings), and multi-subject analyses the number and signal properties of the IMFs can deviate from each other between trials, channels and subjects. A further processing of IMFs, e.g. a simple ensemble averaging, should determine which IMFs of one signal correspond to IMFs from another signal. When the signal properties have similar characteristics, the IMFs are assigned to each other. This problem is known as correspondence problem.

Methods: From the mathematical point of view, in some cases the correspondence problem can be transformed into an assignment problem which can be solved e.g. by the Kuhn-Munkres algorithm (KMA) by which a minimal cost matching can be found. We use the KMA for solving classic assignment problems, i.e. the pairwise correspondence between two sets of IMFs of equal cardinalities, and for pairwise correspondences between two sets of IMFs with different cardinalities representing an unbalanced assignment problem which is a special case of the k-cardinality assignment problem.

Results: A KMA-based approach to solve the correspondence problem was tested by using simulated, heart rate variability (HRV), and EEG data. The KMA-based results of HRV decomposition are compared with those obtained from a hierarchical cluster analysis (state-of-the-art). The major difference between the two approaches is that there is a more consistent assignment pattern using KMA. Integrating KMA into complex analysis concepts enables a comprehensive exploitation of the key advantages of the EMD. This can be demonstrated by non-linear analysis of HRV-related IMFs and by an EMD-based cross-frequency coupling analysis of the EEG data.

Conclusions: The successful application to HRV and EEG analysis demonstrates that our solutions can be used for automated EMD-based processing concepts for biomedical signals.

Signal Informatics as an Advanced Integrative Concept in the Framework of Medical Informatics

Objectives: The main objective is to show current topics and future trends in the field of medical signal processing which are derived from current research concepts. Signal processing as an integrative concept within the scope of medical informatics is demonstrated.

Methods: For all examples time-variant multivariate autoregressive models were used. Based on this modeling, the concept of Granger causality in terms of the time-variant Granger causality index and the time-variant partial directed coherence was realized to investigate directed information transfer between different brain regions.

Results: Signal informatics encompasses several diverse domains including: processing steps, methodologies, levels and subject fields, and applications. Five trends can be recognized and in order to illustrate these trends, three analysis strategies derived from current neuroscientific studies are presented. These examples comprise high-dimensional fMRI and EEG data. In the first example, the quantification of time-variant-directed information transfer between activated brain regions on the basis of fast-fMRI data is introduced and discussed. The second example deals with the investigation of differences in word processing between dyslexic and normal reading children. Different dynamic neural networks of the directed information transfer are identified on the basis of event-related potentials. The third example shows time-variant cortical connectivity networks derived from a source model.

Conclusions: These examples strongly emphasize the integrative nature of signal informatics, encompassing processing steps, methodologies, levels and subject fields, and applications.

Primary somatosensory contextual modulation is encoded by oscillation frequency change

OBJECTIVE

This study characterized thalamo-cortical communication by assessing the effect of context-dependent modulation on the very early somatosensory evoked high-frequency oscillations (HF oscillations).

METHODS

We applied electrical stimuli to the median nerve together with an auditory oddball paradigm, presenting standard and deviant target tones representing differential cognitive contexts to the constantly repeated electrical stimulation. Median nerve stimulation without auditory stimulation served as unimodal control.

RESULTS

A model consisting of one subcortical (near thalamus) and two cortical (Brodmann areas 1 and 3b) dipolar sources explained the measured HF oscillations. Both at subcortical and the cortical levels HF oscillations were significantly smaller during bimodal (somatosensory plus auditory) than unimodal (somatosensory only) stimulation. A delay differential equation model was developed to investigate interactions within the 3-node thalamo-cortical network. Importantly, a significant change in the eigenfrequency of Brodmann area 3b was related to the context-dependent modulation, while there was no change in the network coupling.

CONCLUSION

This model strongly suggests cortico-thalamic feedback from both cortical Brodmann areas 1 and 3b to the thalamus. With the 3-node network model, thalamo-cortical feedback could be described.

SIGNIFICANCE

Frequency encoding plays an important role in contextual modulation in the somatosensory thalamo-cortical network.

Time-variant partial directed coherence for analysing connectivity: a methodological study

For the past decade, the detection and quantification of interactions within and between physiological networks has become a priority-in-common between the fields of biomedicine and computer science. Prominent examples are the interaction analysis of brain networks and of the cardiovascular-respiratory system. The aim of the study is to show how and to what extent results from time-variant partial directed coherence analysis are influenced by some basic estimator and data parameters. The impacts of the Kalman filter settings, the order of the autoregressive (AR) model, signal-to-noise ratios, filter procedures and volume conduction were investigated. These systematic investigations are based on data derived from simulated connectivity networks and were performed using a Kalman filter approach for the estimation of the time-variant multivariate AR model. Additionally, the influence of electrooculogram artefact rejection on the significance and dynamics of interactions in 29 channel electroencephalography recordings, derived from a photic driving experiment, is demonstrated. For artefact rejection, independent component analysis was used. The study provides rules to correctly apply particular methods that will aid users to achieve more reliable interpretations of the results.

Towards the time varying estimation of complex brain connectivity networks by means of a General Linear Kalman Filter approach

One of the main limitations of the brain functional connectivity estimation methods based on Autoregressive Modeling, like the Granger Causality family of estimators, is the hypothesis that only stationary signals can be included in the estimation process. This hypothesis precludes the analysis of transients which often contain important information about the neural processes of interest. On the other hand, previous techniques developed for overcoming this limitation are affected by problems linked to the dimension of the multivariate autoregressive model (MVAR), which prevents from analysing complex networks like those at the basis of most cognitive functions in the brain. The General Linear Kalman Filter (GLKF) approach to the estimation of adaptive MVARs was recently introduced to deal with a high number of time series (up to 60) in a full multivariate analysis. In this work we evaluated the performances of this new method in terms of estimation quality and adaptation speed, by means of a simulation study in which specific factors of interest were systematically varied in the signal generation to investigate their effect on the method performances. The method was then applied to high density EEG data related to an imaginative task. The results confirmed the possibility to use this approach to study complex connectivity networks in a full multivariate and adaptive fashion, thus opening the way to an effective estimation of complex brain connectivity networks.

Connectivity analysis of somatosensory evoked potentials in patients with major depression. Analysis of connectivity

OBJECTIVES

Connectivity analysis was used to investigate the processing of intracutaneous stimuli and directed interactions within the pain matrix in patients with major depression (MD) and healthy controls (HCs), by means of frequency selective generalized partial directed coherence (gPDC).

METHODS

Eighteen patients with MD and 18 HCs underwent stimulations consisting of moderately painful intracutaneous electrical stimuli to the right and left middle fingers. Connectivity analysis was based on nine selected EEG electrodes.

RESULTS

Stimulus-induced changes of the gPDC in a pre/post stimulus comparison and changes in the connectivity pattern in the post-stimulus condition were found. We could identify network changes correlating to the side stimulated, as well as differences between HCs and MD patients.

CONCLUSIONS

These data support the suggestion that pain processing in response to noxious stimulation in MD patients is different compared to healthy controls, suggesting aberrant functional connectivity. Generalized partial directed coherence is shown to be a promising method to detect changes in connectivity in both within- and between-subject designs.

Signal informatics as an advanced integrative concept in the framework of medical informatics. New trends demonstrated by examples derived from neuroscience

OBJECTIVES

The main objective is to show current topics and future trends in the field of medical signal processing which are derived from current research concepts. Signal processing as an integrative concept within the scope of medical informatics is demonstrated.

METHODS

For all examples time-variant multivariate autoregressive models were used. Based on this modeling, the concept of Granger causality in terms of the time-variant Granger causality index and the time-variant partial directed coherence was realized to investigate directed information transfer between different brain regions.

RESULTS

Signal informatics encompasses several diverse domains including: processing steps, methodologies, levels and subject fields, and applications. Five trends can be recognized and in order to illustrate these trends, three analysis strategies derived from current neuroscientific studies are presented. These examples comprise high-dimensional fMRI and EEG data. In the first example, the quantification of time-variant-directed information transfer between activated brain regions on the basis of fast-fMRI data is introduced and discussed. The second example deals with the investigation of differences in word processing between dyslexic and normal reading children. Different dynamic neural networks of the directed information transfer are identified on the basis of event-related potentials. The third example shows time-variant cortical connectivity networks derived from a source model.

CONCLUSIONS

These examples strongly emphasize the integrative nature of signal informatics, encompassing processing steps, methodologies, levels and subject fields, and applications.

Model-related analysis of EEG burst patterns in sedated patients

A model-related analysis approach was introduced to study amplitude-frequency dependencies within and between EEG frequency components. An oscillator network was used to model EEG burst patterns of sedated patients during encephalographic burst-suppression periods (BSP). The parameter set of the oscillator network was determined for a set of bursts during BSP. In this way, these burst-related parameter sets were used to investigate (i) the dynamics of interrelation of the amplitude and frequency within and between the frequency components during the occurrence of burst patterns and (ii) changes of signal properties (burst-by-burst) during the BSP. Representative results are demonstrated for one patient (group of 7 patients).

Time-variant analysis of fast-fMRI and dynamic contrast agent MRI sequences as examples of 4-dimensional image analysis

OBJECTIVES

Image sequences with time-varying information content need appropriate analysis strategies. The exploration of directed information transfer (interactions) between neuronal assemblies is one of the most important aims of current functional MRI (fMRI) analysis. Additionally, we examined perfusion maps in dynamic contrast agent MRI sequences of stroke patients. In this investigation, the focus centers on distinguishing between brain areas with normal and reduced perfusion on the basis of the dynamics of contrast agent inflow and washout.

METHODS

Fast fMRI sequences were analyzed with time-variant Granger causality (tvGC). The tvGC is based on a time-variant autoregressive model and is used for the quantification of the directed information transfer between activated brain areas. Generalized Dynamic Neural Networks (GDNN) with time-variant weights were applied on dynamic contrast agent MRI sequences as a nonlinear operator in order to enhance differences in the signal courses of pixels of normal and injured tissues.

RESULTS

A simple motor task (self-paced finger tapping) is used in an fMRI design to investigate directed interactions between defined brain areas. A significant information transfer can be determined for the direction primary motor cortex to supplementary motor area during a short time period of about five seconds after stimulus. The analysis of dynamic contrast agent MRI sequences demonstrates that the trained GDNN enables a reliable tissue classification. Three classes are of interest: normal tissue, tissue at risk for death, and dead tissue.

CONCLUSIONS

The time-variant multivariate analysis of directed information transfer derived from fMRI sequences and the computation of perfusion maps by GDNN demonstrate that dynamic analysis methods are essential tools for 4D image analysis.

Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity

Directed information transfer in the human brain occurs presumably by oscillations. As of yet, most approaches for the analysis of these oscillations are based on time-frequency or coherence analysis. The present work concerns the modeling of cortical 600 Hz oscillations, localized within the Brodmann Areas 3b and 1 after stimulation of the nervus medianus, by means of coupled differential equations. This approach leads to the so-called parameter identification problem, where based on a given data set, a set of unknown parameters of a system of ordinary differential equations is determined by special optimization procedures. Some suitable algorithms for this task are presented in this paper. Finally an oscillatory network model is optimally fitted to the data taken from ten volunteers.

Fundusspektrometrie bei altersbezogener Makulopathie

BACKGROUND

Spectroscopic methods permit the non-invasive detection of fundus pigments by the wavelength-dependent absorption of fluorescence as well as by the fluorescence lifetime. From the relative concentrations of haemoglobin and oxyhaemoglobin, the oxygen saturation can be calculated. The onset of age-related maculopathy might be delayed by a high optical density of xanthophyll. The detection of alterations in fundus autofluorescence points to age-related pathomechanisms (accumulation of lipofuscin, formation of connective tissue). The detection of autofluorescence of redox-pairs of coenzymes results in information about metabolic states at the cellular level, and might make possible an early detection of age-related changes when they are still reversible.

METHOD

The evaluation of reflectance spectra, detected by imaging ophthalmo-spectrometry, results in the calculation of oxygen saturation or in the optical density of xanthophyll or of melanin. Fluorescence spectra can be measured also by this technique. For the 2-dimensional determination of the distribution of xanthophyll, a very simple method was developed, requiring fundus illumination by one wavelength only. In the detection of time-resolved autofluorescence, the fluorescence lifetime is used for the determination of endogenous fluorophores.

RESULTS

As result of comparing studies between ARM patients and healthy subjects, the consumption of retinal oxygen was increased already in the children of ARM patients. An increasing optical density of xanthophyll was determined after lutein supplementation. Differences in fluorescence lifetime were determined between ARM patients and healthy subjects, but their interpretation requires investigations of cell or of organ model cultures.

CONCLUSIONS

The described methods permit in vivo basic investigations of ARM and can be considered as impulses for the development of diagnostic devices.

[Determination of the concentration distribution of macular pigment from reflection and fluorescence images]

OBJECTIVE

The macular pigment xanthophyll protects the macula in two ways: firstly, it absorbs hazardous blue light and secondly, it acts as a radical scavenger. A low concentration of xanthophyll may be regarded as a risk factor for age-related macular degeneration (AMD). Therefore, we investigated a simple method to determine the xanthophyll concentration at the fundus which is suitable for patient screening.

METHOD

The local distribution of xanthophyll density was determined from monochromatic blue reflection images and autofluorescence images of the fundus in 18 healthy volunteers (mean age: 23.9 years). The significance of the parameters maximal, global, and mean concentration were compared.

RESULTS

The maximal optical density of xanthophyll determined from reflection images was found to be 0.29+/-0.08 (mean for all test persons) which is in good agreement with literature data. The total xanthophyll concentration which is proportional to the maximal density, appeared to be appropriate to describe a person's overall xanthophyll status. Because of the low intensity of autofluorescence images, these are less useful for the determination of the xanthophyll concentration.

CONCLUSIONS

Because of it's simplicity, the determination of xanthophyll concentration as described here can be performed by every ophthalmologist using a fundus camera and is, therefore, suitable as a screening method.

Prediction of movement following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia by means of middle latency auditory evoked responses

This paper investigates the applicability of generalized dynamic neural networks for the design of a two-valued anesthetic depth indicator during isoflurane/nitrous oxide anesthesia. The indicator construction is based on the processing of middle latency auditory evoked responses (MLAER) in combination with the observation of the patient's movement reaction to skin incision. The framework of generalized dynamic neural networks does not require any data preprocessing, visual data inspection or subjective feature extraction. The study is based on a data set of 106 patients scheduled for elective surgery under isoflurane/nitrous oxide anesthesia. The processing of the measured MLAER is performed by a recurrent neural network that transforms the MLAER signals into signals having a very uncomplex structure. The evaluation of these signals is self-evident, and yields to a simple threshold classifier. Using only evoked potentials before the pain stimulus, the patient's reaction could be predicted with a probability of 81.5%. The MLAER is closely associated to the patient's reaction to skin incision following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia. In combination with other parameters, MLAER could contribute to an objective and trustworthy movement prediction to noxious stimulation.

Light paths in retinal vessel oxymetry

The oxygen utilization and, therefore, the metabolic state, of a distinctive area of the retina may be calculated from the diameter of the supplying artery and vein, the haemoglobin oxygenation, and the velocity of the blood. The first two parameters can be determined by imaging spectrometry at the patients ocular fundus. However, the reflected light emerging from a vessel followed different pathways through the ocular fundus layers and the vessel embedded in the retina. The contribution of the single pathways to the vessel reflection profile is investigated by a Monte Carlo simulation. Considering retinal vessels with diameters of 25-200 microm we found the reflection from a thin vessel to be determined by the single and double transmission of light at 560 nm. The backscattering from the blood column determines the reflectance in the case of a thick vessel. However, both components are in the same order of magnitude. This has to be considered in the calculation of the oxygen saturation of blood in retinal vessels from their reflection spectra.

Identification of the stimulated hemiretina in primary school children and adults based on left and right hemifield pattern reversal visual evoked potentials--a comparative study

OBJECTIVES

The analysis of left and right hemifield pattern-reversal visual evoked potentials (PVEPs) in children and the identification of the stimulated hemiretina testing different identification procedures previously applied to adults.

METHODS

Lateral hemifield PVEPs were recorded in 40 children (6-11 years) and 27 adults (25-40 years) from, at least, 19 standard electrodes. Two procedures were tested for the determination of the stimulated hemifield: firstly, the evaluation of the values of instantaneous frequency at the occipital electrodes at P100 latency (determined by the global field power), and secondly, the application of a generalised dynamic neural network (GDNN) using the PVEP time course at selected electrode positions as the external input.

RESULTS

P100 latency as well as P100 amplitude over the contralateral occiput in children were significantly greater than in adults. Contrary to the behaviour in adults, instantaneous frequency is not a robust identifier of left and right hemiretina stimulation in children. The best identification performances were achieved when using group trained GDNNs with the bipolar difference signals of electrodes P3/P4 or T5/T6 as the external input.

CONCLUSIONS

The PVEPs at electrodes P3/P4 and T5/T6 contain essential information for the determination of the stimulated hemifield. This should be further considered during the development of on-line procedures for automatic PVEP detection in future studies.

Identification of hemifield single trial PVEP on the basis of generalized dynamic neural network classifiers

This paper is concerned with the application of generalized dynamic neural networks for the identification of hemifield pattern-reversal visual evoked potentials. The identification process is performed by different networks with time-varying weights using signals from different electrode positions as external inputs. Since dynamic neural networks are able to process time-varying signals, the identification of the stimulated hemiretinae is performed without feature extraction. The performance of the method presented is compared with a reference method based on the values of instantaneous frequency at the occipital electrode positions at P100 latency.

New approaches for the detection and analysis of electroencephalographic burst-suppression patterns in patients under sedation

An automatic EEG pattern detection unit was developed and tested for the recognition of burst-suppression periods and for the separation of burst from suppression patterns. The median, standard deviation and the 95% edge frequency were computed from single channels of the EEG within a moving window and completed by the continuous computation of frequency band power via an adapted Hilbert resonance filter. These parameters were given to the inputs of two hierarchically arranged artificial neural networks (NNs). The output signals of NNs indicate the suppression and burst phases. The burst recognition was focused on the precise recognition of the burst onset. In subsequent processing steps the time course of percentages of burst patterns within their corresponding burst-suppression-phases was calculated and the time locations of burst onsets can be used to trigger an averaging for a burst-related analysis. The data for our investigations were derived from the routine EEG derivations of 12 patients with various neurosurgical diseases. A group-related training of the NNs was realized. For the group-related trained NNs EEG data for 6 patients were used for training and the data of 6 other patients for testing the classification performance of the pattern recognition units. Additionally, the reliability of the detection algorithm was tested with data of two patients with convulsive state, resistant to treatment, and burst-suppression like pattern EEC.

Multimodal time-variant signal analysis of neonatal EEG burst patterns

It can be shown that dominant rhythmic signal components of neonatal EEG burst patterns (discontinuous EEG in quiet sleep) are characterized by a quadratic phase coupling (bispectral analysis), i.e. a multiplicative interaction (connection) between the underlying electrophysiological processes can be assumed. By means of pattern recognition algorithms as well as time-variant spectral and coherence analysis, a so-called "initial wave" (narrow band rhythm within a frequency range of 3-12 Hz) can be demonstrated within the first part of the burst pattern. The detection of this signal component and of the quadratic phase coupling is more successful in the frontal region. By means of amplitude demodulation of the "initial wave" the phase coupling can be attributed to an amplitude modulation. The results were derived from 6 neonates (20 burst patterns for each neonate; 8-channel recordings). A 16-channel EEG-recording was analyzed for one neonate.

Interrelations between EEG frequency components in sedated intensive care patients during burst-suppression period

The EEG during basic sedation and burst patterns during electroencephalic burst-suppression patterns (BSP) were analyzed. The aim of EEG analysis was the characterization and quantification of the interrelations between distinct frequency components in both states of sedation. The data for the investigations were derived from the routine EEG derivations of 12 patients with various neurosurgical diseases. It can be demonstrated that the degree of interrelation (amplitude modulation) between a low-frequency component (0-2.5 Hz) and oscillations with higher frequency (3-7.5 and 8-12 Hz) is increased in burst patterns during BSP compared with the EEG during basic sedation. It can be concluded that the degree of interrelations depends on the sedation depth induced by hypnotic drugs.

[An experimental study on the use of the Hilbert operator as a method of signal preprocessing for the determination of oxygen saturation in retinal vessels]

For the non-invasive measurement of the oxygen saturation in human retinal vessels, the light reflected by a vessel and its surroundings is evaluated. Differences in the absorption and scattering properties of the optical media provide so-called vessel profiles, but the central vessel section is often disturbed by a regular reflex. In order to eliminate this reflex, a method based on the Hilbert transform is presented, which can be used for the determination of logarithmic differences between the reflected light on and that beside the vessel. The data for our investigations were produced by simulation of the radiation transport in multi-layered tissue. A linear regression between expected and measured values based on 40 pairs was used for the evaluation of the proposed method. A linear relationship was shown to exist.