Hubert Preissl

Hand Movement Direction Decoded from MEG and EEG

Brain activity can be used as a control signal for brain–machine interfaces (BMIs). A powerful and widely acknowledged BMI approach, so far only applied in invasive recording techniques, uses neuronal signals related to limb movements for equivalent, multidimensional control of an external effector. Here, we investigated whether this approach is also applicable for noninvasive recording techniques. To this end, we recorded whole-head MEG during center-out movements with the hand and found significant power modulation of MEG activity between rest and movement in three frequency bands: an increase for ≤7 Hz (low-frequency band) and 62–87 Hz (high-γ band) and a decrease for 10–30 Hz (β band) during movement. Movement directions could be inferred on a single-trial basis from the low-pass filtered MEG activity as well as from power modulations in the low-frequency band, but not from the β and high-γ bands. Using sensors above the motor area, we obtained a surprisingly high decoding accuracy of 67% on average across subjects. Decoding accuracy started to rise significantly above chance level before movement onset. Based on simultaneous MEG and EEG recordings, we show that the inference of movement direction works equally well for both recording techniques. In summary, our results show that neuronal activity associated with different movements of the same effector can be distinguished by means of noninvasive recordings and might, thus, be used to drive a noninvasive BMI.

Functional development of the visual system in human fetus using magnetoencephalography.

The development of the human brain in utero is normally regarded as a dynamic process involving mainly structural and quantitative changes in neurons and their distribution. However, it is generally accepted that a parallel development of functional specialization occurs in certain areas of the brain, especially in the primary cortex. Nearly all knowledge of functional fetal brain development has been obtained from various animal studies rather than human studies. These studies show that the primary sensory areas like auditory, visual, and somatosensory cortex show a basic function similar to that of a fully developed brain. It has been specifically shown that the visual system develops during fetal life and becomes functional before birth. Several studies have demonstrated the feasibility of using visual evoked response (VER) recordings on preterm human infants to follow the functional development of the visual system. With the advent of the noninvasive technique of magnetoencephalography (MEG), human fetal VER recordings are now possible thus providing the opportunity to track its functional development with gestation. We present and discuss the results of VER recordings in human fetuses starting at 28 weeks of gestation performed using a 151-channel MEG system.

The Insulin Effect on Cerebrocortical Theta Activity Is Associated with Serum Concentrations of Saturated Nonesterified Fatty Acids

CONTEXTnInsulin action in the brain contributes to adequate regulation of body weight, neuronal survival, and suppression of endogenous glucose production. We previously demonstrated by magnetoencephalography in lean humans that insulin stimulates activity in beta and theta frequency bands, whereas this effect was abolished in obese individuals.nnnOBJECTIVEnThe present study aims to define metabolic signals associated with the suppression of the cerebrocortical response in obese humans.nnnDESIGN AND SETTINGnWe determined insulin-mediated modulation of spontaneous cerebrocortical activity by magnetoencephalography during a hyperinsulinemic euglycemic clamp and related it to measures of ectopic fat deposition and mediators of peripheral insulin resistance. Visceral fat mass and intrahepatic lipid content were quantified by magnetic resonance imaging and spectroscopy. Multiple regression analysis was used to analyze associations of cerebrocortical insulin sensitivity and metabolic markers related to obesity.nnnPARTICIPANTSnForty-nine healthy, nondiabetic humans participated in the study.nnnRESULTSnIn a multiple regression, insulin-mediated stimulation of theta activity was negatively correlated to body mass index, visceral fat mass, and intrahepatic lipid content. Although fasting saturated nonesterified fatty acids mediated the correlations of theta activity with abdominal and intrahepatic lipid stores, adipocytokines displayed no independent correlation with insulin-mediated cortical activity in the theta frequency band.nnnCONCLUSIONSnThus, insulin action at the level of cerebrocortical activity in the brain is diminished in the presence of elevated levels of saturated nonesterified fatty acids.

Neuronal correlates of reduced memory performance in overweight subjects.

There is growing evidence that excessive body weight correlates with impaired cognitive performance like executive function, attention and memory. In our study, we applied a visual working memory task to quantify associations between body weight and executive function. In total, 34 lean (BMI 22±2.1 kg/m(2)) and 34 obese (BMI 30.4±3.2 kg/m(2)) subjects were included. Magnetic brain activity and behavioral responses were recorded during a one-back visual memory task with food and non-food pictures, which were matched for color, size and complexity. Behavioral responses (reaction time and accuracy) were reduced in obese subjects independent of the stimulus category. Neuronal activity at the source level showed a positive correlation between the right dorsolateral prefrontal cortex (DLPFC) activity and BMI only for the food category. In addition, a negative correlation between BMI and neuronal activity was observed in the occipital area for both categories. Therefore we conclude that increased body weight is associated with reduced task performance and specific neuronal changes. This altered activity is probably related to executive function as well as encoding and retrieval of information.

Adaptive rule based fetal QRS complex detection using hilbert transform

In this paper we introduce an adaptive rule based QRS detection algorithm using the Hilbert transform (adHQRS) for fetal magnetocardiography processing. Hilbert transform is used to combine multiple channel measurements and the adaptive rule based decision process is used to eliminate spurious beats. The algorithm has been tested with a large number of datasets and promising results were obtained.

Detection of Uterine MMG Contractions Using a Multiple Change Point Estimator and the K-Means Cluster Algorithm

We propose a single channel two-stage time-segment discriminator of uterine magnetomyogram (MMG) contractions during pregnancy. We assume that the preprocessed signals are piecewise stationary having distribution in a common family with a fixed number of parameters. Therefore, at the first stage, we propose a model-based segmentation procedure, which detects multiple change-points in the parameters of a piecewise constant time-varying autoregressive model using a robust formulation of the Schwarz information criterion (SIC) and a binary search approach. In particular, we propose a test statistic that depends on the SIC, derive its asymptotic distribution, and obtain closed-form optimal detection thresholds in the sense of the Neyman-Pearson criterion; therefore, we control the probability of false alarm and maximize the probability of change-point detection in each stage of the binary search algorithm. We compute and evaluate the relative energy variation [root mean squares (RMS)] and the dominant frequency component [first order zero crossing (FOZC)] in discriminating between time segments with and without contractions. The former consistently detects a time segment with contractions. Thus, at the second stage, we apply a nonsupervised K-means cluster algorithm to classify the detected time segments using the RMS values. We apply our detection algorithm to real MMG records obtained from ten patients admitted to the hospital for contractions with gestational ages between 31 and 40 weeks. We evaluate the performance of our detection algorithm in computing the detection and false alarm rate, respectively, using as a reference the patients feedback. We also analyze the fusion of the decision signals from all the sensors as in the parallel distributed detection approach.

Habituation of visual evoked responses in neonates and fetuses: A MEG study

In this study we aimed to develop a habituation paradigm that allows the investigation of response decrement and response recovery and examine its applicability for measuring the habituation of the visually evoked responses (VERs) in neonatal and fetal magnetoencephalographic recordings. Two paradigms, one with a long and one with a short inter-train interval (ITI), were developed and tested in separate studies. Both paradigms consisted of a train of four light flashes; each train being followed by a 500Hz burst tone. Healthy pregnant women underwent two prenatal measurements and returned with their babies for a neonatal investigation. The amplitudes of the neonatal VERs in the long-ITI condition showed within-train response decrement. An increased response to the auditory dishabituator was found confirming response recovery. In the short-ITI condition, neonatal amplitude decrement could not be demonstrated while response recovery was present. In both ITI conditions, the response rate of the cortical responses was much lower in the fetuses than in the neonates. Fetal VERs in the long-ITI condition indicate amplitude decline from the first to the second flash with no further decrease. The long-ITI paradigm might be useful to investigate habituation of the VERs in neonates and fetuses, although the latter requires precaution.

Application of wavelet transform to uterine electromyographic signals recorded using abdominal surface electrodes.

Objective: The aim of this study was to explore the use of the wavelet transform technique to extract and display simultaneously the time, frequency and amplitude information corresponding to electromyographic (EMG) activity of the uterus during labor recorded using abdominal electrodes. Methods: Uterine EMG signals were recorded from patients in labor using three pairs of electrodes placed across the maternal abdomen. In all the patients uterine activity was also recorded either from an intrauterine pressure catheter (IUPC) or a tocodynamometer. The EMG signals were analyzed using spectral analysis and wavelet analysis. Results: Uterine EMG bursts corresponded with uterine activity measured with either the IUPC or the tocodynamometer. Using wavelet analysis a time-frequency-amplitude plot was obtained to separate out the frequency components relating to uterine EMG activity. Conclusion: This study showed that the wavelet transform could be a useful tool to study the uterine EMG activity. Continued studies on frequency content, amplitude and origin of uterine EMG activity could be helpful in understanding uterine contraction.

Design of a light stimulator for fetal and neonatal magnetoencephalography.

The design, safety analysis and performance of a fetal visual stimulation system suitable for fetal and neonatal magnetoencephalography studies are presented. The issue of fetal, neonatal and maternal safety is considered and the maximum permissible exposure is computed for the maternal skin and the adult eye. The risk for neonatal eye exposure is examined. It is demonstrated that the fetus, neonate and mother are not at risk.

Detection of Discontinuous Patterns in Spontaneous Brain Activity of Neonates and Fetuses

The discontinuous patterns in neonatal magnetoencephalographic (MEG) data are quantified with a novel Hilbert phase (HP) based approach. The expert neurologists scores were used as the gold standard. The performance of this approach was analyzed using a receiver operating characteristic (ROC) curve, and it was compared with two other approaches, namely spectral ratio (SR) and discrete wavelet transform (DWT) that have been proposed for the detection of discontinuous patterns in neonatal EEG. The area under the ROC curve (AUC) was used as a performance measure. AUCs obtained for SR, HP, and DWT were 0.87, 0.80, and 0.56, respectively. Although the performance of HP was lower than SR, it carries information about the frequency content of the signal that helps to distinguish brain patterns from artifacts such as cardiac residuals. Based on this property, the HP approach was extended to fetal MEG data. Further, using the frequency property of the HP approach, burst duration and interburst interval were computed for the discontinuous patterns detected and they are in agreement with reported values.