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News Article | April 27, 2017
Site: www.eurekalert.org

University of Bonn researchers are exploring 'evolving epileptic brain networks' to gain a better understanding of brain activity in epilepsy patients and the roles played by different regions of the brain WASHINGTON, D.C., April, 27, 2017 -- Epilepsy is a complex neurological disorder that afflicts approximately 50 million people worldwide. Although this disease has been known to exist for centuries, the exact mechanism of its cardinal symptom, the epileptic seizure, remains poorly understood. In fact, roughly 25 percent of epileptic seizures can't be controlled by any of the therapies available today. Recent advances have led to a conceptualization of epilepsy as a "network disease" exhibiting connections within the brain. This large-scale epileptic network comprises various areas of the brain involved in normal brain activity during both seizure-free intervals and those involved in so-called pathophysiological activities such as seizures. Little is known, however, about which specific areas of the brain contribute to a patient's epileptic network or the roles these different areas play. As a group of researchers in Germany now reports this week in Chaos, from AIP Publishing, one way to get closer to the complex wiring of the human brain is by merging concepts from a timed-based synchronization theory and space-based network theory to construct functional brain networks. Until now, the "seizure-generating area" of the brain -- in which the earliest signs of seizure activity can be observed -- was considered the most important of these regions. This finding was based on very limited data and it was unclear whether its importance changes with time. With this new analytical approach, Professor Klaus Lehnertz, head of the Neurophysics Group in the Department of Epileptology at the University of Bonn, and his group explored the temporal and spatial variability of the importance of the brain's different regions. "New developments in network theory are providing powerful tools to construct so-called 'functional networks' from observations of brain activities such as the electroencephalogram (EEG), and helping to identify the important nodes and links within such networks," Lehnertz said. By associating network nodes with individually sampled brain regions, Lehnertz's group can define a link between a pair of nodes by assessing the degree of synchrony between neuronal signals from all pairs of nodes; the higher the degree, the stronger the link. "Applying these analysis concepts to multichannel long-term EEG recordings from 17 epilepsy patients with high temporal resolution allowed us to derive a sequence of functional brain networks spanning several days in duration," said Christian Geier, a doctoral student working with Lehnertz. "For each network, we assess various aspects of the importance of individual brain regions with different centrality indices that were developed earlier for the social sciences. Then, we explore how the importance of network nodes fluctuates over time." The group's work is particularly significant because they showed for the first time how the importance of individual nodes within functional brain networks fluctuates on timescales spanning tens of seconds up to days. They further showed that these fluctuations can be largely attributed to the normal, daily rhythms of a patient, yet only minimally attributed to phenomena directly related to the disease. Perhaps their most intriguing finding is that in general, according to Geier, there isn't a constant importance hierarchy between brain regions. "Rather, they take turns in importance on various time scales," Geier said. "And, depending on which aspect of importance is assessed, the seizure-generating area isn't -- as commonly believed -- the most important node within a large-scale epileptic network." The understandings gained from this research are part of the necessary foundation for developing treatments related to the causes and symptoms of epilepsy. "When different brain regions assume the highest importance within a functional brain network is the key to improving both prediction and control of epileptic seizures," Lehnertz said. "In the long run, this improved understanding may enable the development of better treatment options for patients suffering from epilepsy. And understanding the importance of the nodes and links of functional brain networks may also be relevant for other neurological diseases." The article, "Long-term viability of importance of brain regions in evolving epileptic brain networks," is authored by Christian Geier and Klaus Lehnertz. The article appeared in Chaos April 25, 2017 (DOI: 10.1063/1.4979796) and can be accessed at http://aip. . Chaos is devoted to increasing the understanding of nonlinear phenomena in all disciplines and describing their manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines. See http://chaos. .


Recent advances have led to a conceptualization of epilepsy as a "network disease" exhibiting connections within the brain. This large-scale epileptic network comprises various areas of the brain involved in normal brain activity during both seizure-free intervals and those involved in so-called pathophysiological activities such as seizures. Little is known, however, about which specific areas of the brain contribute to a patient's epileptic network or the roles these different areas play. As a group of researchers in Germany now reports this week in Chaos, one way to get closer to the complex wiring of the human brain is by merging concepts from a timed-based synchronization theory and space-based network theory to construct functional brain networks. Until now, the "seizure-generating area" of the brain—in which the earliest signs of seizure activity can be observed—was considered the most important of these regions. This finding was based on very limited data and it was unclear whether its importance changes with time. With this new analytical approach, Professor Klaus Lehnertz, head of the Neurophysics Group in the Department of Epileptology at the University of Bonn, and his group explored the temporal and spatial variability of the importance of the brain's different regions. "New developments in network theory are providing powerful tools to construct so-called 'functional networks' from observations of brain activities such as the electroencephalogram (EEG), and helping to identify the important nodes and links within such networks," Lehnertz said. By associating network nodes with individually sampled brain regions, Lehnertz's group can define a link between a pair of nodes by assessing the degree of synchrony between neuronal signals from all pairs of nodes; the higher the degree, the stronger the link. "Applying these analysis concepts to multichannel long-term EEG recordings from 17 epilepsy patients with high temporal resolution allowed us to derive a sequence of functional brain networks spanning several days in duration," said Christian Geier, a doctoral student working with Lehnertz. "For each network, we assess various aspects of the importance of individual brain regions with different centrality indices that were developed earlier for the social sciences. Then, we explore how the importance of network nodes fluctuates over time." The group's work is particularly significant because they showed for the first time how the importance of individual nodes within functional brain networks fluctuates on timescales spanning tens of seconds up to days. They further showed that these fluctuations can be largely attributed to the normal, daily rhythms of a patient, yet only minimally attributed to phenomena directly related to the disease. Perhaps their most intriguing finding is that in general, according to Geier, there isn't a constant importance hierarchy between brain regions. "Rather, they take turns in importance on various time scales," Geier said. "And, depending on which aspect of importance is assessed, the seizure-generating area isn't—as commonly believed—the most important node within a large-scale epileptic network." The understandings gained from this research are part of the necessary foundation for developing treatments related to the causes and symptoms of epilepsy. "When different brain regions assume the highest importance within a functional brain network is the key to improving both prediction and control of epileptic seizures," Lehnertz said. "In the long run, this improved understanding may enable the development of better treatment options for patients suffering from epilepsy. And understanding the importance of the nodes and links of functional brain networks may also be relevant for other neurological diseases." More information: Christian Geier et al, Long-term variability of importance of brain regions in evolving epileptic brain networks, Chaos: An Interdisciplinary Journal of Nonlinear Science (2017). DOI: 10.1063/1.4979796


Acqualagna L.,TU Berlin | Bosse S.,Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut | Porbadnigk A.K.,TU Berlin | Curio G.,Neurophysics Group | And 5 more authors.
Journal of Neural Engineering | Year: 2015

Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment. Approach. As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively. Main results. Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images. Significance. The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component. © 2015 IOP Publishing Ltd.


Porbadnigk A.K.,TU Berlin | Treder M.S.,TU Berlin | Fazli S.,TU Berlin | Tangermann M.,TU Berlin | And 6 more authors.
2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 | Year: 2013

The last years have seen a rise in interest in using BCI methodology for investigating non-medical questions beyond the purpose of communication and control. This abstract first provides a short introduction to BCI challenges from a machine learning perspective. The remaining sections present selected applications of BCI discussing in particular the use of EEG in combination with BCI methods for investigating how signal quality is processed on a sensory and cognitive level. © 2013 IEEE.


Bosse S.,Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut | Acqualagna L.,TU Berlin | Porbadnigk A.K.,TU Berlin | Blankertz B.,TU Berlin | And 4 more authors.
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014

Conventionally, the quality of images and related codecs are assessed using subjective tests, such as Degradation Category Rating. These quality assessments consider the behavioral level only. Recently, it has been proposed to complement this approach by investigating how quality is processed in the brain of a user (using electroencephalography, EEG), potentially leading to results that are less biased by subjective factors. In this paper, a novel method is presented for assessing how image quality is processed on a neural level, using Steady-State Visual Evoked Potentials (SSVEPs) as EEG features. We tested our approach in an EEG study with 16 participants who were presented with distorted images of natural textures. Subsequently, we compared our approach analogously to the standardized Degradation Category Rating quality assessment. Remarkably, our novel method yields a correlation of r = 0.93 to MOS on the recorded dataset. © 2014 IEEE.


Scheer H.J.,Physikalisch - Technische Bundesanstalt | Fedele T.,Physikalisch - Technische Bundesanstalt | Fedele T.,Neurophysics Group | Curio G.,Neurophysics Group | Burghoff M.,Physikalisch - Technische Bundesanstalt
Physiological Measurement | Year: 2011

Ultrafast electroencephalographic signals, having frequencies above 500 Hz, can be observed in somatosensory evoked potential measurements. Usually, these recordings have a poor signal-to-noise ratio (SNR) because weak signals are overlaid by intrinsic noise of much higher amplitude like that generated by biological sources and the amplifier. As an example, recordings at the scalp taken during electrical stimulation of the median nerve show a 600 Hz burst with submicro-volt amplitudes which can be extracted from noise by the use of massive averaging and digital signal processing only. We have investigated this signal by means of a very low noise amplifier made in-house (minimal voltage noise 2.7 nV Hz -1/2, FET inputs). We examined how the SNR of the data is altered by the bandwidth and the use of amplifiers with different intrinsic amplifier noise levels of 12 and 4.8 nV Hz -1/2, respectively. By analyzing different frequency contributions of the signal, we found an extremely weak 1 kHz component superimposed onto the well-known 600 Hz burst. Previously such high-frequency electroencephalogram responses around 1 kHz have only been observed by deep brain electrodes implanted for tremor therapy of Parkinson patients. For the non-invasive measurement of such signals, we recommend that amplifier noise should not exceed 4 nV Hz -1/2. © 2011 Institute of Physics and Engineering in Medicine.


Bosse S.,Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut | Acqualagna L.,TU Berlin | Porbadnigk A.K.,TU Berlin | Curio G.,Neurophysics Group | And 4 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

An approach to the neural measurement of perceived image quality using electroencephalography (EEG) is presented. 6 different images were tested on 6 different distortion levels. The distortions were introduced by a hybrid video encoder. The presented study consists of two parts: In a first part, subjects were asked to evaluate the quality of the test stimuli behaviorally during a conventional psychophysical test using a degradation category rating procedure. In a second part, subjects were presented undistorted and distorted texture images in a periodically alternating fashion at a fixed frequency. This alternating presentation elicits so called steady-state visual evoked potentials (SSVEP) as a brain response that can be measured on the scalp. The amplitude of modulations in the brain signals is significantly and strongly negatively correlated with the magnitude of visual impairment reported by the subjects. This neurophysiological approach to image quality assessment may potentially lead to a more objective evaluation, as behavioral approaches suffer from drawbacks such as biases, inter-subject variances and limitations to test duration. © 2015 SPIE.

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