Christian Rummel

Publications (32) View all

• Source

  Available from: Veronika Schöpf

  Article: Time course based artifact identification for independent components of resting state fMRI

  C. Rummel, R.K. Verma, V. Schöpf, M. Hauf, E. Abela, J Zapata Berruecos, R. Wiest
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  ABSTRACT: In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for restingstate fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group restingstate studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.
  Frontiers in Human Neuroscience 05/2013; 7(214). · 2.34 Impact Factor
• Article: Widespread grey matter changes and hemodynamic correlates to interictal epileptiform discharges in pharmacoresistant mesial temporal epilepsy.

  Roland Wiest, Lea Estermann, Olivier Scheidegger, Christian Rummel, Kay Jann, Margitta Seeck, Kaspar Schindler, Martinus Hauf
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  ABSTRACT: Focal onset epilepsies most often occur in the temporal lobes. To improve diagnosis and therapy of patients suffering from pharmacoresistant temporal lobe epilepsy it is highly important to better understand the underlying functional and structural networks. In mesial temporal lobe epilepsy (MTLE) widespread functional networks are involved in seizure generation and propagation. In this study we have analyzed the spatial distribution of hemodynamic correlates (HC) to interictal epileptiform discharges on simultaneous EEG/fMRI recordings and relative grey matter volume (rGMV) reductions in 10 patients with MTLE. HC occurred beyond the seizure onset zone in the hippocampus, in the ipsilateral insular/operculum, temporo-polar and lateral neocortex, cerebellum, along the central sulcus and bilaterally in the cingulate gyrus. rGMV reductions were detected in the middle temporal gyrus, inferior temporal gyrus and uncus to the hippocampus, the insula, the posterior cingulate and the anterior lobe of the cerebellum. Overlaps between HC and decreased rGMV were detected along the mesolimbic network ipsilateral to the seizure onset zone. We conclude that interictal epileptic activity in MTLE induces widespread metabolic changes in functional networks involved in MTLE seizure activity. These functional networks are spatially overlapping with areas that show a reduction in relative grey matter volumes.
  Journal of Neurology 01/2013; · 3.47 Impact Factor
• Article: Chronic Pelvic Pain Syndrome in Men is Associated with Reduction of Relative Gray Matter Volume in the Anterior Cingulate Cortex Compared to Healthy Controls.

  Livio Mordasini, Christian Weisstanner, Christian Rummel, George N Thalmann, Rajeev K Verma, Roland Wiest, Thomas M Kessler
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  ABSTRACT: PURPOSE: Although chronic pelvic pain syndrome impairs the life of millions of people worldwide, the exact pathomechanisms involved remain to be elucidated. As with other chronic pain syndromes, the central nervous system may have an important role in chronic pelvic pain syndrome. Thus, we assessed brain alterations associated with abnormal pain processing in patients with chronic pelvic pain syndrome. MATERIALS AND METHODS: Using brain morphology assessment applying structural magnetic resonance imaging, we prospectively investigated a consecutive series of 20 men with refractory chronic pelvic pain syndrome, and compared these patients to 20 gender and age matched healthy controls. Between group differences in relative gray matter volume and the association with bother of chronic pelvic pain syndrome were assessed using whole brain covariate analysis. RESULTS: Patients with chronic pelvic pain syndrome had a mean (±SD) age of 40 (±14) years, a mean NIH-CPSI (National Institutes of Health Chronic Prostatitis Symptom Index) total score of 28 (±6) and a mean pain subscale of 14 (±3). In patients with chronic pelvic pain syndrome compared to healthy controls there was a significant reduction in relative gray matter volume in the anterior cingulate cortex of the dominant hemisphere. This finding correlated with the NIH-CPSI total score (r = 0.57) and pain subscale (r = 0.51). CONCLUSIONS: Reduction in relative gray matter volume in the anterior cingulate cortex and correlation with bother of chronic pelvic pain syndrome suggest an essential role for the anterior cingulate cortex in chronic pelvic pain syndrome. Since this area is a core structure of emotional pain processing, central pathomechanisms of chronic pelvic pain syndrome may be considered a promising therapeutic target and may explain the often unsatisfactory results of treatments focusing on peripheral dysfunction.
  The Journal of urology 10/2012; · 4.02 Impact Factor
• Article: A Systems-Level Approach to Human Epileptic Seizures.

  Christian Rummel, Marc Goodfellow, Heidemarie Gast, Martinus Hauf, Frédérique Amor, Alexander Stibal, Luigi Mariani, Roland Wiest, Kaspar Schindler
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  ABSTRACT: Epileptic seizures are due to the pathological collective activity of large cellular assemblies. A better understanding of this collective activity is integral to the development of novel diagnostic and therapeutic procedures. In contrast to reductionist analyses, which focus solely on small-scale characteristics of ictogenesis, here we follow a systems-level approach, which combines both small-scale and larger-scale analyses. Peri-ictal dynamics of epileptic networks are assessed by studying correlation within and between different spatial scales of intracranial electroencephalographic recordings (iEEG) of a heterogeneous group of patients suffering from pharmaco-resistant epilepsy. Epileptiform activity as recorded by a single iEEG electrode is determined objectively by the signal derivative and then subjected to a multivariate analysis of correlation between all iEEG channels. We find that during seizure, synchrony increases on the smallest and largest spatial scales probed by iEEG. In addition, a dynamic reorganization of spatial correlation is observed on intermediate scales, which persists after seizure termination. It is proposed that this reorganization may indicate a balancing mechanism that decreases high local correlation. Our findings are consistent with the hypothesis that during epileptic seizures hypercorrelated and therefore functionally segregated brain areas are re-integrated into more collective brain dynamics. In addition, except for a special sub-group, a highly significant association is found between the location of ictal iEEG activity and the location of areas of relative decrease of localised EEG correlation. The latter could serve as a clinically important quantitative marker of the seizure onset zone (SOZ).
  Neuroinformatics 09/2012; · 2.97 Impact Factor
• Article: On seeing the trees and the forest: Single-signal and multisignal analysis of periictal intracranial EEG.

  Kaspar Schindler, Heidemarie Gast, Marc Goodfellow, Christian Rummel
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  ABSTRACT: Purpose:  Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals. Methods:  Single and multiple EEG signals are analyzed by combining methods from symbolic dynamics and information theory. This computationally efficient approach is chosen because at its core it consists of analyzing the occurrence of patterns and bears analogy to classical visual EEG reading. Symbolization is achieved by first mapping the EEG signals into bit strings, that is, long sequences of zeros and ones, depending solely on whether their amplitudes increase or decrease. Bit strings reflect relational aspects between consecutive values of the original EEG signals, but not the values themselves. For each bit string the relative frequencies of the different constituent short bit patterns are then determined and used to compute two information theoretical measures: (1) redundancy (R) of single bit strings characterizes electrical brain activity on a comparatively small spatial scale represented by a single EEG signal and (2) averaged pair-wise mutual information with all other bit strings (M), which allows tracking of larger-scale EEG dynamics. Key Findings:  We analyzed 20 periictal intracranial EEG recordings from five patients with pharmacoresistant temporal lobe epilepsy. At seizure onset, R first strongly increased and then decreased toward seizure termination, whereas M gradually increased throughout the seizure. Bit strings with maximal R were always derived from EEG signals recorded from the visually identified seizure-onset zone. When compared to the bit strings derived from other EEG signals, their M was relatively smaller. These findings are consistent with a strong but transient occurrence of information-poor, that is, redundant electrical brain activity on a smaller spatial scale, which is particularly pronounced in the seizure-onset zone. On a larger spatial scale, a progressively more collective state emerges, as revealed by increasing amounts of mutual information. Significance:  Information theoretical analysis of bit patterns derived from EEG signals helps to characterize periictal brain activity on different spatial scales in a quantitative and efficient way and may provide clinically relevant results.
  Epilepsia 07/2012; 53(9):1658-68. · 3.96 Impact Factor