Ulrike Richter

Levodopa-induced dyskinesia is strongly associated with resonant cortical oscillations.

The standard pharmacological treatment for Parkinsons disease using the dopamine precursor levodopa is unfortunately limited by gradual development of disabling involuntary movements for which the underlying causes are poorly understood. Here we show that levodopa-induced dyskinesia in hemiparkinsonian rats is strongly associated with pronounced 80 Hz local field potential oscillations in the primary motor cortex following levodopa treatment. When this oscillation is interrupted by application of a dopamine antagonist onto the cortical surface the dyskinetic symptoms disappear. The finding that abnormal cortical oscillations are a key pathophysiological mechanism calls for a revision of the prevailing hypothesis that links levodopa-induced dyskinesia to an altered sensitivity to dopamine only in the striatum. Apart from having important implications for the treatment of Parkinsons disease, the discovered pathophysiological mechanism may also play a role in several other psychiatric and neurological conditions involving cortical dysfunction.

A novel approach to propagation pattern analysis in intracardiac atrial fibrillation signals

The purpose of this study is to investigate propagation patterns in intracardiac signals recorded during atrial fibrillation (AF) using an approach based on partial directed coherence (PDC), which evaluates directional coupling between multiple signals in the frequency domain. The PDC is evaluated at the dominant frequency of AF signals and tested for significance using a surrogate data procedure specifically designed to assess causality. For significantly coupled sites, the approach allows also to estimate the delay in propagation. The methods potential is illustrated with two simulation scenarios based on a detailed ionic model of the human atrial myocyte as well as with real data recordings, selected to present typical propagation mechanisms and recording situations in atrial tachyarrhythmias. In both simulation scenarios the significant PDCs correctly reflect the direction of coupling and thus the propagation between all recording sites. In the real data recordings, clear propagation patterns are identified which agree with previous clinical observations. Thus, the results illustrate the ability of the novel approach to identify propagation patterns from intracardiac signals during AF, which can provide important information about the underlying AF mechanisms, potentially improving the planning and outcome of arrhythmia ablation.

Spinal cord stimulation alleviates motor deficits in a primate model of Parkinson disease.

Although deep brain electrical stimulation can alleviate the motor symptoms of Parkinson disease (PD), just a small fraction of patients with PD can take advantage of this procedure due to its invasive nature. A significantly less invasive method--epidural spinal cord stimulation (SCS)--has been suggested as an alternative approach for symptomatic treatment of PD. However, the mechanisms underlying motor improvements through SCS are unknown. Here, we show that SCS reproducibly alleviates motor deficits in a primate model of PD. Simultaneous neuronal recordings from multiple structures of the cortico-basal ganglia-thalamic loop in parkinsonian monkeys revealed abnormal highly synchronized neuronal activity within each of these structures and excessive functional coupling among them. SCS disrupted this pathological circuit behavior in a manner that mimics the effects caused by pharmacological dopamine replacement therapy or deep brain stimulation. These results suggest that SCS should be considered as an additional treatment option for patients with PD.

Propagation Pattern Analysis During Atrial Fibrillation Based on Sparse Modeling

In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares (LS) estimation. The propagation pattern analysis incorporates prior information on sparse coupling as well as the distance between the recording sites. Two optimization methods are employed for estimation of the model parameters, namely, the adaptive group least absolute selection and shrinkage operator (aLASSO), and a novel method named the distance-adaptive group LASSO (dLASSO). Using simulated data, both optimization methods were superior to LS estimation with respect to detection and estimation performance. The normalized error between the true and estimated model parameters dropped from 0.20± 0.04 for LS estimation to 0.03 ± 0.01 for both aLASSO and dLASSO when the number of available data samples exceeded the number of model parameters by a factor of 5. For shorter data segments, the error reduction was more pronounced and information on the distance gained in importance. Propagation pattern analysis was also studied on intracardiac AF data, the results showing that the identification of propagation patterns is substantially simplified by the sparsity assumption.

Spatial characteristics of atrial fibrillation electrocardiograms

BACKGROUND The present study investigates spatial properties of atrial fibrillation (AF) by analyzing vectorcardiogram loops synthesized from 12-lead electrocardiograms (ECGs). METHODS After atrial signal extraction, spatial properties are characterized through analysis of successive, fixed-length signal segments and expressed in loop orientation, that is, azimuth and elevation, as well as in loop morphology, that is, planarity and planar geometry. It is hypothesized that more organized AF, expressed by a lower AF frequency, is associated with decreased variability in loop morphology. Atrial fibrillation frequency is determined using spectral analysis. RESULTS Twenty-six patients with chronic AF were analyzed using 60-second ECG recordings. Loop orientation was similar when determined from either entire 60- or 1-second segments. For 1-second segments, the correlation between AF frequency and the parameters planarity and planar geometry were 0.608 (P < .001) and 0.543 (P < .005), respectively. CONCLUSIONS Quantification of AF organization based on AF frequency and spatial characteristics from the ECG is possible. The results suggested a relatively weak coupling between loop morphology and AF frequency when determined from the surface ECG.

Mechanisms underlying cortical resonant states: implications for levodopa-induced dyskinesia

Abstract A common observation in recordings of neuronal activity from the cerebral cortex is that populations of neurons show patterns of synchronized oscillatory activity. However, it has been suggested that neuronal synchronization can, in certain pathological conditions, become excessive and possibly have a pathogenic role. In particular, aberrant oscillatory activation patterns have been implicated in conditions involving cortical dysfunction. We here review the mechanisms thought to be involved in the generation of cortical oscillations and discuss their relevance in relation to a recent finding indicating that high-frequency oscillations in the cerebral cortex have an important role in the generation of levodopa-induced dyskinesia. On the basis of these insights, it is suggested that the identification of physiological changes associated with symptoms of disease is a particularly important first step toward a more rapid development of novel treatment strategies.

Untangling Cortico-Striatal Connectivity and Cross-Frequency Coupling in L-DOPA-Induced Dyskinesia

We simultaneously recorded local field potentials (LFPs) in the primary motor cortex and sensorimotor striatum in awake, freely behaving, 6-OHDA lesioned hemi-parkinsonian rats in order to study the features directly related to pathological states such as parkinsonian state and levodopa-induced dyskinesia. We analyzed the spectral characteristics of the obtained signals and observed that during dyskinesia the most prominent feature was a relative power increase in the high gamma frequency range at around 80 Hz, while for the parkinsonian state it was in the beta frequency range. Here we show that during both pathological states effective connectivity in terms of Granger causality is bidirectional with an accent on the striatal influence on the cortex. In the case of dyskinesia, we also found a high increase in effective connectivity at 80 Hz. In order to further understand the 80-Hz phenomenon, we performed cross-frequency analysis and observed characteristic patterns in the case of dyskinesia but not in the case of the parkinsonian state or the control state. We noted a large decrease in the modulation of the amplitude at 80 Hz by the phase of low frequency oscillations (up to ~10 Hz) across both structures in the case of dyskinesia. This may suggest a lack of coupling between the low frequency activity of the recorded network and the group of neurons active at ~80 Hz.

Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia.

Disorders affecting the central nervous system have proven particularly hard to treat, and disappointingly few novel therapies have reached the clinics in recent decades. A better understanding of the physiological processes in the brain underlying various symptoms could therefore greatly improve the rate of progress in this field. We here show how systems-level descriptions of different brain states reliably can be obtained through a newly developed method based on large-scale recordings in distributed neural networks encompassing several different brain structures. Using this technology, we characterize the neurophysiological states associated with parkinsonism and levodopa-induced dyskinesia in a rodent model of Parkinsons disease together with pharmacological interventions aimed at reducing dyskinetic symptoms. Our results show that the obtained electrophysiological data add significant information to conventional behavioral evaluations and hereby elucidate the underlying effects of treatments in greater detail. Taken together, these results potentially open up for studies of neurophysiological mechanisms underlying symptoms in a wide range of neurological and psychiatric conditions that until now have been very hard to investigate in animal models of disease.

Signal-Adapted Tight Frames on Graphs

The analysis of signals on complex topologies modeled by graphs is a topic of increasing importance. Decompositions play a crucial role in the representation and processing of such information. Here, we propose a new tight frame design that is adapted to a class of signals on a graph. The construction starts from a prototype Meyer-type system of kernels with uniform subbands. The ensemble energy spectral density is then defined for a given set of signals defined on the graph. The prototype design is then warped such that the resulting subbands capture the same amount of energy for the signal class. This approach accounts at the same time for graph topology and signal features. The proposed frames are constructed for three different graph signal sets and are compared with non-signal-adapted frames. Vertex localization of a set of resulting atoms is studied. The frames are then used to decompose a set of real graph signals and are also used in a setting of signal denoising. The results illustrate the superiority of the designed signal-adapted frames, over frames blind to signal characteristics, in representing data and in denoising.

Design of a high-density multi-channel electrode for multi-structure parallel recordings in rodents

In neurophysiology, investigating brain connectivity within and between different brain structures is of fundamental importance for understanding nervous system function and its relation to behavior. Yet, parallel recordings in multiple brain structures is highly challenging, especially in rodents, which are most commonly employed in neurophysiological research but rather small in size. In this study, the design and manufacturing of a high-density multi-channel electrode for chronic, multi-structure parallel recordings in rats is presented and exemplified with functional neuronal recordings from 128 recording channels, placed bilaterally in eight different brain structures, in an awake, freely moving animal.