Brain (Hyper)Excitability Revealed by Optimal Electrical Stimulation of GABAergic Interneurons.

BACKGROUND: Neurological disorders are often characterized by an excessive and prolonged imbalance between neural excitatory and inhibitory processes. An ubiquitous finding among these disorders is the disrupted function of inhibitory GABAergic interneurons. OBJECTIVE: The objective is to propose a novel stimulation procedure able to evaluate the efficacy of inhibition imposed by GABAergic interneurons onto pyramidal cells from evoked responses observed in local field potentials (LFPs). METHODS: Using a computational modeling approach combined with in vivo and in vitro electrophysiological recordings, we analyzed the impact of electrical extracellular, local, bipolar stimulation (ELBS) on brain tissue. We implemented the ELBS effects in a neuronal population model in which we can tune the excitation-inhibition ratio and we investigated stimulation-related parameters. Computer simulations led to sharp predictions regarding: i) the shape of evoked responses as observed in local field potentials, ii) the type of cells (pyramidal neurons and interneurons) contributing to these field responses and iii) the optimal tuning of stimulation parameters (intensity and frequency) to evoke meaningful responses. These predictions were tested in vivo (mouse). Neurobiological mechanisms were assessed in vitro (hippocampal slices). RESULTS: Appropriately-tuned ELBS allows for preferential activation of GABAergic interneurons. A quantitative neural network excitability index (NNEI) is proposed. It is computed from stimulation-induced responses as reflected in local field potentials. NNEI was used in four patients with focal epilepsy. Results show that it can readily reveal hyperexcitable brain regions. CONCLUSION: Well-tuned ELBS and NNEI can be used to locally probe brain regions and quantify the (hyper)excitability of the underlying brain tissue.

Connectivity and phase coherence in neural network models of interconnected Z(4)-bi-stable units.

A phenomenological neural network model with bi-stable oscillatory units is used to model up- and down-states. These states have been observed in vivo in biological neuronal systems and feature oscillatory, limit cycle type of behavior in the up-states. A network is formed by a set of interconnected units. Two different types of network layouts are considered in this work: networks with hierarchical connections and hubs and networks with random connections. The phase coherence between the different units is analyzed and compared to the connectivity distance between nodes. In addition the connectivity degree of a node is associated to the average phase coherence with all other units. The results show that we may be able to identify the set of hubs in a network based on the phase coherence estimates between the different nodes. If the network is very dense or randomly connected, the underlying network structure, however, can not be derived uniquely from the phase coherence.

Plasticity-modulated seizure dynamics for seizure termination in realistic neuronal models.

In previous studies we showed that autonomous absence seizure generation and termination can be explained by realistic neuronal models eliciting bi-stable dynamics. In these models epileptic seizures are triggered either by external stimuli (reflex epilepsies) or by internal fluctuations. This scenario predicts exponential distributions of the duration of the seizures and of the inter-ictal intervals. These predictions were validated in rat models of absence epilepsy, as well as in a few human cases. Nonetheless, deviations from the predictions with respect to seizure duration distributions remained unexplained. The objective of the present work is to implement a simple but realistic computational model of a neuronal network including synaptic plasticity and ionic current dynamics and to explore the dynamics of the model with special emphasis on the distributions of seizure and inter-ictal period durations. We use as a basis our lumped model of cortical neuronal circuits. Here we introduce 'activity dependent' parameters, namely post-synaptic voltage-dependent plasticity, as well as a voltage-dependent hyperpolarization-activated current driven by slow and fast activation conductances. We examine the distributions of the durations of the seizure-like model activity and the normal activity, described respectively by the limit cycle and the steady state in the dynamics. We use a parametric γ-distribution fit as a quantifier. Our results show that autonomous, activity-dependent membrane processes can account for experimentally obtained statistical distributions of seizure durations, which were not explainable using the previous model. The activity-dependent membrane processes that display the strongest effect in accounting for these distributions are the hyperpolarization-dependent cationic (I(h)) current and the GABAa plastic dynamics. Plastic synapses (NMDA-type) in the interneuron population show only a minor effect. The inter-ictal statistics retain their consistency with the experimental data and the previous model.

Removal of epileptogenic sequences from video material: the role of color.

BACKGROUND: After Pokémon viewing triggered an epidemic of seizures in Japan, many efforts have been made to design safety guidelines and systems to protect subjects with photosensitivity. The authors developed a new method based upon nonlinear diffusion techniques capable of filtering the epileptogenic content of a video sequence related to color without altering its spatial and luminance content. METHODS: The authors showed to 25 photosensitive patients (18 women, mean age: 22 years) the original Pokémon sequence and a modified one in an ABBA protocol using two television (TV) sets (100 and 50 Hz). RESULTS: Twenty-three patients had a photoparoxysmal response (PPR) according to Waltz classification with at least one of the scenes. The modified sequence triggered fewer and less severe PPRs than the original version in both TVs (p < 0.001). Original sequences elicited generalized PPRs in 56.5% of the trials for the 50 Hz TV and in 41.3% for the 100 Hz TV, whereas modified sequences elicited these responses in only 8.7% (50 Hz) and 4.3% (100 Hz TV) of the trials (p < 0.001). Sensitivity to the modified version on the 50 Hz TV correlated with pattern sensitivity (p < 0.05). CONCLUSION: Specific manipulations of the color modulation-depth could be enough to decrease dramatically the risk of triggering seizures in susceptible subjects exposed to provocative visual scenes. This new method can be implemented in protective devices able to filter out the epileptogenic video sequences in which color plays a fundamental role while leaving intact the spatial content, frequency, and average luminance.

Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy.

OBJECTIVE: To explore and validate a novel stimulation and analysis paradigm proposed to monitor spatial distribution and temporal changes of the excitability state in patients with temporal lobe epilepsy (TLE). METHODS: We use intermittent pulse stimulation in the frequency range 10-20Hz. A quantitative measure of spectral phase de-modulation, the relative phase clustering index (rPCI) was applied to the evoked EEG signals, measured from electrodes implanted in the hippocampal formation. RESULTS: We found that in the interictal periods, high values of rPCI recorded from specific sites were correlated with the most probable seizure onset sites (SOS). Furthermore we found that high values of rPCI from certain locations correlated with shorter time intervals to the next seizure. CONCLUSIONS: Our clinical findings indicate that although the precise moment of ictal transitions is in general unpredictable, it may be possible to estimate the probability of occurrence of some epileptic seizures. SIGNIFICANCE: The use of the rPCI for probabilistic forecasting of upcoming epileptic seizures is warranted. rPCI measurements may be used to guide interventions with the aim of modifying local tissue excitability that ultimately might prevent ictal transitions.

Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network.

It is currently believed that the mechanisms underlying spindle oscillations are related to those that generate spike and wave (SW) discharges. The mechanisms of transition between these two types of activity, however, are not well understood. In order to provide more insight into the dynamics of the neuronal networks leading to seizure generation in a rat experimental model of absence epilepsy we developed a computational model of thalamo-cortical circuits based on relevant (patho)physiological data. The model is constructed at the macroscopic level since this approach allows to investigate dynamical properties of the system and the role played by different mechanisms in the process of seizure generation, both at short and long time scales. The main results are the following: (i) SW discharges represent dynamical bifurcations that occur in a bistable neuronal network; (ii) the durations of paroxysmal and normal epochs have exponential distributions, indicating that transitions between these two stable states occur randomly over time with constant probabilities; (iii) the probabilistic nature of the onset of paroxysmal activity implies that it is not possible to predict its occurrence; (iv) the bistable nature of the dynamical system allows that an ictal state may be aborted by a single counter-stimulus.

Gamma-band phase clustering and photosensitivity: is there an underlying mechanism common to photosensitive epilepsy and visual perception?

Photosensitive epilepsy (PSE) is the most common form of human reflex epilepsy, appearing in up to 10% of epileptic children. It also offers a highly reproducible model to investigate whether changes in neuronal activity preceding the transition to an epileptic photoparoxysmal response (PPR) may be detected. We studied 10 patients with idiopathic PSE (eight female, mean age 26 years, range 9-51 years) using magnetoencephalography. In addition, we also studied the responses of five normal controls (mean age 24 years, age range 9-35 years) and three non-photosensitive epileptic patients (mean age 10 years, range 8-11 years). Spectral analysis of the MEG signals recorded during intermittent photic stimulation revealed relevant information in the phase spectrum. To quantify this effect, we introduced a second order response feature of the stimulus-triggered visual response preceding the PPR: the phase clustering index, which measures how close the phases of successive periods are grouped for each frequency component for all periods of the stimuli applied. We recorded a total of 86 PPRs, including several absence seizures, in nine of the 10 patients. We found that an enhancement of phase synchrony in the gamma-band (30-120 Hz), harmonically related to the frequency of stimulation, preceded the stimulation trials that evolved into PPRs, and differed significantly from that encountered in trials not followed by PPR or in control subjects. This novel finding leads us to postulate that a pathological deviation of normally occurring synchronization of gamma oscillations, underlying perceptional processes, mediates the epileptic transition in PSE.

Computational model of thalamo-cortical networks: dynamical control of alpha rhythms in relation to focal attention.

EEG/MEG rhythmic activities such as alpha rhythms, of the visual or of the somato-sensory cortex, are commonly modulated as subjects perform certain tasks or react to specific stimuli. In general, these activities change depending on extrinsic or intrinsic events. A decrease of the amplitude of alpha rhythmic activity occurring after a given event, which manifests as a decrease of a spectral peak, is called event-related desynchronization (ERD), whereas the inverse is called event-related synchronization (ERS), since it is assumed that the power of a spectral peak is related to the degree of synchrony of the underlying oscillating neuronal populations. An intriguing observation in this respect [Pfurtscheller and Neuper, Neurosci. Lett. 174 (1994) 93-96] was that ERD of alpha rhythms recorded over the central areas was accompanied by ERS, within the same frequency band, recorded over neighboring areas. In case the event was a hand movement, ERD was recorded over the scalp overlying the hand cortical area, whereas ERS was concomitantly recorded over the midline, whereas if the movement was of the foot the opposite was found. We called this phenomenon 'focal ERD/surround ERS'. The question of how this phenomenon may be generated was approached by means of a computational model of thalamo-cortical networks, that incorporates basic properties of neurons and synaptic interactions. These simulation studies revealed that this antagonistic ERD/ERS phenomenon depends on the functional interaction between the populations of thalamo-cortical cells (TCR) and reticular nucleus cells (RE) and on how this interaction is modulated by cholinergic inputs. An essential feature of this interaction is the existence of cross-talk between different sectors of RE that correspond to distinct sensory modules (e.g. hand, foot). These observations led us to formulate the hypothesis that this basic neurophysiological mechanism can account for the general observation that enhanced attention given to a certain stimulus (the focus) is coupled to inhibition of attention to other stimuli (the surround).

Self-organized dynamics in plastic neural networks: bistability and coherence.

In this paper, we study the combined dynamics of the neural activity and the synaptic efficiency changes in a fully connected network of biologically realistic neurons with simple synaptic plasticity dynamics including both potentiation and depression. Using a mean-field of technique, we analyzed the equilibrium states of neural networks with dynamic synaptic connections and found a class of bistable networks. For this class of networks, one of the stable equilibrium states shows strong connectivity and coherent responses to external input. In the other stable equilibrium, the network is loosely connected and responds non coherently to external input. Transitions between the two states can be achieved by positively or negatively correlated external inputs. Such networks can therefore switch between their phases according to the statistical properties of the external input. Non-coherent input can only "rcad" the state of the network, while a correlated one can change its state. We speculate that this property, specific for plastic neural networks, can give a clue to understand fully unsupervised learning models.

Magnetic source imaging in fixation-off sensitivity: relationship with alpha rhythm.

A patient in whom a variety of abnormal EEG findings can be elicited by elimination of central vision and fixation demonstrates fixation-off sensitivity. The underlying mechanisms of fixation-off sensitivity and its relationship with alpha rhythm remain unclear. To obtain a better understanding of this issue, we used a whole-head magnetoencephalograph to study an epileptic child with fixation-off sensitivity resulting in a 3-Hz, large-amplitude oscillation (300 microV) over the occipital regions on the EEG. Magnetic source localization revealed alpha activity around the calcarine fissure and surrounding parieto-occipital areas. Magnetic sources of abnormalities relating to fixation-off sensitivity, however, usually were located deeper in the brain, suggesting more extensively distributed sources, with involvement of the cingulate gyrus and the basomesial occipitotemporal region. Distributions of the sources of both types of activities show independent clusters but also an appreciable domain of overlap. Our findings indicate that abnormalities related to fixation-off sensitivity can emerge in thalamocortical networks, with larger and more anterior cortical distribution than those that generate alpha rhythm. Transition in the type of oscillation appears not only to depend on a change in cellular dynamics but also to be reflected in a different spatial distribution of the underlying neuronal networks.

Advances in three-dimensional diagnostic radiology.

The maturity of current 3D rendering software in combination with recent developments in computer vision techniques enable an exciting range of applications for the visualisation, measurement and interactive manipulation of volumetric data, relevant both for diagnostic imaging and for anatomy. This paper reviews recent work in this area from the Image Sciences Institute at Utrecht University. The processes that yield a useful visual presentation are sequential. After acquisition and before any visualisation, an essential step is to prepare the data properly: this field is known as 'image processing' or 'computer vision' in analogy with the processing in human vision. Examples will be discussed of modern image enhancement and denoising techniques, and the complex process of automatically finding the objects or regions of interest, i.e. segmentation. One of the newer and promising methodologies for image analysis is based on a mathematical analysis of the human (cortical) visual processing: multiscale image analysis. After preprocessing the 3D rendering can be acquired by simulating the 'ray casting' in the computer. New possibilities are presented, such as the integrated visualisation in one image of (accurately registered) datasets of the same patient acquired in different modality scanners. Other examples include colour coding of functional data such as SPECT brain perfusion or functional magnetic resonance (MR) data and even metric data such as skull thickness on the rendered 3D anatomy from MR or computed tomography (CT). Optimal use and perception of 3D visualisation in radiology requires fast display and truly interactive manipulation facilities. Modern and increasingly cheaper workstations ( < $10000) allow this to be a reality. It is now possible to manipulate 3D images of 256 at 15 frames per second interactively, placing virtual reality within reach. The possibilities of modern workstations become increasingly more sophisticated and versatile. Examples presented include the automatic detection of the optimal viewing angle of the neck of aneurysms and the simulation of the design and placement procedure of intra-abdominal aortic stents. Such developments, together with the availability of high-resolution datasets of modern scanners and data such as from the NIH Visible Human project, have a dramatic impact on interactive 3D anatomical atlases.

Coherency and connectivity in oscillating neural networks: linear partialization analysis.

This paper studies the relation between the functional synaptic connections between two artificial neural networks and the correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks showed oscillatory behavior as a result of their internal synaptic connectivity. We found that both excitation and inhibition cause phase locking of the oscillating activities. When the two networks excite each other the oscillations synchronize with zero phase lag, whereas mutual inhibition between the networks resulted in an anti-phase (half period phase difference) synchronization. Correlations between the activities of the two networks can also be caused by correlated external inputs driving the systems (common input). Our analysis shows that when the networks exhibit oscillatory behavior and the rate of the common input is smaller than a characteristic network oscillator frequency, the cross-correlation functions between the activities of two systems still carry information about the mutual synaptic connectivity. This information can be retrieved with linear partialization, removing the influence of the common input. We further explored the network responses to periodic external input. We found that when the input is of a frequency smaller than a certain threshold, the network responds with bursts at the same frequency as the input. Above the threshold, the network responds with a fraction of the input frequency. This frequency threshold, characterizing the oscillatory properties of the network, is also found to determine the limit to which linear partialization works.

Non-linear dynamics of columns of cat visual cortex revealed by simulation and experiment.

Correlation images were derived from simultaneous recordings of 12 signals representing the synaptic activity at different layers of a column in cat visual cortex (area 18) and 12 signals representing the local average spiking activity at the same locations. Because the ongoing activity and the activity evoked by stroboscopic flashes yielded the same correlation image, ongoing activity is caused by an input to a column similar to flash-evoked activity and is thus not endogenous. Moving bar stimuli evoked bursts of oscillations (25-75 Hz band) in the correlation image. The rhythm of these oscillations was not related to any frequency component in the stimulus. In all correlation images we observed that synaptic activity in one layer resulted in simultaneous spiking activity in all layers with latency differences smaller than 2 ms (the sample interval used). Similar behaviour was observed in a simulation experiment where we 'realistically' modelled one column of visual cortex with 1000 three-compartmental neurons in 11 functional layers. When such a model column was tuned to yield a stable and excitable system with low ongoing activity, activation of any of the layers caused simultaneous activity in all 11 layers. Both the simulation and the experimental results suggest that a column can be regarded as a basic processing element sending the same information over all its outputs to other columns within the same cortical region, other visual areas and subcortical structures.