On-demand low-frequency stimulation for seizure control: efficacy and behavioural implications.

Mesial temporal lobe epilepsy (MTLE), the most common form of focal epilepsy in adults, is often refractory to medication and associated with hippocampal sclerosis. Deep brain stimulation represents an alternative treatment option for drug-resistant patients who are ineligible for resective brain surgery. In clinical practice, closed-loop stimulation at high frequencies is applied to interrupt ongoing seizures, yet has (i) a high incidence of false detections; (ii) the drawback of delayed seizure-suppressive intervention; and (iii) limited success in sclerotic tissue. As an alternative, low-frequency stimulation (LFS) has been explored recently in patients with focal epilepsies. In preclinical epilepsy models, hippocampal LFS successfully prevented seizures when applied continuously. Since it would be advantageous to reduce the stimulation load, we developed a protocol for on-demand LFS. Given the importance of the hippocampus for navigation and memory, we investigated potential consequences of LFS on hippocampal function. To this end, we used the intrahippocampal kainate mouse model, which recapitulates the key features of MTLE, including spontaneous seizure activity and hippocampal sclerosis. Specifically, our online detection algorithm monitored epileptiform activity in hippocampal local field potential recordings and identified short epileptiform bursts preceding focal seizure clusters, triggering hippocampal LFS to stabilize the network state. To probe behavioural performance, we tested the acute influence of LFS on anxiety-like behaviour in the light-dark box test, spatial and non-spatial memory in the object location memory and novel object recognition test, as well as spatial navigation and long-term memory in the Barnes maze. On-demand LFS was almost as effective as continuous LFS in preventing focal seizure clusters but with a significantly lower stimulation load. When we compared the behavioural performance of chronically epileptic mice to healthy controls, we found that both groups were equally mobile, but epileptic mice displayed an increased anxiety level, altered spatial learning strategy and impaired memory performance. Most importantly, with the application of hippocampal LFS before behavioural training and test sessions, we could rule out deleterious effects on cognition and even show an alleviation of deficits in long-term memory recall in chronically epileptic mice. Taken together, our findings may provide a promising alternative to current therapies, overcoming some of their major limitations, and inspire further investigation of LFS for seizure control in focal epilepsy syndromes.

Structural Modularity Tunes Mesoscale Criticality in Biological Neuronal Networks.

Numerous studies suggest that biological neuronal networks self-organize toward a critical state with stable recruitment dynamics. Individual neurons would then statistically activate exactly one further neuron during activity cascades termed neuronal avalanches. Yet, it is unclear if and how this can be reconciled with the explosive recruitment dynamics within neocortical minicolumns in vivo and within neuronal clusters in vitro, which indicates that neurons form supercritical local circuits. Theoretical studies propose that modular networks with a mix of regionally subcritical and supercritical dynamics would create apparently critical dynamics, resolving this inconsistency. Here, we provide experimental support by manipulating the structural self-organization process of networks of cultured rat cortical neurons (either sex). Consistent with the prediction, we show that increasing clustering in neuronal networks developing in vitro strongly correlates with avalanche size distributions transitioning from supercritical to subcritical activity dynamics. Avalanche size distributions approximated a power law in moderately clustered networks, indicating overall critical recruitment. We propose that activity-dependent self-organization can tune inherently supercritical networks toward mesoscale criticality by creating a modular structure in neuronal networks.SIGNIFICANCE STATEMENT Critical recruitment dynamics in neuronal networks are considered optimal for information processing in the brain. However, it remains heavily debated how neuronal networks would self-organize criticality by detailed fine-tuning of connectivity, inhibition, and excitability. We provide experimental support for theoretical considerations that modularity tunes critical recruitment dynamics at the mesoscale level of interacting neuron clusters. This reconciles reports of supercritical recruitment dynamics in local neuron clusters with findings on criticality sampled at mesoscopic network scales. Intriguingly, altered mesoscale organization is a prominent aspect of various neuropathological diseases currently investigated in the framework of criticality. We therefore believe that our findings would also be of interest for clinical scientists searching to link the functional and anatomic signatures of such brain disorders.

Mesoscale Architecture Shapes Initiation and Richness of Spontaneous Network Activity.

Spontaneous activity in the absence of external input, including propagating waves of activity, is a robust feature of neuronal networks in vivo and in vitro The neurophysiological and anatomical requirements for initiation and persistence of such activity, however, are poorly understood, as is their role in the function of neuronal networks. Computational network studies indicate that clustered connectivity may foster the generation, maintenance, and richness of spontaneous activity. Since this mesoscale architecture cannot be systematically modified in intact tissue, testing these predictions is impracticable in vivo Here, we investigate how the mesoscale structure shapes spontaneous activity in generic networks of rat cortical neurons in vitro In these networks, neurons spontaneously arrange into local clusters with high neurite density and form fasciculating long-range axons. We modified this structure by modulation of protein kinase C, an enzyme regulating neurite growth and cell migration. Inhibition of protein kinase C reduced neuronal aggregation and fasciculation of axons, i.e., promoted uniform architecture. Conversely, activation of protein kinase C promoted aggregation of neurons into clusters, local connectivity, and bundling of long-range axons. Supporting predictions from theory, clustered networks were more spontaneously active and generated diverse activity patterns. Neurons within clusters received stronger synaptic inputs and displayed increased membrane potential fluctuations. Intensified clustering promoted the initiation of synchronous bursting events but entailed incomplete network recruitment. Moderately clustered networks appear optimal for initiation and propagation of diverse patterns of activity. Our findings support a crucial role of the mesoscale architectures in the regulation of spontaneous activity dynamics.SIGNIFICANCE STATEMENT Computational studies predict richer and persisting spatiotemporal patterns of spontaneous activity in neuronal networks with neuron clustering. To test this, we created networks of varying architecture in vitro Supporting these predictions, the generation and spatiotemporal patterns of propagation were most variable in networks with intermediate clustering and lowest in uniform networks. Grid-like clustering, on the other hand, facilitated spontaneous activity but led to degenerating patterns of propagation. Neurons outside clusters had weaker synaptic input than neurons within clusters, in which increased membrane potential fluctuations facilitated the initiation of synchronized spike activity. Our results thus show that the intermediate level organization of neuronal networks strongly influences the dynamics of their activity.

Theta frequency decreases throughout the hippocampal formation in a focal epilepsy model.

Mesial temporal lobe epilepsy is characterized by focal, recurrent spontaneous seizures, sclerosis and granule cell dispersion (GCD) in the hippocampal formation. Changes in theta rhythm properties have been correlated with the severity of hippocampal restructuring and were suggested as a cause of memory deficits accompanying epilepsy. For severe sclerosis, it has even been questioned whether theta band oscillations persist. We asked how theta oscillations change with graded restructuring along the longitudinal hippocampal axis and whether these changes correlate with the overall severity of temporal lobe epilepsy. We recorded local field potentials in the medial entorhinal cortex and along the septo-temporal axis of the dentate gyrus at sites with different degrees of GCD in freely behaving epileptic mice. Theta frequency was decreased at all recording positions throughout the dentate gyrus and in the medial entorhinal cortex, irrespective of the extent of GCD or the rate of severe epileptic events. The frequency reduction by up to 1.7 Hz, corresponding to 1/3 octaves within the theta range, was present during rest, exploration and running. Despite the frequency reduction, theta oscillations remained coherent across the hippocampal formation and were modulated by running speed as in controls. The reduction in theta frequency thus is likely not a consequence of the local restructuring but rather a global phenomenon affecting the hippocampal formation as a whole.

Synaptic Remodeling of Entorhinal Input Contributes to an Aberrant Hippocampal Network in Temporal Lobe Epilepsy.

The hippocampus is reciprocally connected with the entorhinal cortex. Although several studies emphasized a role for the entorhinal cortex in mesial temporal lobe epilepsy (MTLE), it remains uncertain whether its synaptic connections with the hippocampus are altered. To address this question, we traced hippocampo-entorhinal and entorhino-hippocampal projections, assessed their connectivity with the respective target cells and examined functional alterations in a mouse model for MTLE. We show that hippocampal afferents to the dorsal entorhinal cortex are lost in the epileptic hippocampus. Conversely, entorhino-dentate projections via the medial perforant path (MPP) are preserved, but appear substantially altered on the synaptic level. Confocal imaging and 3D-reconstruction revealed that new putative contacts are established between MPP fibers and dentate granule cells (DGCs). Immunohistochemical identification of pre- and postsynaptic elements indicated that these contacts are functionally mature synapses. On the ultrastructural level, pre- and postsynaptic compartments of MPP synapses were strongly enlarged. The length and complexity of postsynaptic densities were also increased pointing to long-term potentiation-related morphogenesis. Finally, whole-cell recordings of DGCs revealed an enhancement of evoked excitatory postsynaptic currents. In conclusion, the synaptic rearrangement of excitatory inputs to DGCs from the medial entorhinal cortex may contribute to the epileptogenic circuitry in MTLE.

Autonomous Optimization of Targeted Stimulation of Neuronal Networks.

Driven by clinical needs and progress in neurotechnology, targeted interaction with neuronal networks is of increasing importance. Yet, the dynamics of interaction between intrinsic ongoing activity in neuronal networks and their response to stimulation is unknown. Nonetheless, electrical stimulation of the brain is increasingly explored as a therapeutic strategy and as a means to artificially inject information into neural circuits. Strategies using regular or event-triggered fixed stimuli discount the influence of ongoing neuronal activity on the stimulation outcome and are therefore not optimal to induce specific responses reliably. Yet, without suitable mechanistic models, it is hardly possible to optimize such interactions, in particular when desired response features are network-dependent and are initially unknown. In this proof-of-principle study, we present an experimental paradigm using reinforcement-learning (RL) to optimize stimulus settings autonomously and evaluate the learned control strategy using phenomenological models. We asked how to (1) capture the interaction of ongoing network activity, electrical stimulation and evoked responses in a quantifiable 'state' to formulate a well-posed control problem, (2) find the optimal state for stimulation, and (3) evaluate the quality of the solution found. Electrical stimulation of generic neuronal networks grown from rat cortical tissue in vitro evoked bursts of action potentials (responses). We show that the dynamic interplay of their magnitudes and the probability to be intercepted by spontaneous events defines a trade-off scenario with a network-specific unique optimal latency maximizing stimulus efficacy. An RL controller was set to find this optimum autonomously. Across networks, stimulation efficacy increased in 90% of the sessions after learning and learned latencies strongly agreed with those predicted from open-loop experiments. Our results show that autonomous techniques can exploit quantitative relationships underlying activity-response interaction in biological neuronal networks to choose optimal actions. Simple phenomenological models can be useful to validate the quality of the resulting controllers.

Mossy fiber sprouting and pyramidal cell dispersion in the hippocampal CA2 region in a mouse model of temporal lobe epilepsy.

Dentate granule cells and the hippocampal CA2 region are resistant to cell loss associated with mesial temporal lobe epilepsy (MTLE). It is known that granule cells undergo mossy fiber sprouting in the dentate gyrus which contributes to a recurrent, proepileptogenic circuitry in the hippocampus. Here it is shown that mossy fiber sprouting also targets CA2 pyramidal cell somata and that the CA2 region undergoes prominent structural reorganization under epileptic conditions. Using the intrahippocampal kainate mouse model for MTLE and the CA2-specific markers Purkinje cell protein 4 (PCP4) and regulator of G-Protein signaling 14 (RGS14), it was found that during epileptogenesis CA2 neurons survive and disperse in direction of CA3 and CA1 resulting in a significantly elongated CA2 region. Using transgenic mice that express enhanced green fluorescent protein (eGFP) in granule cells and mossy fibers, we show that the recently described mossy fiber projection to CA2 undergoes sprouting resulting in aberrant large, synaptoporin-expressing mossy fiber boutons which surround the CA2 pyramidal cell somata. This opens up the potential for altered synaptic transmission that might contribute to epileptic activity in CA2. Indeed, intrahippocampal recordings in freely moving mice revealed that epileptic activity occurs concomitantly in the dentate gyrus and in CA2. Altogether, the results call attention to CA2 as a region affected by MTLE-associated pathological restructuring.

Quantitative examination of stimulus-response relations in cortical networks in vitro.

Variable responses of neuronal networks to repeated sensory or electrical stimuli reflect the interaction of the stimulus' response with ongoing activity in the brain and its modulation by adaptive mechanisms, such as cognitive context, network state, or cellular excitability and synaptic transmission capability. Here, we focus on reliability, length, delays, and variability of evoked responses with respect to their spatial distribution, interaction with spontaneous activity in the networks, and the contribution of GABAergic inhibition. We identified network-intrinsic principles that underlie the formation and modulation of spontaneous activity and stimulus-response relations with the use of state-dependent stimulation in generic neuronal networks in vitro. The duration of spontaneously recurring network-wide bursts of spikes was best predicted by the length of the preceding interval. Length, delay, and structure of responses to identical stimuli systematically depended on stimulus timing and distance to the stimulation site, which were described by a set of simple functions of spontaneous activity. Response length at proximal recording sites increased with the duration of prestimulus inactivity and was best described by a saturation function y(t) = A(1 - e(-αt)). Concomitantly, the delays of polysynaptic late responses at distant sites followed an exponential decay y(t) = Be(-ßt) + C. In addition, the speed of propagation was determined by the overall state of the network at the moment of stimulation. Disinhibition increased the number of spikes/network burst and interburst interval length at unchanged gross firing rate, whereas the response modulation by the duration of prestimulus inactivity was preserved. Our data suggest a process of network depression during bursts and subsequent recovery that limit evoked responses following distinct rules. We discuss short-term synaptic depression due to depletion of neurotransmitter vesicles as an underlying mechanism. The seemingly unreliable patterns of spontaneous activity and stimulus-response relations thus follow a predictable structure determined by the interdependencies of network structures and activity states.

Altered θ coupling between medial entorhinal cortex and dentate gyrus in temporal lobe epilepsy.

PURPOSE: Temporal lobe epilepsy is often accompanied by neuron loss and rewiring in the hippocampus. We hypothesized that the interaction of subnetworks of the entorhinal-hippocampal loop between epileptic events should show significant signatures of these pathologic changes. METHODS: We combined simultaneous recording of local field potentials in entorhinal cortex (EC) and dentate gyrus (DG) in freely behaving kainate-injected mice with histologic analyses and computational modeling. KEY FINDINGS: In healthy mice, theta band activity was synchronized between EC and DG. In contrast, in epileptic mice, theta activity in the EC was delayed with respect to the DG. A computational neural mass model suggests that hippocampal cell loss imbalances the coupling of subnetworks, introducing the shift. SIGNIFICANCE: We show that pathologic dynamics in epilepsy encompass ongoing activity in the entorhinal-hippocampal loop beyond acute epileptiform activity. This predominantly affects theta band activity, which links this shift in entorhinal-hippocampal interaction to behavioral aspects in epilepsy.

Self-organized criticality in developing neuronal networks.

Recently evidence has accumulated that many neural networks exhibit self-organized criticality. In this state, activity is similar across temporal scales and this is beneficial with respect to information flow. If subcritical, activity can die out, if supercritical epileptiform patterns may occur. Little is known about how developing networks will reach and stabilize criticality. Here we monitor the development between 13 and 95 days in vitro (DIV) of cortical cell cultures (n = 20) and find four different phases, related to their morphological maturation: An initial low-activity state (≈19 DIV) is followed by a supercritical (≈20 DIV) and then a subcritical one (≈36 DIV) until the network finally reaches stable criticality (≈58 DIV). Using network modeling and mathematical analysis we describe the dynamics of the emergent connectivity in such developing systems. Based on physiological observations, the synaptic development in the model is determined by the drive of the neurons to adjust their connectivity for reaching on average firing rate homeostasis. We predict a specific time course for the maturation of inhibition, with strong onset and delayed pruning, and that total synaptic connectivity should be strongly linked to the relative levels of excitation and inhibition. These results demonstrate that the interplay between activity and connectivity guides developing networks into criticality suggesting that this may be a generic and stable state of many networks in vivo and in vitro.

Intracortical probe arrays with silicon backbone and microelectrodes on thin polyimide wings enable long-term stable recordingsin vivo.

Objective.Recording and stimulating neuronal activity across different brain regions requires interfacing at multiple sites using dedicated tools while tissue reactions at the recording sites often prevent their successful long-term application. This implies the technological challenge of developing complex probe geometries while keeping the overall footprint minimal, and of selecting materials compatible with neural tissue. While the potential of soft materials in reducing tissue response is uncontested, the implantation of these materials is often limited to reliably target neuronal structures across large brain volumes.Approach.We report on the development of a new multi-electrode array exploiting the advantages of soft and stiff materials by combining 7-µm-thin polyimide wings carrying platinum electrodes with a silicon backbone enabling a safe probe implantation. The probe fabrication applies microsystems technologies in combination with a temporal wafer fixation method for rear side processing, i.e. grinding and deep reactive ion etching, of slender probe shanks and electrode wings. The wing-type neural probes are chronically implanted into the entorhinal-hippocampal formation in the mouse forin vivorecordings of freely behaving animals.Main results.Probes comprising the novel wing-type electrodes have been realized and characterized in view of their electrical performance and insertion capability. Chronic electrophysiologicalin vivorecordings of the entorhinal-hippocampal network in the mouse of up to 104 days demonstrated a stable yield of channels containing identifiable multi-unit and single-unit activity outperforming probes with electrodes residing on a Si backbone.Significance.The innovative fabrication process using a process compatible, temporary wafer bonding allowed to realize new Michigan-style probe arrays. The wing-type probe design enables a precise probe insertion into brain tissue and long-term stable recordings of unit activity due to the application of a stable backbone and 7-µm-thin probe wings provoking locally a minimal tissue response and protruding from the glial scare of the backbone.

Functional identification of biological neural networks using reservoir adaptation for point processes.

The complexity of biological neural networks does not allow to directly relate their biophysical properties to the dynamics of their electrical activity. We present a reservoir computing approach for functionally identifying a biological neural network, i.e. for building an artificial system that is functionally equivalent to the reference biological network. Employing feed-forward and recurrent networks with fading memory, i.e. reservoirs, we propose a point process based learning algorithm to train the internal parameters of the reservoir and the connectivity between the reservoir and the memoryless readout neurons. Specifically, the model is an Echo State Network (ESN) with leaky integrator neurons, whose individual leakage time constants are also adapted. The proposed ESN algorithm learns a predictive model of stimulus-response relations in in vitro and simulated networks, i.e. it models their response dynamics. Receiver Operating Characteristic (ROC) curve analysis indicates that these ESNs can imitate the response signal of a reference biological network. Reservoir adaptation improved the performance of an ESN over readout-only training methods in many cases. This also held for adaptive feed-forward reservoirs, which had no recurrent dynamics. We demonstrate the predictive power of these ESNs on various tasks with cultured and simulated biological neural networks.

Dynamics of multiple interacting excitatory and inhibitory populations with delays.

A network consisting of excitatory and inhibitory (EI) neurons is a canonical model for understanding local cortical network activity. In this study, we extended the local circuit model and investigated how its dynamical landscape can be enriched when it interacts with another excitatory (E) population with long transmission delays. Through analysis of a rate model and numerical simulations of a corresponding network of spiking neurons, we studied the transition from stationary to oscillatory states by analyzing the Hopf bifurcation structure in terms of two network parameters: (1) transmission delay between the EI subnetwork and the E population and (2) inhibitory couplings that induced oscillatory activity in the EI subnetwork. We found that the critical coupling strength can strongly modulate as a function of transmission delay, and consequently the stationary state can be interwoven intricately with the oscillatory state. Such a dynamical landscape gave rise to an isolated stationary state surrounded by multiple oscillatory states that generated different frequency modes, and cross-frequency coupling developed naturally at the bifurcation points. We identified the network motifs with short- and long-range inhibitory connections that underlie the emergence of oscillatory states with multiple frequencies. Thus, we provided a mechanistic explanation of how the transmission delay to and from the additional E population altered the dynamical landscape. In summary, our results demonstrated the potential role of long-range connections in shaping the network activity of local cortical circuits.

NanoPt-A Nanostructured Electrode Coating for Neural Recording and Microstimulation.

Bioelectronic devices, interfacing neural tissue for therapeutic, diagnostic, or rehabilitation purposes, rely on small electrode contacts in order to achieve highly sophisticated communication at the neural interface. Reliable recording and safe stimulation with small electrodes, however, are limited when conventional electrode metallizations are used, demanding the development of new materials to enable future progress within bioelectronics. In this study, we present a versatile process for the realization of nanostructured platinum (nanoPt) coatings with a high electrochemically active surface area, showing promising biocompatibility and providing low impedance, high charge injection capacity, and outstanding long-term stability both for recording and stimulation. The proposed electrochemical fabrication process offers exceptional control over the nanoPt deposition, allowing the realization of specific coating morphologies such as small grains, pyramids, or nanoflakes, and can moreover be scaled up to wafer level or batch fabrication under economic process conditions. The suitability of nanoPt as a coating for neural interfaces is here demonstrated, in vitro and in vivo, revealing superior stimulation performance under chronic conditions. Thus, nanoPt offers promising qualities as an advanced neural interface coating which moreover extends to the numerous application fields where a large (electro)chemically active surface area contributes to increased efficiency.

Hippocampal low-frequency stimulation prevents seizure generation in a mouse model of mesial temporal lobe epilepsy.

Mesial temporal lobe epilepsy (MTLE) is the most common form of focal, pharmacoresistant epilepsy in adults and is often associated with hippocampal sclerosis. Here, we established the efficacy of optogenetic and electrical low-frequency stimulation (LFS) in interfering with seizure generation in a mouse model of MTLE. Specifically, we applied LFS in the sclerotic hippocampus to study the effects on spontaneous subclinical and evoked generalized seizures. We found that stimulation at 1 Hz for 1 hr resulted in an almost complete suppression of spontaneous seizures in both hippocampi. This seizure-suppressive action during daily stimulation remained stable over several weeks. Furthermore, LFS for 30 min before a pro-convulsive stimulus successfully prevented seizure generalization. Finally, acute slice experiments revealed a reduced efficacy of perforant path transmission onto granule cells upon LFS. Taken together, our results suggest that hippocampal LFS constitutes a promising approach for seizure control in MTLE.

Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth.

The spatial distribution of neurons and activity-dependent neurite outgrowth shape long-range interaction, recurrent local connectivity and the modularity in neuronal networks. We investigated how this mesoscale architecture develops by interaction of neurite outgrowth, cell migration and activity in cultured networks of rat cortical neurons and show that simple rules can explain variations of network modularity. In contrast to theoretical studies on activity-dependent outgrowth but consistent with predictions for modular networks, spontaneous activity and the rate of synchronized bursts increased with clustering, whereas peak firing rates in bursts increased in highly interconnected homogeneous networks. As Ca2+ influx increased exponentially with increasing network recruitment during bursts, its modulation was highly correlated to peak firing rates. During network maturation, long-term estimates of Ca2+ influx showed convergence, even for highly different mesoscale architectures, neurite extent, connectivity, modularity and average activity levels, indicating homeostatic regulation towards a common set-point of Ca2+ influx.

Inhomogeneities in Network Structure and Excitability Govern Initiation and Propagation of Spontaneous Burst Activity.

The mesoscale architecture of neuronal networks strongly influences the initiation of spontaneous activity and its pathways of propagation. Spontaneous activity has been studied extensively in networks of cultured cortical neurons that generate complex yet reproducible patterns of synchronous bursting events that resemble the activity dynamics in developing neuronal networks in vivo. Synchronous bursts are mostly thought to be triggered at burst initiation sites due to build-up of noise or by highly active neurons, or to reflect reverberating activity that circulates within larger networks, although neither of these has been observed directly. Inferring such collective dynamics in neuronal populations from electrophysiological recordings crucially depends on the spatial resolution and sampling ratio relative to the size of the networks assessed. Using large-scale microelectrode arrays with 1024 electrodes at 0.3 mm pitch that covered the full extent of in vitro networks on about 1 cm2, we investigated where bursts of spontaneous activity arise and how their propagation patterns relate to the regions of origin, the network's structure, and to the overall distribution of activity. A set of alternating burst initiation zones (BIZ) dominated the initiation of distinct bursting events and triggered specific propagation patterns. Moreover, BIZs were typically located in areas with moderate activity levels, i.e., at transitions between hot and cold spots. The activity-dependent alternation between these zones suggests that the local networks forming the dominating BIZ enter a transient depressed state after several cycles (similar to Eytan et al., 2003), allowing other BIZs to take over temporarily. We propose that inhomogeneities in the network structure define such BIZs and that the depletion of local synaptic resources limit repetitive burst initiation.

Bursts with High and Low Load of Epileptiform Spikes Show Context-Dependent Correlations in Epileptic Mice.

Hypersynchronous network activity is the defining hallmark of epilepsy and manifests in a wide spectrum of phenomena, of which electrographic activity during seizures is only one extreme. The aim of this study was to differentiate between different types of epileptiform activity (EA) patterns and investigate their temporal succession and interactions. We analyzed local field potentials (LFPs) from freely behaving male mice that had received an intrahippocampal kainate injection to model mesial temporal lobe epilepsy (MTLE). Epileptiform spikes occurred in distinct bursts. Using machine learning, we derived a scale reflecting the spike load of bursts and three main burst categories that we labeled high-load, medium-load, and low-load bursts. We found that bursts of these categories were non-randomly distributed in time. High-load bursts formed clusters and were typically surrounded by transition phases with increased rates of medium-load and low-load bursts. In apparent contradiction to this, increased rates of low-load bursts were also associated with longer background phases, i.e., periods lacking high-load bursting. Furthermore, the rate of low-load bursts was more strongly correlated with the duration of background phases than the overall rate of epileptiform spikes. Our findings are consistent with the hypothesis that low-level EA could promote network stability but could also participate in transitions towards major epileptiform events, depending on the current state of the network.

FIND--a unified framework for neural data analysis.

The complexity of neurophysiology data has increased tremendously over the last years, especially due to the widespread availability of multi-channel recording techniques. With adequate computing power the current limit for computational neuroscience is the effort and time it takes for scientists to translate their ideas into working code. Advanced analysis methods are complex and often lack reproducibility on the basis of published descriptions. To overcome this limitation we develop FIND (Finding Information in Neural Data) as a platform-independent, open source framework for the analysis of neuronal activity data based on Matlab (Mathworks). Here, we outline the structure of the FIND framework and describe its functionality, our measures of quality control, and the policies for developers and users. Within FIND we have developed a unified data import from various proprietary formats, simplifying standardized interfacing with tools for analysis and simulation. The toolbox FIND covers a steadily increasing number of tools. These analysis tools address various types of neural activity data, including discrete series of spike events, continuous time series and imaging data. Additionally, the toolbox provides solutions for the simulation of parallel stochastic point processes to model multi-channel spiking activity. We illustrate two examples of complex analyses with FIND tools: First, we present a time-resolved characterization of the spiking irregularity in an in vivo extracellular recording from a mushroom-body extrinsic neuron in the honeybee during odor stimulation. Second, we describe layer specific input dynamics in the rat primary visual cortex in vivo in response to visual flash stimulation on the basis of multi-channel spiking activity.

Active Control of Dye Release for Neuronal Tracing Using PEDOT-PSS Coated Electrodes.

Penetrating neural probes comprising arrays of microelectrodes are commonly used to monitor local field potentials and multi-unit activity in animal brain over time frames of weeks. To correlate these recorded signals to specific tissue areas, histological analysis is performed after the experimental endpoint. Even if the lesion of the penetrating probe shaft can be observed, a precise reconstruction of the exact electrode positions is still challenging. To overcome these experimental difficulties, we developed a new concept, whereupon recording electrodes are coated with a poly (3, 4-ethylenedioxythiophene/ polystyrenesulfonate) (PEDOT/PSS)-based film. The conducting polymer acts as dye reservoir over several weeks and afterwards provides controlled delivery of neurotracers. This paper presents a recording electrode based on a PEDOT/PSS bilayer optimized for dye delivery and with reduced impedance. Controlled exchange of neurotracer dye is successfully demonstrated in vitro using spectrofluorometry and in neuroblastoma cell cultures. A second PEDOT/PSS capping layer on top of the dye reservoir lowers the passive leakage of dye by a factor of 6.4 and prevents a direct contact of the dye filled layer with the cells. Stability tests over four weeks demonstrate the electrochemical stability of the PEDOT coating, as well as retained functionality of the dye delivery system.