Position- and Time-Dependent Arc Expression Links Neuronal Activity to Synaptic Plasticity During Epileptogenesis

In mesial temporal lobe epilepsy (mTLE) an initial precipitating injury can trigger aberrant wiring of neuronal circuits causing seizure activity. While circuit reorganization is known to be largely activity-dependent, the interactions between neuronal activity and synaptic plasticity during the development of mTLE remain poorly understood. Therefore, the present study aimed at delineating the spatiotemporal relationship between epileptic activity, activity-dependent gene expression and synaptic plasticity during kainic acid-induced epileptogenesis in mice. We show that during epileptogenesis the sclerotic hippocampus differed from non-sclerotic regions by displaying a consistently lower power of paroxysmal discharges. However, the power of these discharges steadily increased during epileptogenesis. This increase was paralleled by the upregulation of the activity-related cytoskeleton protein (Arc) gene expression in dentate granule cells (DGCs) of the sclerotic hippocampus. Importantly, we found that Arc mRNA-upregulating DGCs exhibited increased spine densities and spine sizes, but at the same time decreased AMPA-type glutamate receptor (AMPAR) densities. Finally, we show that in vivo optogenetic stimulation of DGC synapses evoked robust seizure activity in epileptic mice, but failed to induce dendritic translocation of Arc mRNA as under healthy conditions, supporting the theory of a breakdown of the dentate gate in mTLE. We conclude that during epileptogenesis epileptic activity emerges early and persists in the whole hippocampus, however, only the sclerotic part shows modulation of discharge amplitudes accompanied by plasticity of DGCs. In this context, we identified Arc as a putative mediator between seizure activity and synaptic plasticity

Early tissue damage and microstructural reorganization predict disease severity in experimental epilepsy

Mesial temporal lobe epilepsy (mTLE) is the most common focal epilepsy in adults and is often refractory to medication. So far, resection of the epileptogenic focus represents the only curative therapy. It is unknown whether pathological processes preceding epilepsy onset are indicators of later disease severity. Using longitudinal multi-modal MRI, we monitored hippocampal injury and tissue reorganization during epileptogenesis in a mouse mTLE model. The prognostic value of MRI biomarkers was assessed by retrospective correlations with pathological hallmarks Here, we show for the first time that the extent of early hippocampal neurodegeneration and progressive microstructural changes in the dentate gyrus translate to the severity of hippocampal sclerosis and seizure burden in chronic epilepsy. Moreover, we demonstrate that structural MRI biomarkers reflect the extent of sclerosis in human hippocampi. Our findings may allow an early prognosis of disease severity in mTLE before its first clinical manifestations, thus expanding the therapeutic window

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

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

Extending stability through hierarchical clusters in Echo State Networks

Echo State Networks (ESN) are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantitatively determine the influence of the most relevant network parameters, we analysed the impact of reservoir substructures on stability in hierarchically clustered ESNs (HESN), as they allow a smooth transition from highly structured to increasingly homogeneous reservoirs. Previous studies used the largest eigenvalue of the reservoir connectivity matrix (spectral radius) as a predictor for stable network dynamics. Here, we evaluate the impact of clusters, hierarchy and intercluster connectivity on the predictive power of the spectral radius for stability. Both hierarchy and low relative cluster sizes extend the range of spectral radius values, leading to stable networks, while increasing intercluster connectivity decreased maximal spectral radius