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Background: Electrical stimulation of brain structures has been widely used in rodent models for kindling or modeling deep brain stimulation used clinically. This requires surgical implantation of intracranial electrodes and subsequent chronic stimulation in individual animals for several weeks. Anchoring screws and dental acrylic have long been used to secure implanted intracranial electrodes in rats. However, such an approach is limited when carried out in mouse models as the thin mouse skull may not be strong enough to accommodate the anchoring screws. We describe here a screw-free, glue-based method for implanting bipolar stimulating electrodes in the mouse brain and validate this method in a mouse model of hippocampal electrical kindling. Methods: Male C57 black mice (initial ages of 6-8 months) were used in the present experiments. Bipolar electrodes were implanted bilaterally in the hippocampal CA3 area for electrical stimulation and electroencephalographic recordings. The electrodes were secured onto the skull via glue and dental acrylic but without anchoring screws. A daily stimulation protocol was used to induce electrographic discharges and motor seizures. The locations of implanted electrodes were verified by hippocampal electrographic activities and later histological assessments. Results: Using the glue-based implantation method, we implanted bilateral bipolar electrodes in 25 mice. Electrographic discharges and motor seizures were successfully induced via hippocampal electrical kindling. Importantly, no animal encountered infection in the implanted area or a loss of implanted electrodes after 4-6 months of repetitive stimulation/recording. Conclusion: We suggest that the glue-based, screw-free method is reliable for chronic brain stimulation and high-quality electroencephalographic recordings in mice. The technical aspects described this study may help future studies in mouse models. [ABSTRACT FROM AUTHOR]

Inhibitory, interneuronal networks are known to underlie high-frequency (gamma, 40-80 Hz) population oscillations, and they are also known to underlie low-frequency rhythms. For example, spontaneous, slow (0.5-4 Hz) rhythms occur in rodent hippocampus. However, it is unclear whether an inhibitory network can generate population oscillations much slower than the intrinsic firing frequencies of its constituent neurons. Here we show that an inhibitory network model in the absence of any slow processes is able to produce low-frequency rhythms. To obtain this, we bridge our network model simulations with a dynamical mean-field (DMA) model to approximate the location of relevant parameter regimes. The individual interneuron model is a two-dimensional conductance-based model and the network is formed with fast, inhibitory GABAA type synapses. The DMA model representing a large all-to-all coupled system consists of 30 equations that include equations describing synaptic noises. Bifurcation analysis is used to explore the DMA model, in particular, to identify parameter regimes for which bursting activities occur. These parameters are used in network simulations. The network model consists of an all-to-all coupled network of 20 interneurons. Each interneuron is described by: CdV/dt = Iapp + bη-gL(V-EL)- gNam(V-ENa)-gKn(V-EK)-gsyn(V-Esyn) ∑isi; m(V) = 1/(1+exp(- 4/3-V/15)); dn/dt = 1/(1+exp(-5-V/5))-n; dsi/dt=a(1-si)/ (1+exp(-Vi/2))-si/τ, where ∑isi sums the (inhibitory) synaptic gating variables from other interneurons in the network, η represents white noise of unit strength and b represents the strength of the noise. Figure 1 shows a raster plot from a 30 second network simulation (left) with the corresponding average summated synaptic activities (right). The parameters used are taken from identified bursting regimes in the DMA model analysis. Parameters values: Iapp= 4.8 µA/cm²; b= 0.08 ms1/2mA/cm²; gL= 8 mS/ cm²; gK= 10 mS/cm²; gNa= 20 mS/cm²; gsyn= 0.0263 mS/ cm²; EL= -80 mV; EK= -90 mV; ENa= 60 mV; Esyn= -85 mV; τ = 10 ms; C = 1 µF/cm². The intrinsic firing frequency at Iapp = 4.8 µA/cm² for these neurons (with zero noise) is 52 Hz. Slow population rhythms (approx 0.5 Hz), or bursts of synaptic activities, can be seen to emerge due to a "switching" between sparsely firing and coherently firing network states. A DMA model analysis has been used to find parameter regimes that allow slow rhythms to be expressed by inhibitory network models. These regimes are identified by bursting activities in a simpler mean-field model. Given the bridging used between the DMA model and the network simulations, we expect that this slow pattern should also occur in much larger network models. We have previously obtained values for synaptic "noise" parameters underlying slow hippocampal rhythms. It will be interesting to determine whether bursting in the DMA models, and thus slow population rhythms in large network simulations, occur using these experimentally-based synaptic noise parameter values. If so, this would suggest a novel way in which slow rhythms could emerge in biological, inhibitory networks. [ABSTRACT FROM AUTHOR]

The article summarizes the results of a study on the role of neural inhibition in shaping the in vitro spontaneous hippocampal network rhythms of rodents. It has found a link between the transition from the quiescent to the rhythmic state of spontaneous rhythmic field potentials (SRFPs) and inhibitory conductance dominance with inhibitory fluctuations of over 10%. It concludes that SRFPs are inhibitory-based rhythms. The quiescent state is also inihibitory dominant.