Measuring activity of the subthalamic nucleus in acute slices using multi electrode arrays

The symptoms of Parkinson’s disease (a.o.: tremor, rigidity) can be suppressed by electrical stimulation of the basal ganglia. The most common target nucleus of this so called Deep Brain Stimulation (DBS) is the subthalamic nucleus (STN). Good clinical results are obtained by the application of pulses of 200 s, 1-3 V amplitude at a constant rate of about 130 Hz. However, the mechanism(s) responsible for the clinical improvements are not yet elucidated.\ud The use of acute brain slices as a model is widely used, despite the inevitable loss of many connections. Accurate (i.e. subthreshold) measurements of single neuron and multiple neuron (up to ~3, for practical reasons) membrane potentials are obtained by patch-clamp technique. We propose to use arrays of microelectrodes in slice recordings of STN. We present here our first results

Experimental analysis and computational modeling of interburst intervals in spontaneous activity of cortical neuronal culture

Rhythmic bursting is the most striking behavior of cultured cortical networks and may start in the second week after plating. In this study, we focus on the intervals between spontaneously occurring bursts, and compare experimentally recorded values with model simulations. In the models, we use standard neurons and synapses, with physiologically plausible parameters taken from literature. All networks had a random recurrent architecture with sparsely connected neurons. The number of neurons varied between 500 and 5,000. We find that network models with homogeneous synaptic strengths produce asynchronous spiking or stable regular bursts. The latter, however, are in a range not seen in recordings. By increasing the synaptic strength in a (randomly chosen) subset of neurons, our simulations show interburst intervals (IBIs) that agree better with in vitro experiments. In this regime, called weakly synchronized, the models produce irregular network bursts, which are initiated by neurons with relatively stronger synapses. In some noise-driven networks, a subthreshold, deterministic, input is applied to neurons with strong synapses, to mimic pacemaker network drive. We show that models with such “intrinsically active neurons” (pacemaker-driven models) tend to generate IBIs that are determined by the frequency of the fastest pacemaker and do not resemble experimental data. Alternatively, noise-driven models yield realistic IBIs. Generally, we found that large-scale noise-driven neuronal network models required synaptic strengths with a bimodal distribution to reproduce the experimentally observed IBI range. Our results imply that the results obtained from small network models cannot simply be extrapolated to models of more realistic size. Synaptic strengths in large-scale neuronal network simulations need readjustment to a bimodal distribution, whereas small networks do not require such change

President Stegenga Letter, 1957

A letter from President Preston J. Stegenga to Theora England regarding Delta Psi Omega, thanking her for her leadership.https://nwcommons.nwciowa.edu/theoracorrespondence/1002/thumbnail.jp

Oscillations in subthalamic nucleus measured by multi electrode arrays

The subthalamic nucleus (STN) of the basal ganglia, is involved in the generation of Parkinsonian symptoms and forms one of the main targets for Deep Brain Stimulation (DBS). Effective frequencies of DBS are around 130 Hz. The effect of such stimuli in the STN is largely unknown but has been hypothesized to result in neuronal block, interrupting the pathophysiological oscillatory behavior which is observed in the Parkinsionian basal ganglia. Modelling studies suggest that synchronized oscillation at tremor (4-8 Hz) or beta (14-30 Hz) frequencies may occur. To study synchronicity of the STN in detail, we record action-potential activity from rat brain slices using multi electrode arrays (MEAs). These arrays consist of 60 recording sites and thus allow the study of spatio-temporal activity patterns. Here we show the characteristics of spike trains which we recorded in the STN

Intra-burst firing characteristics as network state parameters

Introduction \ud In our group we are aiming to demonstrate learning and memory capabilities of cultured networks of cortical neurons. A first step is to identify parameters that accurately describe changes in the network due to learning. Usually, such parameters are calculated from the responses to test-stimuli before and after a learning experiment. We propose that parameters should be calculated from the spontaneous activity before and after a learning experiment, as the applying of test-stimuli itself may alter the network. Since bursting is dominant in our cultures, we have investigated its spatio-temporal structure. \ud \ud Methods \ud Networks of cortical neurons were cultured on a MEA. Over a period from 9 to 35 DIV, the spontaneous activity has been measured on a regular basis. Measurements on a single day are always continuous; otherwise cultures are kept in a stove under controlled conditions (37 ˚C, 5% CO2, 100% humidity). Network bursts were detected by analysing the Array-Wide Spiking Rate (AWSR, the sum of activity over all electrodes). Next, we estimated the instantaneous AWSR during a burst by convolving spike-occurrences with a Gaussian function. We investigated the changes in burst profiles over time by aligning them to their peak AWSR. In 4 hour recording sessions, we grouped the burst profiles over 1 hour, resulting in 4 average burst profiles per day. In addition, a sufficient amount of aligned bursts yielded enough data to calculate the contribution of each recording site. \ud \ud Results \ud The burst profiles, calculated over a period of 1 hour, generally show little variation (figure 1). In subsequent hours, the profiles gradually change shape. Over a period of days however, the shape can change dramatically (figure 2). The relatively slow changes over the period of hours indicate an underlying probabilistic structure in the AWSR during bursts. The apparent structure in the burst profiles result from the relationships between individual recording sites, and thus also on the connectivity in the neural network. This is revealed in more detail by showing the contributions of individual sites (figure 3). The spike envelopes have a shape that is too detailed to be described accurately by a small set of parameters. \ud \ud Discussion \ud The burst profiles prove to be stable over a period of one hour, and gradually change their shape over several hours, as has also been suggested in [1]. The day-to-day changes in burst profiles may be the result of these gradual changes, thereby suggesting an intrinsically changing network. However, they can also be the result of putting the cultures back in the stove. The spike envelopes per recording site offer more detailed descriptions of the network state than the burst profiles. This may however be the amount of detail required to reveal the changes made during learning experiments. A subsequent refinement can be made by identifying distinct subgroups of bursts, as has been suggested in [2]

Cultured cortical networks described by conditional firing probabilities

Networks of cortical neurons were grown over multi electrode arrays to enable simultaneous measu-rement of action potentials from 60 electrodes. All possible pairs of electrodes (i,j) were tested for syn-chronized activity. We calculated conditional firing probability (CFPi,j[τ]) as the probability of an action potential at electrode j at t=τ, given that a spike was detected at i at t=0. If a CFPi,j[τ] distribution clearly deviated from flat, electrodes i and j were considered related. A function was fitted to each CFP-curve to obtain parameters for strength and delay. In young cultures the set of identified relationships changed rather quickly. At 16 days in vitro (DIV) 50% of the set changed within one day. Beyond 25 DIV this set stabilized: during a period of a week more than 50% of the set remained intact. Most individual relationships developed rather gradually. Moreover, beyond 25 DIV relational strength appeared quite stable during periods of ≈ 10 hours, with coefficients of variation (100×SD/mean) of ≈ 25% on average. CFP analysis provides a robust method to describe the stable underlying probabilistic structure of highly varying spontaneous activity in cultured cortical networks. It may offer a suitable basis for plasticity studies, in which induced changes should exceed spontaneous fluctuations. CFP analysis is likely to describe the network in sufficient detail to detect subtle changes in individual relationships. Analysis of data continuously recorded for ≈ 6 weeks, showed that highest stability is reached after ≈ 25 DIV, suggesting the 4th and 5th week as a suitable period for plasticity studies.\ud \u

Ultrasound stimulation of mandibular bone defect healing

The conclusions of the experimental work presented in this thesis are: 1. Low intensity pulsed ultrasound is not effective in stimulating bone growth into a rat mandibular defect, either with or without the use of osteoconductive membranes. 2. Low intensity pulsed ultrasound does not seem to have an effect on the early bone formation in the vertically distracted, severely resorbed mandible. This thesis focused on a small area in the field of ultrasound and bone healing that had not been explored before. The animal experimental work indicates that ultrasound does not stimulate mandibular bone defect healing with or without the use of osteoconductive membranes in healthy animals. This may be related to the ultrasound field variables used, to an optimal healing tendency of the head and neck region, or to limitations of the animal model. To reveal which of these possibilities is the most plausible, additional research is needed. For now, it is not recommendable to apply ultrasound in maxillofacial surgery to stimulate bone defect healing. In situations where mechanical loading or blood perfusion is limited, as for example in the case of mandibular fractures or osteoradionecrosis, ultrasound might have an effect. More importantly, unravelling the mechanism of action as to how ultrasound stimulates bone healing in certain cases may eventually predict if, and if so, when, ultrasound may be of value in maxillofacial surgery.