The effect of bleach duration and age on the ERG photostress test

Background: The ERG photostress test assesses the recovery of the focal 41 Hz ERG following exposure to a bright light that bleaches a significant proportion of photopigment. The aims of this study were: 1) to compare the repeatability of the ERG photostress test recovery time constant following long and short duration light exposure, and 2) to determine the effect of age on the ERG photostress test recovery time constant. Methods: Focal 41 Hz ERGs were recorded from 23 participants (age range 20–71 years) at 20-second intervals for 5 minutes following either a short-duration (photoflash) or long-duration (equilibrium) light exposure. After a 5-minute wash-out period, the procedure was repeated using the second bleach modality. The time constant of cone recovery was determined by fitting an exponential model to the amplitude recovery data. The whole procedure was repeated on a second occasion. The co-efficient of repeatability (CoR) was calculated for each bleaching technique. The relationship between the time constant of recovery and age was investigated (Pearson’s correlation coefficient). Results: The time constant of recovery following an equilibrium bleach was more repeatable than recovery following a photoflash (CoR = 85s and 184s respectively). Eight trials (from seven participants) failed to show a reduction in amplitude following the photoflash, suggesting that a blink or fixation loss had occurred. All participants were reliably light-adapted by the equilibrium bleach. For the equilibrium bleach data, the time constant of recovery increased with age at a rate of 27 seconds per decade. Conclusions: The equilibrium bleach was more reliable and repeatable than the photoflash. Increasing participant age was shown to result in a lengthening of the recovery time constant, of a magnitude comparable to previously published psychophysical data

Simple, Fast and Accurate Implementation of the Diffusion Approximation Algorithm for Stochastic Ion Channels with Multiple States

The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise) with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC) seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA) methods use Stochastic Differential Equations (SDE) to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled activation subunits, while the DA was modeled using uncoupled activation subunits. Implementations of DA with coupled subunits, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable - allowing an easy and efficient DA implementation. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods.Comment: 32 text pages, 10 figures, 1 supplementary text + figur

Antiepileptic drugs’ tolerability and safety – a systematic review and meta-analysis of adverse effects in dogs

<p>Various anti-epileptic drugs (AEDs) are used for the management of idiopathic epilepsy (IE) in dogs. Their safety profile is an important consideration for regulatory bodies, owners and prescribing clinicians. However, information on their adverse effects still remains limited with most of it derived from non-blinded non-randomized uncontrolled trials and case reports.</p><p><span>This poster won third place, which was presented at the Veterinary Evidence Today conference, Edinburgh November 1-3, 2016. </span></p><br /> <img src="https://www.veterinaryevidence.org/rcvskmod/icons/oa-icon.jpg" alt="Open Access" /

Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation

Differential effects of tactile high- and low-frequency stimulation on tactile discrimination in human subjects

<p>Abstract</p> <p>Background</p> <p>Long-term potentiation (LTP) and long-term depression (LTD) play important roles in mediating activity-dependent changes in synaptic transmission and are believed to be crucial mechanisms underlying learning and cortical plasticity. In human subjects, however, the lack of adequate input stimuli for the induction of LTP and LTD makes it difficult to study directly the impact of such protocols on behavior.</p> <p>Results</p> <p>Using tactile high- and low-frequency stimulation protocols in humans, we explored the potential of such protocols for the induction of perceptual changes. We delivered tactile high-frequency and low-frequency stimuli (t-HFS, t-LFS) to skin sites of approximately 50 mm²on the tip of the index finger. As assessed by 2-point discrimination, we demonstrate that 20 minutes of t-HFS improved tactile discrimination, while t-LFS impaired performance. T-HFS-effects were stable for at least 24 hours whereas t-LFS-induced changes recovered faster. While t-HFS changes were spatially very specific with no changes on the neighboring fingers, impaired tactile performance after t-LFS was also observed on the right middle-finger. A central finding was that for both t-LFS and t-HFS perceptual changes were dependent on the size of the stimulated skin area. No changes were observed when the stimulated area was very small (< 1 mm²) indicating special requirements for spatial summation.</p> <p>Conclusion</p> <p>Our results demonstrate differential effects of such protocols in a frequency specific manner that might be related to LTP- and LTD-like changes in human subjects.</p

Frustrated hierarchical synchronization and emergent complexity in the human connectome network

The spontaneous emergence of coherent behavior through synchronization plays a key role in neural function, and its anomalies often lie at the basis of pathologies. Here we employ a parsimonious (mesoscopic) approach to study analytically and computationally the synchronization (Kuramoto) dynamics on the actual human-brain connectome network. We elucidate the existence of a so-far-uncovered intermediate phase, placed between the standard synchronous and asynchronous phases, i.e. between order and disorder. This novel phase stems from the hierarchical modular organization of the connectome. Where one would expect a hierarchical synchronization process, we show that the interplay between structural bottlenecks and quenched intrinsic frequency heterogeneities at many different scales, gives rise to frustrated synchronization, metastability, and chimera-like states, resulting in a very rich and complex phenomenology. We uncover the origin of the dynamic freezing behind these features by using spectral graph theory and discuss how the emerging complex synchronization patterns relate to the need for the brain to access –in a robust though flexible way– a large variety of functional attractors and dynamical repertoires without ad hoc fine-tuning to a critical pointWe acknowledge financial support from J. de Andalucía, grant P09-FQM-4682 and we thank O. Sporns for providing us access to the human connectome data

Structure of Spontaneous UP and DOWN Transitions Self-Organizing in a Cortical Network Model

Synaptic plasticity is considered to play a crucial role in the experience-dependent self-organization of local cortical networks. In the absence of sensory stimuli, cerebral cortex exhibits spontaneous membrane potential transitions between an UP and a DOWN state. To reveal how cortical networks develop spontaneous activity, or conversely, how spontaneous activity structures cortical networks, we analyze the self-organization of a recurrent network model of excitatory and inhibitory neurons, which is realistic enough to replicate UP–DOWN states, with spike-timing-dependent plasticity (STDP). The individual neurons in the self-organized network exhibit a variety of temporal patterns in the two-state transitions. In addition, the model develops a feed-forward network-like structure that produces a diverse repertoire of precise sequences of the UP state. Our model shows that the self-organized activity well resembles the spontaneous activity of cortical networks if STDP is accompanied by the pruning of weak synapses. These results suggest that the two-state membrane potential transitions play an active role in structuring local cortical circuits

Accurate and Fast Simulation of Channel Noise in Conductance-Based Model Neurons by Diffusion Approximation

Stochastic channel gating is the major source of intrinsic neuronal noise whose functional consequences at the microcircuit- and network-levels have been only partly explored. A systematic study of this channel noise in large ensembles of biophysically detailed model neurons calls for the availability of fast numerical methods. In fact, exact techniques employ the microscopic simulation of the random opening and closing of individual ion channels, usually based on Markov models, whose computational loads are prohibitive for next generation massive computer models of the brain. In this work, we operatively define a procedure for translating any Markov model describing voltage- or ligand-gated membrane ion-conductances into an effective stochastic version, whose computer simulation is efficient, without compromising accuracy. Our approximation is based on an improved Langevin-like approach, which employs stochastic differential equations and no Montecarlo methods. As opposed to an earlier proposal recently debated in the literature, our approximation reproduces accurately the statistical properties of the exact microscopic simulations, under a variety of conditions, from spontaneous to evoked response features. In addition, our method is not restricted to the Hodgkin-Huxley sodium and potassium currents and is general for a variety of voltage- and ligand-gated ion currents. As a by-product, the analysis of the properties emerging in exact Markov schemes by standard probability calculus enables us for the first time to analytically identify the sources of inaccuracy of the previous proposal, while providing solid ground for its modification and improvement we present here