Enduring Medial Perforant Path Short-Term Synaptic Depression at High Pressure

The high pressure neurological syndrome develops during deep-diving (>1.1 MPa) involving impairment of cognitive functions, alteration of synaptic transmission and increased excitability in cortico-hippocampal areas. The medial perforant path (MPP), connecting entorhinal cortex with the hippocampal formation, displays synaptic frequency-dependent-depression (FDD) under normal conditions. Synaptic FDD is essential for specific functions of various neuronal networks. We used rat cortico-hippocampal slices and computer simulations for studying the effects of pressure and its interaction with extracellular Ca2+ ([Ca2+]o) on FDD at the MPP synapses. At atmospheric pressure, high [Ca2+]o (4–6 mM) saturated single MPP field EPSP (fEPSP) and increased FDD in response to short trains at 50 Hz. High pressure (HP; 10.1 MPa) depressed single fEPSPs by 50%. Increasing [Ca2+]o to 4 mM at HP saturated synaptic response at a subnormal level (only 20% recovery of single fEPSPs), but generated a FDD similar to atmospheric pressure. Mathematical model analysis of the fractions of synaptic resources used by each fEPSP during trains (normalized to their maximum) and the total fraction utilized within a train indicate that HP depresses synaptic activity also by reducing synaptic resources. This data suggest that MPP synapses may be modulated, in addition to depression of single events, by reduction of synaptic resources and then may have the ability to conserve their dynamic properties under different conditions

On the Firing Rate Dependency of the Phase Response Curve of Rat Purkinje Neurons In Vitro

Synchronous spiking during cerebellar tasks has been observed across Purkinje cells: however, little is known about the intrinsic cellular mechanisms responsible for its initiation, cessation and stability. The Phase Response Curve (PRC), a simple input-output characterization of single cells, can provide insights into individual and collective properties of neurons and networks, by quantifying the impact of an infinitesimal depolarizing current pulse on the time of occurrence of subsequent action potentials, while a neuron is firing tonically. Recently, the PRC theory applied to cerebellar Purkinje cells revealed that these behave as phase-independent integrators at low firing rates, and switch to a phase-dependent mode at high rates. Given the implications for computation and information processing in the cerebellum and the possible role of synchrony in the communication with its post-synaptic targets, we further explored the firing rate dependency of the PRC in Purkinje cells. We isolated key factors for the experimental estimation of the PRC and developed a closed-loop approach to reliably compute the PRC across diverse firing rates in the same cell. Our results show unambiguously that the PRC of individual Purkinje cells is firing rate dependent and that it smoothly transitions from phase independent integrator to a phase dependent mode. Using computational models we show that neither channel noise nor a realistic cell morphology are responsible for the rate dependent shift in the phase response curve

Highly Scalable Parallel Processing of Extracellular Recordings of Multielectrode Arrays

Technological advances of Multielectrode Arrays (MEAs) used for multi- site, parallel electrophysiological recordings, lead to an ever increasing amount of raw data being generated. Arrays with hundreds up to a few thousands of electrodes are slowly seeing widespread use and the expectation is that more sophisticated arrays will become available in the near future. In order to process the large data volumes resulting from MEA recordings there is a pressing need for new software tools able to process many data channels in parallel. Here we present a new tool for processing MEA data recordings that makes use of new programming paradigms and recent technology developments to unleash the power of modern highly parallel hardware, such as multi-core CPUs with vector instruction sets or GPGPUs. Our tool builds on and complements existing MEA data analysis packages. It shows high scalability and can be used to speed up some performance critical pre-processing steps such as data filtering and spike detection, helping to make the analysis of larger data sets tractable

Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems

Treatments for Ocular Diseases in Pregnancy and Breastfeeding: A Narrative Review.

Pregnancy is a medical condition in which the physiological changes in the maternal body and the potential impact on the developing fetus require a cautious approach in terms of drug administration. Individual treatment, a thorough assessment of the extent of the disease, and a broad knowledge of the therapeutic options and different routes of administration of ophthalmic drugs are essential to ensure the best possible results while minimizing risks. Although there are currently several routes of administration of drugs for the treatment of eye diseases, even with topical administration, there is a certain amount of systemic absorption that must be taken into account. Despite continuous developments and advances in ophthalmic drugs, no updated data are available on their safety profile in these contexts. The purpose of this review is both to summarize the current information on the safety of ophthalmic treatments during pregnancy and lactation and to provide a practical guide to the ophthalmologist for the treatment of eye diseases while minimizing harm to the developing fetus and addressing maternal health needs

Ruptured Brain Arteriovenous Malformations: Surgical Timing and Outcomes-A Retrospective Study of 25 Cases

Background One important problem in treatment of ruptured brain arteriovenous malformations (bAVMs) is surgical timing. The aim of the study was to understand which parameters affect surgical timing and outcomes the most. Materials and Methods Between January 2010 and December 2018, 25patients underwent surgery for a ruptured bAVM at our institute. Intracerebral hemorrhage (ICH) score was used to evaluate hemorrhage severity, while Spetzler-Martin scale for AVM architecture. We divided patients in two groups: early surgery and delayed surgery. The modified Rankin Scale (mRS) evaluated the outcomes. Results Eleven patients were in the early surgery group: age 38 ± 18 years, Glasgow Coma Scale (GCS) 7.64 ± 2.86, ICH score 2.82 ± 0.71, hematoma volume 45.55 ± 23.21 mL. Infratentorial origin of hemorrhage was found in 27.3% cases; AVM grades were I to II in 82%, III in 9%, and IV in 9% cases. Outcome at 3 months was favorable in 36.4% cases and in 54.5% after 1 year. Fourteen patients were in the delayed surgery group: age 41 ± 16 years, GCS 13.21 ± 2.39, ICH score 1.14 ± 0.81, hematoma volume 29.89 ± 21.33 mL. Infratentorial origin of hemorrhage was found in 14.2% cases; AVM grades were I to II in 50% and III in 50%. Outcome at 3 months was favorable in 78.6% cases and in 92.8% after 1 year. Conclusions The early outcome is influenced more by the ICH score, while the delayed outcome by Spetzler-Martin grading. These results suggest that it is better to perform surgery after a rest period, away from the hemorrhage when possible. Moreover, this study suggests how in young patient with a high ICH score and a low AVM grade, early surgery seems to be a valid and feasible therapeutic strategy

A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces

In this paper, the adoption of Machine Learning (ML) classifiers is addressed to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces (BCIs). The proposed BCI is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In this setup, Augmented Reality Smart Glasses are used to generate and display the flickering stimuli for the SSVEP elicitation. An experimental campaign was conducted on 20 adult volunteers. Successively, a Leave-One-Subject-Out Cross Validation was performed to validate the proposed algorithm. The obtained experimental results demonstrate that suitable ML-based processing strategies outperform the state-of-the-art techniques in terms of classification accuracy. Furthermore, it was also shown that the adoption of an inter-subjective model successfully led to a decrease in the 3-σ uncertainty: this can facilitate future developments of ready-to-use systems

P-012: Probiotic bacteria enhance antigen sampling and processing by dendritic cells in pediatric IBD

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