An Adaptive Generalized Leaky Integrate-and-Fire Model for Hippocampal CA1 Pyramidal Neurons and Interneurons.

Full-scale morphologically and biophysically realistic model networks, aiming at modeling multiple brain areas, provide an invaluable tool to make significant scientific advances from in-silico experiments on cognitive functions to digital twin implementations. Due to the current technical limitations of supercomputer systems in terms of computational power and memory requirements, these networks must be implemented using (at least) simplified neurons. A class of models which achieve a reasonable compromise between accuracy and computational efficiency is given by generalized leaky integrate-and fire models complemented by suitable initial and update conditions. However, we found that these models cannot reproduce the complex and highly variable firing dynamics exhibited by neurons in several brain regions, such as the hippocampus. In this work, we propose an adaptive generalized leaky integrate-and-fire model for hippocampal CA1 neurons and interneurons, in which the nonlinear nature of the firing dynamics is successfully reproduced by linear ordinary differential equations equipped with nonlinear and more realistic initial and update conditions after each spike event, which strictly depends on the external stimulation current. A mathematical analysis of the equilibria stability as well as the monotonicity properties of the analytical solution for the membrane potential allowed (i) to determine general constraints on model parameters, reducing the computational cost of an optimization procedure based on spike times in response to a set of constant currents injections; (ii) to identify additional constraints to quantitatively reproduce and predict the experimental traces from 85 neurons and interneurons in response to any stimulation protocol using constant and piecewise constant current injections. Finally, this approach allows to easily implement a procedure to create infinite copies of neurons with mathematically controlled firing properties, statistically indistinguishable from experiments, to better reproduce the full range and variability of the firing scenarios observed in a real network.

On the structural connectivity of large-scale models of brain networks at cellular level.

The brain's structural connectivity plays a fundamental role in determining how neuron networks generate, process, and transfer information within and between brain regions. The underlying mechanisms are extremely difficult to study experimentally and, in many cases, large-scale model networks are of great help. However, the implementation of these models relies on experimental findings that are often sparse and limited. Their predicting power ultimately depends on how closely a model's connectivity represents the real system. Here we argue that the data-driven probabilistic rules, widely used to build neuronal network models, may not be appropriate to represent the dynamics of the corresponding biological system. To solve this problem, we propose to use a new mathematical framework able to use sparse and limited experimental data to quantitatively reproduce the structural connectivity of biological brain networks at cellular level.

Preventable proportion of intubation-associated pneumonia: Role of adherence to a care bundle.

OBJECTIVE: The aim of the present study was to estimate the preventable proportion of Intubation-Associated Pneumonia (IAP) in the Intensive Care Units (ICUs) participating in the Italian Nosocomial Infections Surveillance in ICUs (SPIN-UTI) network, taking into account differences in intrinsic patients' risk factors, and additionally considering the compliance with the European bundle for IAP prevention. METHODS: A prospective patient-based survey was conducted and all patients staying in ICU for more than 2 days were enrolled in the surveillance. Compliance with the bundle was assessed using a questionnaire for each intubated patient. A twofold analysis by the parametric g-formula was used to compute the number of infections to be expected if the infection incidence in all ICUs could be reduced to that one of the top-tenth-percentile-ranked ICUs and to that one of the ICU with the highest compliance to all five bundle components. RESULTS: A total of 1,840 patients and of 17 ICUs were included in the first analysis showing a preventable proportion of 44% of IAP. In a second analysis on a subset of data, considering compliance with the European bundle, a preventable proportion of 40% of IAP was shown. A significant negative trend of IAP incidences was observed with increasing number of bundle components performed (p<0.001) and a strong negative correlation between these two factors was shown (r = -0.882; p = 0.048). CONCLUSIONS: The g-formula controlled for time-varying factors is a valuable approach for estimating the preventable proportion of IAP and the impact of interventions, based entirely on an observed population in a real-world setting. However, both the study design that cannot definitively prove a causative relationship between bundle compliance and IAP risk, and the small number of patients included in the care bundle compliance analysis, may represent limits of the study and further and larger studies should be conducted.

On phasic inhibition during hippocampal theta.

Two computational models are used to explore the possible implications of recent experimental data (Royer et al. 2012) on phasic inhibition during theta frequency (4-10 Hz) oscillations in the hippocampi of actively behaving rodents. A working hypothesis from previous experimental and modelling studies is that a theta cycle is divided into encoding (when synaptic plasticity is enhanced) and recall (when plasticity is suppressed) half cycles. Using a compartmental model of a CA1 pyramidal cell, including dendritic spines, we demonstrate that out-of-phase perisomatic and dendritic inhibition, respectively, can promote the necessary conditions for these half cycles. Perisomatic inhibition allows dendritic calcium spikes that promote synaptic LTP, while minimising cell output. Dendritic inhibition, on the other hand, both controls cell output and suppresses dendritic calcium spikes, preventing LTP. The exact phase relationship between these sub-cycles may not be fixed. Using a simple sum-of-sinusoids activity model, we suggest an interpretation of the data of Royer et al. (2012) in which a fixed-phase encoding sub-cycle is surrounded by a flexible-phase recall cycle that follows the peak of excitatory drive and consequent phase precession of activity as an animal passes through a pyramidal cell's place field.