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Neural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

Auditory evoked fields (AEFs) are commonly studied, yet their underlying neural mechanisms remain poorly understood. Here, we used the biophysical modelling software Human Neocortical Neurosolver (HNN) whose foundation is a canonical neocortical circuit model to interpret the cell and network mechanisms contributing to macroscale AEFs elicited by a simple tone, measured with magnetoencephalography. We found that AEFs can be reproduced by activating the neocortical circuit through a layer specific sequence of feedforward and feedback excitatory synaptic drives, similar to prior simulation of somatosensory evoked responses, supporting the notion that basic structures and activation patterns are preserved across sensory regions. We also applied the modeling framework to develop and test predictions on neural mechanisms underlying AEF differences in the left and right hemispheres, as well as in hemispheres contralateral and ipsilateral to the presentation of the auditory stimulus. We found that increasing the strength of the excitatory synaptic cortical feedback inputs to supragranular layers simulates the commonly observed right hemisphere dominance, while decreasing the input latencies and simultaneously increasing the number of cells contributing to the signal accounted for the contralateral dominance. These results provide a direct link between human data and prior animal studies and lay the foundation for future translational research examining the mechanisms underlying alteration in this fundamental biomarker of auditory processing in healthy cognition and neuropathology.

Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called “electroencephalography (EEG) source connectivity” has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible \"ground truth\" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the “EEG source connectivity” pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.

Purpose The COVID-19 pandemic creates inefficiencies in the health-care system by having devastating consequences. It has demonstrated how inefficiencies in the health system can have a significant impact on social cohesion, economic growth and public confidence in government. The main purpose of this study is to explore the contemporary challenges faced by the pharmaceutical industry in Europe. Design/methodology/approach This study used a systematic literature review method and adopted inclusion and exclusion criteria after constructive reviews of articles from Web of Science and Scopus databases along with the ranked journals in the Chartered Association of Business Schools to search the following key terms “challenges in the European pharmaceutical industry” during the period from 2011 to 2022. The terms are set to be searched in the publications’ titles, abstracts and keywords. Findings This study reviewed 57 papers, and the systematic review revealed the vulnerability of the European pharmaceutical industry, such as the default patent system, ineffective research and development, debate on the role of alliances, low level of expertise in the European health-care system, pharmaceutical supply chain management and other issues. Research limitations/implications This study suggests that future research may explore the challenges of multisectoral and cross-country perspectives to get a better understanding, and for the long-term sustainability of public pharmaceutical spending, new models of enhancing research investments are needed, and Europe can still play a leading role in its tradition structure within capturing innovative ideas. Practical implications It provides new useful insights to policymakers, global leaders and managers to devise policies to achieve a performance-oriented culture in their institutions and firms. Social implications The pharmaceutical sector has recognized the influence of social determinants of health. It moves toward sustained sound health of people to have a flourishing pharmaceutical sector. Originality/value There is an insufficient study on the contemporary challenges of the European pharmaceutical industry. This study presents the argument that earlier studies ignored the contemporary issues facing the European pharmaceutical industry from a comprehensive and wider angle. In addition, the COVID-19 pandemic is a recent occurrence, and it causes inefficiency in the health-care sector, where the pharmaceutical industry plays a crucial role; importantly, this topic is emerging and underresearched in the existing literature. There is also a lack of systematic literature review studies in this field.

Substitution of the four paraphenyl hydrogens of iron tetraphenylporphyrin by trimethylammonio groups provides a water-soluble molecule able to catalyze the electrochemical conversion of carbon dioxide into carbon monoxide. The reaction, performed in pH-neutral water, forms quasi-exclusively carbon monoxide with very little production of hydrogen, despite partial equilibration of CO ₂ with carbonic acid—a low p K ₐ acid. This selective molecular catalyst is endowed with a good stability and a high turnover frequency. On this basis, prescribed composition of CO–H ₂ mixtures can be obtained by adjusting the pH of the solution, optionally adding an electroinactive buffer. The development of these strategies will be greatly facilitated by the fact that one operates in water. The same applies for the association of the cathode compartment with a proton-producing anode by means of a suitable separator. Significance CO ₂-to-CO electrochemical conversion is a key step in the production of liquid fuels through dihydrogen-reductive Fischer–Tropsch chemistry. Among molecular catalysts, iron porphyrins reduced electrochemically to the Fe(0) state are particularly efficient and led to a deeper understanding of mechanisms involving coupled bond-breaking proton–electron transfer processes. The replacement of nonaqueous solvents by water should make the CO ₂-to-CO half-cell reaction much more attractive for applications, particularly because it would allow association with a water-oxidation anode through a proton-exchange membrane. Here it is demonstrated that electrochemical CO production catalyzed by a water-soluble iron porphyrin can occur with high catalytic efficiency. Manipulation of pH and buffering then allows conversions from those involving complete CO selectivity to ones with prescribed CO–H ₂ mixtures.

We investigate both experimentally and using a computational model how the power of the electroencephalogram (EEG) recorded in human subjects tracks the presentation of sounds with acoustic intensities that increase exponentially (looming) or remain constant (flat). We focus on the link between this EEG tracking response, behavioral reaction times and the time scale of fluctuations in the resting state, which show considerable inter-subject variability. Looming sounds are shown to generally elicit a sustained power increase in the alpha and beta frequency bands. In contrast, flat sounds only elicit a transient upsurge at frequencies ranging from 7 to 45 Hz. Likewise, reaction times (RTs) in an audio-tactile task at different latencies from sound onset also present significant differences between sound types. RTs decrease with increasing looming intensities, i.e. as the sense of urgency increases, but remain constant with stationary flat intensities. We define the reaction time variation or “gain” during looming sound presentation, and show that higher RT gains are associated with stronger correlations between EEG power responses and sound intensity. Higher RT gain further entails higher relative power differences between loom and flat in the alpha and beta bands. The full-width-at-half-maximum of the autocorrelation function of the eyes-closed resting state EEG also increases with RT gain. The effects are topographically located over the central and frontal electrodes. A computational model reveals that the increase in stimulus–response correlation in subjects with slower resting state fluctuations is expected when EEG power fluctuations at each electrode and in a given band are viewed as simple coupled low-pass filtered noise processes jointly driven by the sound intensity. The model assumes that the strength of stimulus-power coupling is proportional to RT gain in different coupling scenarios, suggesting a mechanism by which slower resting state fluctuations enhance EEG response and shorten reaction times.

Arousal results in widespread activation of brain areas to increase their response in task and behavior relevant ways. Mediated by the Ascending Reticular Arousal System (ARAS), arousal-dependent inputs interact with neural circuitry to shape their dynamics. In the occipital cortex, such inputs may trigger shifts between dominant oscillations, where α activity is replaced by γ activity, or vice versa. A salient example of this are spectral power alternations observed while eyes are opened and/or closed. These transitions closely follow fluctuations in arousal, suggesting a common origin. To better understand the mechanisms at play, we developed and analyzed a computational model composed of two modules: a thalamocortical feedback circuit coupled with a superficial cortical network. Upon activation by noise-like inputs originating from the ARAS, our model is able to demonstrate that noise-driven non-linear interactions mediate transitions in dominant peak frequency, resulting in the simultaneous suppression of α limit cycle activity and the emergence of γ oscillations through coherence resonance. Reduction in input provoked the reverse effect - leading to anticorrelated transitions between α and γ power. Taken together, these results shed a new light on how arousal shapes oscillatory brain activity.

Significance Conversion of CO ₂ into liquid fuels is one of the most important contemporary energy and environmental challenges. As a first step in this direction, the electrochemical reduction of CO ₂ to CO requires catalysts, usually derived from transition metal complexes. In this paper, a very efficient, electrogenerated iron-porphyrin catalyst was obtained by introducing both pendant acid groups and fluorine substituents in the molecule. The former stabilize the CO ₂-catalyst key intermediate via H bonding and provide a high local proton concentration. The latter help decrease the energy required to drive catalysis. Benchmarking this molecule with other catalysts shows that it is at present the most efficient, to the best of our knowledge, homogeneous molecular catalyst of the CO ₂-to-CO conversion in terms of selectivity, overpotential, and turnover frequency. A very efficient electrogenerated Fe ⁰ porphyrin catalyst was obtained by substituting in tetraphenylporphyrin two of the opposite phenyl rings by ortho- , ortho '-phenol groups while the other two are perfluorinated. It proves to be an excellent catalyst of the CO ₂-to-CO conversion as to selectivity (the CO faradaic yield is nearly quantitative), overpotential, and turnover frequency. Benchmarking with other catalysts, through catalytic Tafel plots, shows that it is the most efficient, to the best of our knowledge, homogeneous molecular catalyst of the CO ₂-to-CO conversion at present. Comparison with another Fe ⁰ tetraphenylporphyrin bearing eight ortho- , ortho '-phenol functionalities launches a general strategy where changes in substituents will be designed so as to optimize the operational combination of all catalyst elements of merit.