Plasticity in the adult language system: a longitudinal electrophysiological study on second language learning

Event-related potentials (ERPs) were used to trace changes in brain activity related to progress in second language learning. Twelve English-speaking exchange students learning German in Switzerland were recruited. ERPs to visually presented single words from the subjects' native language (English), second language (German) and an unknown language (Romansh) were measured before (day 1) and after (day 2) 5 months of intense German language learning. When comparing ERPs to German words from day 1 and day 2, we found topographic differences between 396 and 540 ms. These differences could be interpreted as a latency shift indicating faster processing of German words on day 2. Source analysis indicated that the topographic differences were accounted for by shorter activation of left inferior frontal gyrus (IFG) on day 2. In ERPs to English words, we found Global Field Power differences between 472 and 644 ms. This may due to memory traces related to English words being less easily activated on day 2. Alternatively, it might reflect the fact that--with German words becoming familiar on day 2--English words loose their oddball character and thus produce a weaker P300-like effect on day 2. In ERPs to Romansh words, no differences were observed. Our results reflect plasticity in the neuronal networks underlying second language acquisition. They indicate that with a higher level of second language proficiency, second language word processing is faster and requires shorter frontal activation. Thus, our results suggest that the reduced IFG activation found in previous fMRI studies might not reflect a generally lower activation but rather a shorter duration of activity.

Real-time closed-loop electrophysiology: towards new frontiers in in vitro investigations in the neurosciences

Reflected at any level of organization of the central nervous system, most of the processes ranging from ion channels to neuronal networks occur in a closed loop, where the input to the system depends on its output. In contrast, most in vitro preparations and experimental protocols operate autonomously, and do not depend on the output of the studied system. Thanks to the progress in digital signal processing and real-time computing, it is now possible to artificially close the loop and investigate biophysical processes and mechanisms under increased realism. In this contribution, we review some of the most relevant examples of a new trend in in vitro electrophysiology, ranging from the use of dynamic-clamp to multi-electrode distributed feedback stimulation. We are convinced these represents the beginning of new frontiers for the in vitro investigation of the brain, promising to open the still existing borders between theoretical and experimental approaches while taking advantage of cutting edge technologies.

Calcium indicator loading of neurons using single-cell electroporation

Studies of subcellular Ca(2+) signaling rely on methods for labeling cells with fluorescent Ca(2+) indicator dyes. In this study, we demonstrate the use of single-cell electroporation for Ca(2+) indicator loading of individual neurons and small neuronal networks in rat neocortex in vitro and in vivo. Brief voltage pulses were delivered through glass pipettes positioned close to target cells. This approach resulted in reliable and rapid (within seconds) loading of somata and subsequent complete labeling of dendritic and axonal arborizations. By using simultaneous whole-cell recordings in brain slices, we directly addressed the effect of electroporation on neurons. Cell viability was high (about 85%) with recovery from the membrane permeabilization occurring within a minute. Electrical properties of recovered cells were indistinguishable before and after electroporation. In addition, Ca(2+) transients with normal appearance could be evoked in dendrites, spines, and axonal boutons of electroporated cells. Using negative-stains of somata, targeted single-cell electroporation was equally applicable in vivo. We conclude that electroporation is a simple approach that permits Ca(2+) indicator loading of multiple cells with low background staining within a short amount of time, which makes it especially well suited for functional imaging of subcellular Ca(2+) dynamics in small neuronal networks.

An EEG approach to the neurodevelopmental hypothesis of schizophrenia studying schizophrenics, normal controls and adolescents

Based on an integrative brain model which focuses on memory-driven and EEG state-dependent information processing for the organisation of behaviour, we used the developmental changes of the awake EEG to further investigate the hypothesis that neurodevelopmental abnormalities (deviations in organisation and reorganisation of cortico-cortical connectivity during development) are involved in the pathogenesis of schizophrenia. First-episode, neuroleptic-naive schizophrenics and their matched controls and three age groups of normal adolescents were studied (total: 70 subjects). 19-channel EEG delta-theta, alpha and beta spectral band centroid frequencies during resting (baseline) and after verbal stimuli were used as measure of the level of attained complexity and momentary excitability of the neuronal network (working memory). Schizophrenics compared with all control groups showed lower delta-theta activity centroids and higher alpha and beta activity centroids. Reactivity centroids (centroid after stimulus minus centroid during resting) were used as measure of update of working memory. Schizophrenics showed partial similarities in delta-theta and beta reactivity centroids with the 11-year olds and in alpha reactivity centroids with the 13-year olds. Within the framework of our model, the results suggest multifactorially elicited imbalances in the level of excitability of neuronal networks in schizophrenia, resulting in network activation at dissociated complexity levels, partially regressed and partially prematurely developed. It is hypothesised that activation of age- and/or state-inadequate representations for coping with realities becomes manifest as productive schizophrenic symptoms. Thus, the results support some aspects of the neurodevelopmental hypothesis.

Reduced frontal activation with increasing 2nd language proficiency

The factors influencing the degree of separation or overlap in the neuronal networks responsible for the processing of first and second language are still subject to investigation. This longitudinal study investigates how increasing second language proficiency influences activation differences during lexico-semantic processing of first and second language. Native English speaking exchange students learning German were examined with functional magnetic resonance imaging while reading words in three different languages at two points in time: at the beginning of their stay (day 1) and 5 months later (day 2), when second language proficiency had significantly increased. On day 1, second language words evoked more frontal activation than words from the mother tongue. These differences were diminished on day 2. We therefore conclude that with increasing second language proficiency, lexico-semantic processing of second language words needs less frontal control. Our results demonstrate that lexico-semantic processing of first and second language converges onto similar networks as second language proficiency increases.

Modeling Calcium Concentration and Biochemical Reactions

Almost all regions of the brain receive one or more neuromodulatory inputs, and disrupting these inputs produces deficits in neuronal function. Neuromodulators act through intracellular second messenger pathways to influence the electrical properties of neurons, integration of synaptic inputs, spatio-temporal firing dynamics of neuronal networks, and, ultimately, systems behavior. Second messengers pathways consist of series of bimolecular reactions, enzymatic reactions, and diffusion. Calcium is the second messenger molecule with the most effectors, and thus is highly regulated by buffers, pumps and intracellular stores. Computational modeling provides an innovative, yet practical method to evaluate the spatial extent, time course and interaction among second messenger pathways, and the interaction of second messengers with neuron electrical properties. These processes occur both in compartments where the number of molecules are large enough to describe reactions deterministically (e.g. cell body), and in compartments where the number of molecules is small enough that reactions occur stochastically (e.g. spines). – In this tutorial, I explain how to develop models of second messenger pathways and calcium dynamics. The first part of the tutorial explains the equations used to model bimolecular reactions, enzyme reactions, calcium release channels, calcium pumps and diffusion. The second part explains some of the GENESIS, Kinetikit and Chemesis objects that implement the appropriate equations. In depth explanation of calcium and second messenger models is provided by reviewing code, both in XPP, Chemesis and Kinetikit, that implements simple models of calcium dynamics and second messenger cascades.

ModelDB in Computational Neuroscience Education - A research tool as interactive educational media

ModelDB's mission is to link computational models and publications, supporting the field of computational neuroscience (CNS) by making model source code readily available. It is continually expanding, and currently contains source code for more than 300 models that cover more than 41 topics. Investigators, educators, and students can use it to obtain working models that reproduce published results and can be modified to test for new domains of applicability. Users can browse ModelDB to survey the field of computational neuroscience, or pursue more focused explorations of specific topics. Here we describe tutorials and initial experiences with ModelDB as an interactive educational tool.

Synchronization and desynchronization in epilepsy: controversies and hypotheses

Epilepsy has been historically seen as a functional brain disorder associated with excessive synchronization of large neuronal populations leading to a hypersynchronous state. Recent evidence showed that epileptiform phenomena, particularly seizures, result from complex interactions between neuronal networks characterized by heterogeneity of neuronal firing and dynamical evolution of synchronization. Desynchronization is often observed preceding seizures or during their early stages; in contrast, high levels of synchronization observed towards the end of seizures may facilitate termination. In this review we discuss cellular and network mechanisms responsible for such complex changes in synchronization. Recent work has identified cell-type-specific inhibitory and excitatory interactions, the dichotomy between neuronal firing and the non-local measurement of local field potentials distant to that firing, and the reflection of the neuronal dark matter problem in non-firing neurons active in seizures. These recent advances have challenged long-established views and are leading to a more rigorous and realistic understanding of the pathophysiology of epilepsy.

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance.

Predicción de tráfico de contenedores a corto plazo mediante técnicas de minería de datos : redes neuronales artificiales y redes bayesianas

El correcto pronóstico en el ámbito de la logística de transportes es de vital importancia para una adecuada planificación de medios y recursos, así como de su optimización. Hasta la fecha los estudios sobre planificación portuaria se basan principalmente en modelos empíricos; que se han utilizado para planificar nuevas terminales y desarrollar planes directores cuando no se dispone de datos iniciales, analíticos; más relacionados con la teoría de colas y tiempos de espera con formulaciones matemáticas complejas y necesitando simplificaciones de las mismas para hacer manejable y práctico el modelo o de simulación; que requieren de una inversión significativa como para poder obtener resultados aceptables invirtiendo en programas y desarrollos complejos. La Minería de Datos (MD) es un área moderna interdisciplinaria que engloba a aquellas técnicas que operan de forma automática (requieren de la mínima intervención humana) y, además, son eficientes para trabajar con las grandes cantidades de información disponible en las bases de datos de numerosos problemas prácticos. La aplicación práctica de estas disciplinas se extiende a numerosos ámbitos comerciales y de investigación en problemas de predicción, clasificación o diagnosis. Entre las diferentes técnicas disponibles en minería de datos las redes neuronales artificiales (RNA) y las redes probabilísticas o redes bayesianas (RB) permiten modelizar de forma conjunta toda la información relevante para un problema dado. En el presente trabajo se han analizado dos aplicaciones de estos casos al ámbito portuario y en concreto a contenedores. En la Tesis Doctoral se desarrollan las RNA como herramienta para obtener previsiones de tráfico y de recursos a futuro de diferentes puertos, a partir de variables de explotación, obteniéndose valores continuos. Para el caso de las redes bayesianas (RB), se realiza un trabajo similar que para el caso de las RNA, obteniéndose valores discretos (un intervalo). El principal resultado que se obtiene es la posibilidad de utilizar tanto las RNA como las RB para la estimación a futuro de parámetros físicos, así como la relación entre los mismos en una terminal para una correcta asignación de los medios a utilizar y por tanto aumentar la eficiencia productiva de la terminal. Como paso final se realiza un estudio de complementariedad de ambos modelos a corto plazo, donde se puede comprobar la buena aceptación de los resultados obtenidos. Por tanto, se puede concluir que estos métodos de predicción pueden ser de gran ayuda a la planificación portuaria. The correct assets’ forecast in the field of transportation logistics is a matter of vital importance for a suitable planning and optimization of the necessary means and resources. Up to this date, ports planning studies were basically using empirical models to deal with new terminals planning or master plans development when no initial data are available; analytical models, more connected to the queuing theory and the waiting times, and very complicated mathematical formulations requiring significant simplifications to acquire a practical and easy to handle model; or simulation models, that require a significant investment in computer codes and complex developments to produce acceptable results. The Data Mining (DM) is a modern interdisciplinary field that include those techniques that operate automatically (almost no human intervention is required) and are highly efficient when dealing with practical problems characterized by huge data bases containing significant amount of information. These disciplines’ practical application extends to many commercial or research fields, dealing with forecast, classification or diagnosis problems. Among the different techniques of the Data Mining, the Artificial Neuronal Networks (ANN) and the probabilistic – or Bayesian – networks (BN) allow the joint modeling of all the relevant information for a given problem. This PhD work analyses their application to two practical cases in the ports field, concretely to container terminals. This PhD work details how the ANN have been developed as a tool to produce traffic and resources forecasts for several ports, based on exploitation variables to obtain continuous values. For the Bayesian networks case (BN), a similar development has been carried out, obtaining discreet values (an interval). The main finding is the possibility to use ANN and BN to estimate future needs of the port’s or terminal’s physical parameters, as well as the relationship between them within a specific terminal, that allow a correct assignment of the necessary means and, thus, to increase the terminal’s productive efficiency. The final step is a short term complementarily study of both models, carried out in order to verify the obtained results. It can thus be stated that these prediction methods can be a very useful tool in ports’ planning.

Leading role of thalamic over cortical neurons during postinhibitory rebound excitation

The postinhibitory rebound excitation is an intrinsic property of thalamic and cortical neurons that is implicated in a variety of normal and abnormal operations of neuronal networks, such as slow or fast brain rhythms during different states of vigilance as well as seizures. We used dual simultaneous intracellular recordings of thalamocortical neurons from the ventrolateral nucleus and neurons from the motor cortex, together with thalamic and cortical field potentials, to investigate the temporal relations between thalamic and cortical events during the rebound excitation that follows prolonged periods of stimulus-induced inhibition. Invariably, the rebound spike-bursts in thalamocortical cells occurred before the rebound depolarization in cortical neurons and preceded the peak of the depth-negative, rebound field potential in cortical areas. Also, the inhibitory-rebound sequences were more pronounced and prolonged in cortical neurons when elicited by thalamic stimuli, compared with cortical stimuli. The role of thalamocortical loops in the rebound excitation of cortical neurons was shown further by the absence of rebound activity in isolated cortical slabs. However, whereas thalamocortical neurons remained hyperpolarized after rebound excitation, because of the prolonged spike-bursts in inhibitory thalamic reticular neurons, the rebound depolarization in cortical neurons was prolonged, suggesting the role of intracortical excitatory circuits in this sustained activity. The role of intrathalamic events in triggering rebound cortical activity should be taken into consideration when analyzing information processes at the cortical level; at each step, corticothalamic volleys can set into action thalamic inhibitory neurons, leading to rebound spike-bursts that are transferred back to the cortex, thus modifying cortical activities.

Global slowing of network oscillations in mouse neocortex by diazepam

SCHEFFZUK, C. , KUKUSHKA, V. , VYSSOTSKI, A. L. , DRAGUHN, A. , TORT, A. B. L. , BRANKACK, J. . Global slowing of network oscillations in mouse neocortex by diazepam. Neuropharmacology , v. 65, p. 123-133, 2013.

Optimization of a multielectrode array (MEA)-based approach to study the impact of Aβ on the SH-SY5Y cell line

The human brain stores, integrates, and transmits information recurring to millions of neurons, interconnected by countless synapses. Though neurons communicate through chemical signaling, information is coded and conducted in the form of electrical signals. Neuroelectrophysiology focus on the study of this type of signaling. Both intra and extracellular approaches are used in research, but none holds as much potential in high-throughput screening and drug discovery, as extracellular recordings using multielectrode arrays (MEAs). MEAs measure neuronal activity, both in vitro and in vivo. Their key advantage is the capability to record electrical activity at multiple sites simultaneously. Alzheimer’s disease (AD) is the most common neurodegenerative disease and one of the leading causes of death worldwide. It is characterized by neurofibrillar tangles and aggregates of amyloid-β (Aβ) peptides, which lead to the loss of synapses and ultimately neuronal death. Currently, there is no cure and the drugs available can only delay its progression. In vitro MEA assays enable rapid screening of neuroprotective and neuroharming compounds. Therefore, MEA recordings are of great use in both AD basic and clinical research. The main aim of this thesis was to optimize the formation of SH-SY5Y neuronal networks on MEAs. These can be extremely useful for facilities that do not have access to primary neuronal cultures, but can also save resources and facilitate obtaining faster high-throughput results to those that do. Adhesion-mediating compounds proved to impact cell morphology, viability and exhibition of spontaneous electrical activity. Moreover, SH-SY5Y cells were successfully differentiated and demonstrated acute effects on neuronal function after Aβ addition. This effect on electrical signaling was dependent on Aβ oligomers concentration. The results here presented allow us to conclude that the SH-SY5Y cell line can be successfully differentiated in properly coated MEAs and be used for assessing acute Aβ effects on neuronal signaling.

Evans functions for integral neural field equations with Heaviside firing rate function

In this paper we show how to construct the Evans function for traveling wave solutions of integral neural field equations when the firing rate function is a Heaviside. This allows a discussion of wave stability and bifurcation as a function of system parameters, including the speed and strength of synaptic coupling and the speed of axonal signals. The theory is illustrated with the construction and stability analysis of front solutions to a scalar neural field model and a limiting case is shown to recover recent results of L. Zhang [On stability of traveling wave solutions in synaptically coupled neuronal networks, Differential and Integral Equations, 16, (2003), pp.513-536.]. Traveling fronts and pulses are considered in more general models possessing either a linear or piecewise constant recovery variable. We establish the stability of coexisting traveling fronts beyond a front bifurcation and consider parameter regimes that support two stable traveling fronts of different speed. Such fronts may be connected and depending on their relative speed the resulting region of activity can widen or contract. The conditions for the contracting case to lead to a pulse solution are established. The stability of pulses is obtained for a variety of examples, in each case confirming a previously conjectured stability result. Finally we show how this theory may be used to describe the dynamic instability of a standing pulse that arises in a model with slow recovery. Numerical simulations show that such an instability can lead to the shedding of a pair of traveling pulses.

Global slowing of network oscillations in mouse neocortex by diazepam

SCHEFFZUK, C. , KUKUSHKA, V. , VYSSOTSKI, A. L. , DRAGUHN, A. , TORT, A. B. L. , BRANKACK, J. . Global slowing of network oscillations in mouse neocortex by diazepam. Neuropharmacology , v. 65, p. 123-133, 2013.