Subcolumnar Dendritic and Axonal Organization of Spiny Stellate and Star Pyramid Neurons within a Barrel in Rat Somatosensory Cortex

Excitatory neurons at the level of cortical layer 4 in the rodent somatosensory barrel field often display a strong eccentricity in comparison with layer 4 neurons in other cortical regions. In rat, dendritic symmetry of the 2 main excitatory neuronal classes, spiny stellate and star pyramid neurons (SSNs and SPNs), was quantified by an asymmetry index, the dendrite-free angle. We carefully measured shrinkage and analyzed its influence on morphological parameters. SSNs had mostly eccentric morphology, whereas SPNs were nearly radially symmetric. Most asymmetric neurons were located near the barrel border. The axonal projections, analyzed at the level of layer 4, were mostly restricted to a single barrel except for those of 3 interbarrel projection neurons. Comparing voxel representations of dendrites and axon collaterals of the same neuron revealed a close overlap of dendritic and axonal fields, more pronounced in SSNs versus SPNs and considerably stronger in spiny L4 neurons versus extragranular pyramidal cells. These observations suggest that within a barrel dendrites and axons of individual excitatory cells are organized in subcolumns that may confer receptive field properties such as directional selectivity to higher layers, whereas the interbarrel projections challenge our view of barrels as completely independent processors of thalamic input.

Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons

Layer 2/3 (L2/3) pyramidal neurons are the most abundant cells of the neocortex. Despite their key position in the cortical microcircuit, synaptic integration in dendrites of L2/3 neurons is far less understood than in L5 pyramidal cell dendrites, mainly because of the difficulties in obtaining electrical recordings from thin dendrites. Here we directly measured passive and active properties of the apical dendrites of L2/3 neurons in rat brain slices using dual dendritic-somatic patch-clamp recordings and calcium imaging. Unlike L5 cells, L2/3 dendrites displayed little sag in response to long current pulses, which suggests a low density of I(h) in the dendrites and soma. This was also consistent with a slight increase in input resistance with distance from the soma. Brief current injections into the apical dendrite evoked relatively short (half-width 2-4 ms) dendritic spikes that were isolated from the soma for near-threshold currents at sites beyond the middle of the apical dendrite. Regenerative dendritic potentials and large concomitant calcium transients were also elicited by trains of somatic action potentials (APs) above a critical frequency (130 Hz), which was slightly higher than in L5 neurons. Initiation of dendritic spikes was facilitated by backpropagating somatic APs and could cause an additional AP at the soma. As in L5 neurons, we found that distal dendritic calcium transients are sensitive to a long-lasting block by GABAergic inhibition. We conclude that L2/3 pyramidal neurons can generate dendritic spikes, sharing with L5 pyramidal neurons fundamental properties of dendritic excitability and control by inhibition.

In-situ electrical, mechanical and electrochemical characterizations of one-dimensional nanostructures

One-dimensional nanostructures initiated new aspects to the materials applications due to their superior properties compared to the bulk materials. Properties of nanostructures have been characterized by many techniques and used for various device applications. However, simultaneous correlation between the physical and structural properties of these nanomaterials has not been widely investigated. Therefore, it is necessary to perform in-situ study on the physical and structural properties of nanomaterials to understand their relation. In this work, we will use a unique instrument to perform real time atomic force microscopy (AFM) and scanning tunneling microscopy (STM) of nanomaterials inside a transmission electron microscopy (TEM) system. This AFM/STM-TEM system is used to investigate the mechanical, electrical, and electrochemical properties of boron nitride nanotubes (BNNTs) and Silicon nanorods (SiNRs). BNNTs are one of the subjects of this PhD research due to their comparable, and in some cases superior, properties compared to carbon nanotubes. Therefore, to further develop their applications, it is required to investigate these characteristics in atomic level. In this research, the mechanical properties of multi-walled BNNTs were first studied. Several tests were designed to study and characterize their real-time deformation behavior to the applied force. Observations revealed that BNNTs possess highly flexible structures under applied force. Detailed studies were then conducted to understand the bending mechanism of the BNNTs. Formations of reversible ripples were observed and described in terms of thermodynamic energy of the system. Fracture failure of BNNTs were initiated at the outermost walls and characterized to be brittle. Second, the electrical properties of individual BNNTs were studied. Results showed that the bandgap and electronic properties of BNNTs can be engineered by means of applied strain. It was found that the conductivity, electron concentration and carrier mobility of BNNTs can be tuned as a function of applied stress. Although, BNNTs are considered to be candidate for field emission applications, observations revealed that their properties degrade upon cycles of emissions. Results showed that due to the high emission current density, the temperature of the sample was increased and reached to the decomposition temperature at which the B-N bonds start to break. In addition to BNNTs, we have also performed in-situ study on the electrochemical properties of silicon nanorods (SiNRs). Specifically, lithiation and delithiation of SiNRs were studied by our STM-TEM system. Our observations showed the direct formation of Li22Si5 phases as a result of lithium intercalation. Radial expansion of the anode materials were observed and characterized in terms of size-scale. Later, the formation and growth of the lithium fibers on the surface of the anode materials were observed and studied. Results revealed the formation of lithium islands inside the ionic liquid electrolyte which then grew as Li dendrite toward the cathode material.

Inhibition of dendritic Ca2+ spikes by GABAB receptors in cortical pyramidal neurons is mediated by a direct Gi/o- -subunit interaction with Cav1 channels

Voltage-dependent calcium channels (VDCCs) serve a wide range of physiological functions and their activity is modulated by different neurotransmitter systems. GABAergic inhibition of VDCCs in neurons has an important impact in controlling transmitter release, neuronal plasticity, gene expression and neuronal excitability. We investigated the molecular signalling mechanisms by which GABAB receptors inhibit calcium-mediated electrogenesis (Ca2+ spikes) in the distal apical dendrite of cortical layer 5 pyramidal neurons. Ca2+ spikes are the basis of coincidence detection and signal amplification of distal tuft synaptic inputs characteristic for the computational function of cortical pyramidal neurons. By combining dendritic whole-cell recordings with two-photon fluorescence Ca2+ imaging we found that all subtypes of VDCCs were present in the Ca2+ spike initiation zone, but that they contribute differently to the initiation and sustaining of dendritic Ca2+ spikes. Particularly, Cav1 VDCCs are the most abundant VDCC present in this dendritic compartment and they generated the sustained plateau potential characteristic for the Ca2+ spike. Activation of GABAB receptors specifically inhibited Cav1 channels. This inhibition of L-type Ca2+ currents was transiently relieved by strong depolarization but did not depend on protein kinase activity. Therefore, our findings suggest a novel membrane-delimited interaction of the Gi/o-βγ-subunit with Cav1 channels identifying this mechanism as the general pathway of GABAB receptor-mediated inhibition of VDCCs. Furthermore, the characterization of the contribution of the different VDCCs to the generation of the Ca2+ spike provides new insights into the molecular mechanism of dendritic computation.

Neural reprogramming in retinal degeneration.

PURPOSE: Early visual defects in degenerative diseases such as retinitis pigmentosa (RP) may arise from phased remodeling of the neural retina. The authors sought to explore the functional expression of ionotropic (iGluR) and group 3, type 6 metabotropic (mGluR6) glutamate receptors in late-stage photoreceptor degeneration. METHODS: Excitation mapping with organic cations and computational molecular phenotyping were used to determine whether retinal neurons displayed functional glutamate receptor signaling in rodent models of retinal degeneration and a sample of human RP. RESULTS: After photoreceptor loss in rodent models of RP, bipolar cells lose mGluR6 and iGluR glutamate-activated currents, whereas amacrine and ganglion cells retain iGluR-mediated responsivity. Paradoxically, amacrine and ganglion cells show spontaneous iGluR signals in vivo even though bipolar cells lack glutamate-coupled depolarization mechanisms. Cone survival can rescue iGluR expression by OFF bipolar cells. In a case of human RP with cone sparing, iGluR signaling appeared intact, but the number of bipolar cells expressing functional iGluRs was double that of normal retina. CONCLUSIONS: RP triggers permanent loss of bipolar cell glutamate receptor expression, though spontaneous iGluR-mediated signaling by amacrine and ganglion cells implies that such truncated bipolar cells still release glutamate in response to some nonglutamatergic depolarization. Focal cone-sparing can preserve iGluR display by nearby bipolar cells, which may facilitate late RP photoreceptor transplantation attempts. An instance of human RP provides evidence that rod bipolar cell dendrite switching likely triggers new gene expression patterns and may impair cone pathway function.

The pre- and post-somatic segments of the human type I spiral ganglion neurons--structural and functional considerations related to cochlear implantation.

Human auditory nerve afferents consist of two separate systems; one is represented by the large type I cells innervating the inner hair cells and the other one by the small type II cells innervating the outer hair cells. Type I spiral ganglion neurons (SGNs) constitute 96% of the afferent nerve population and, in contrast to other mammals, their soma and pre- and post-somatic segments are unmyelinated. Type II nerve soma and fibers are unmyelinated. Histopathology and clinical experience imply that human SGNs can persist electrically excitable without dendrites, thus lacking connection to the organ of Corti. The biological background to this phenomenon remains elusive. We analyzed the pre- and post-somatic segments of the type I human SGNs using immunohistochemistry and transmission electron microscopy (TEM) in normal and pathological conditions. These segments were found surrounded by non-myelinated Schwann cells (NMSCs) showing strong intracellular expression of laminin-β2/collagen IV. These cells also bordered the perikaryal entry zone and disclosed surface rugosities outlined by a folded basement membrane (BM) expressing laminin-β2 and collagen IV. It is presumed that human large SGNs are demarcated by three cell categories: (a) myelinated Schwann cells, (b) NMSCs and (c) satellite glial cells (SGCs). Their BMs express laminin-β2/collagen IV and reaches the BM of the sensory epithelium at the habenula perforata. We speculate that the NMSCs protect SGNs from further degeneration following dendrite loss. It may give further explanation why SGNs can persist as electrically excitable monopolar cells even after long-time deafness, a blessing for the deaf treated with cochlear implantation.

The European Registry for Patients with Mechanical Circulatory Support (EUROMACS): first annual report

The European Registry for Patients with Mechanical Circulatory Support (EUROMACS) was founded on 10 December 2009 with the initiative of Roland Hetzer (Deutsches Herzzentrum Berlin, Berlin, Germany) and Jan Gummert (Herz- und Diabeteszentrum Nordrhein-Westfalen, Bad Oeynhausen, Germany) with 15 other founding international members. It aims to promote scientific research to improve care of end-stage heart failure patients with ventricular assist device or a total artificial heart as long-term mechanical circulatory support. Likewise, the organization aims to provide and maintain a registry of device implantation data and long-term follow-up of patients with mechanical circulatory support. Hence, EUROMACS affiliated itself with Dendrite Clinical Systems Ltd to offer its members a software tool that allows input and analysis of patient clinical data on a daily basis. EUROMACS facilitates further scientific studies by offering research groups access to any available data wherein patients and centres are anonymized. Furthermore, EUROMACS aims to stimulate cooperation with clinical and research institutions and with peer associations involved to further its aims. EUROMACS is the only European-based Registry for Patients with Mechanical Circulatory Support with rapid increase in institutional and individual membership. Because of the expeditious data input, the European Association for Cardiothoracic Surgeons saw the need to optimize the data availability and the significance of the registry to improve care of patients with mechanical circulatory support and its potential contribution to scientific intents; hence, the beginning of their alliance in 2012. This first annual report is designed to provide an overview of EUROMACS' structure, its activities, a first data collection and an insight to its scientific contributions.

Transcranial magnetic stimulation (TMS) inhibits cortical dendrites

One of the leading approaches to non-invasively treat a variety of brain disorders is transcranial magnetic stimulation (TMS). However, despite its clinical prevalence, very little is known about the action of TMS at the cellular level let alone what effect it might have at the subcellular level (e.g. dendrites). Here, we examine the effect of single-pulse TMS on dendritic activity in layer 5 pyramidal neurons of the somatosensory cortex using an optical fiber imaging approach. We find that TMS causes GABAB-mediated inhibition of sensory-evoked dendritic Ca(2+) activity. We conclude that TMS directly activates fibers within the upper cortical layers that leads to the activation of dendrite-targeting inhibitory neurons which in turn suppress dendritic Ca(2+) activity. This result implies a specificity of TMS at the dendritic level that could in principle be exploited for investigating these structures non-invasively.

POU domain transcription factor Brn3b is critical for the normal development and survival of retinal ganglion cells

The POU domain transcription factor Brn3b/POU4F2 plays a critical role regulating gene expression in mouse retinal ganglion cells (RGCs). Previous investigations have shown that Brn3b is not required for initial cell fate specification or migration; however, it is essential for normal RGC differentiation. In contrast to wild type axons, the mutant neurites were phenotypically different: shorter, rougher, disorganized, and poorly fasciculated. Wild type axons stained intensely with axon specific marker tau-1, while mutant projections were weakly stained and the mutant projections showed strong labeling with dendrite specific marker MAP2. Brn-3b mutant axonal projections contained more microtubules and fewer neurofilaments, a dendritic characteristic, than the wild type. The mutant neurites also exhibited significantly weaker staining of neurofilament low-molecular-weight (NF-L) in the axon when compared to the wild type, and NF-L accumulation in the neuron cell body. The absence of Brn-3b results in an inability to form normal axons and enhanced apoptosis in RGCs, suggesting that Brn-3b may control a set of genes involved in axon formation. ^ Brn3b contains several distinct sequence motifs: a glycine/serine rich region, two histidine rich regions, and a fifteen amino acid conserved sequence shared by all Brn3 family members in the N-terminus and a POU specific and POU homeodomain in the C-terminus. Brn3b activates a Luciferase reporter over 25 fold in cell culture when binding to native brn3 binding sites upstream of a minimal promoter. When fused to the Gal4 DNA Binding domain (DBD) and driven by either a strong (CMV) or weaker (pAHD) promoter, the N-terminal of Brn3b is capable of similar activation when binding to Gal4 UAS sites, indicating a presumptive activator of transcription. Both full length Brn3b or the C-terminus fused to the Gal4DBD and driven by pCMV repressed a Luciferase reporter downstream of UAS binding sites. Lower levels of expression of the fusion protein driven by pADH resulted in an alleviation of repression. This repression appears to be a limitation of this system of transcriptional analysis and a potential pitfall in conventional pCMV based transfection assays. ^

Silencing microRNA-134 produces neuroprotective and prolonged seizure-suppressive effects

Temporal lobe epilepsy is a common, chronic neurological disorder characterized by recurrent spontaneous seizures. MicroRNAs (miRNAs) are small, noncoding RNAs that regulate post-transcriptional expression of protein-coding mRNAs, which may have key roles in the pathogenesis of neurological disorders. In experimental models of prolonged, injurious seizures (status epilepticus) and in human epilepsy, we found upregulation of miR-134, a brain-specific, activity-regulated miRNA that has been implicated in the control of dendritic spine morphology. Silencing of miR-134 expression in vivo using antagomirs reduced hippocampal CA3 pyramidal neuron dendrite spine density by 21% and rendered mice refractory to seizures and hippocampal injury caused by status epilepticus. Depletion of miR-134 after status epilepticus in mice reduced the later occurrence of spontaneous seizures by over 90% and mitigated the attendant pathological features of temporal lobe epilepsy. Thus, silencing miR-134 exerts prolonged seizure-suppressant and neuroprotective actions; determining whether these are anticonvulsant effects or are truly antiepileptogenic effects requires additional experimentation.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

The 3′-untranslated region of CaMKIIα is a cis-acting signal for the localization and translation of mRNA in dendrites

Neuronal signaling requires that synaptic proteins be appropriately localized within the cell and regulated there. In mammalian neurons, polyribosomes are found not just in the cell body, but also in dendrites where they are concentrated within or beneath the dendritic spine. The α subunit of Ca2+-calmodulin-dependent protein kinase II (CaMKIIα) is one of only five mRNAs known to be present within the dendrites, as well as in the soma of neurons. This targeted subcellular localization of the mRNA for CaMKIIα provides a possible cell biological mechanism both for controlling the distribution of the cognate protein and for regulating independently the level of protein expression in individual dendritic spines. To characterize the cis-acting elements involved in the localization of dendritic mRNA we have produced two lines of transgenic mice in which the CaMKIIα promoter is used to drive the expression of a lacZ transcript, which either contains or lacks the 3′-untranslated region of the CaMKIIα gene. Although both lines of mice show expression in forebrain neurons that parallels the expression of the endogenous CaMKIIα gene, only the lacZ transcripts bearing the 3′-untranslated region are localized to dendrites. The β-galactosidase protein shows a variable level of expression along the dendritic shaft and within dendritic spines, which suggests that neurons can control the local biochemistry of the dendrite either through differential localization of the mRNA or variations in the translational efficiency at different sites along the dendrite.

Distinct short-term plasticity at two excitatory synapses in the hippocampus

A single mossy fiber input contains several release sites and is located on the proximal portion of the apical dendrite of CA3 neurons. It is, therefore, well suited to exert a strong influence on pyramidal cell excitability. Accordingly, the mossy fiber synapse has been referred to as a detonator or teacher synapse in autoassociative network models of the hippocampus. The very low firing rates of granule cells [Jung, M. W. & McNaughton, B. L. (1993) Hippocampus 3, 165–182], which give rise to the mossy fibers, raise the question of how the mossy fiber synapse temporally integrates synaptic activity. We have therefore addressed the frequency dependence of mossy fiber transmission and compared it to associational/commissural synapses in the CA3 region of the hippocampus. Paired pulse facilitation had a similar time course, but was 2-fold greater for mossy fiber synapses. Frequency facilitation, during which repetitive stimulation causes a reversible growth in synaptic transmission, was markedly different at the two synapses. At associational/commissural synapses facilitation occurred only at frequencies greater than once every 10 s and reached a magnitude of about 125% of control. At mossy fiber synapses, facilitation occurred at frequencies as low as once every 40 s and reached a magnitude of 6-fold. Frequency facilitation was dependent on a rise in intraterminal Ca2+ and activation of Ca2+/calmodulin-dependent kinase II, and was greatly reduced at synapses expressing mossy fiber long-term potentiation. These results indicate that the mossy fiber synapse is able to integrate granule cell spiking activity over a broad range of frequencies, and this dynamic range is substantially reduced by long-term potentiation.

Calcium electrogenesis in distal apical dendrites of layer 5 pyramidal cells at a critical frequency of back-propagating action potentials

Action potentials in juvenile and adult rat layer-5 neocortical pyramidal neurons can be initiated at both axonal and distal sites of the apical dendrite. However, little is known about the interaction between these two initiation sites. Here, we report that layer 5 pyramidal neurons are very sensitive to a critical frequency of back-propagating action potentials varying between 60 and 200 Hz in different neurons. Bursts of four to five back-propagating action potentials above the critical frequency elicited large regenerative potentials in the distal dendritic initiation zone. The critical frequency had a very narrow range (10–20 Hz), and the dendritic regenerative activity led to further depolarization at the soma. The dendritic frequency sensitivity was suppressed by blockers of voltage-gated calcium channels, and also by synaptically mediated inhibition. Calcium-fluorescence imaging revealed that the site of largest transient increase in intracellular calcium above the critical frequency was located 400–700 μm from the soma at the site for initiation of calcium action potentials. Thus, the distal dendritic initiation zone can interact with the axonal initiation zone, even when inputs to the neuron are restricted to regions close to the soma, if the output of the neuron exceeds a critical frequency.

Abnormal dendritic spines in fragile X knockout mice: Maturation and pruning deficits

Fragile X syndrome arises from blocked expression of the fragile X mental retardation protein (FMRP). Golgi-impregnated mature cerebral cortex from fragile X patients exhibits long, thin, tortuous postsynaptic spines resembling spines observed during normal early neocortical development. Here we describe dendritic spines in Golgi-impregnated cerebral cortex of transgenic fragile X gene (Fmr1) knockout mice that lack expression of the protein. Dendritic spines on apical dendrites of layer V pyramidal cells in occipital cortex of fragile X knockout mice were longer than those in wild-type mice and were often thin and tortuous, paralleling the human syndrome and suggesting that FMRP expression is required for normal spine morphological development. Moreover, spine density along the apical dendrite was greater in the knockout mice, which may reflect impaired developmental organizational processes of synapse stabilization and elimination or pruning.