The effects of cocaine self-administration on dendritic spine density in the rat hippocampus are dependent on genetic background

Chronic exposure to cocaine induces modifications to neurons in the brain regions involved in addiction. Hence, we evaluated cocaine-induced changes in the hippocampal CA1 field in Fischer 344 (F344) and Lewis (LEW) rats, 2 strains that have been widely used to study genetic predisposition to drug addiction, by combining intracellular Lucifer yellow injection with confocal microscopy reconstruction of labeled neurons. Specifically, we examined the effects of cocaine self-administration on the structure, size, and branching complexity of the apical dendrites of CA1 pyramidal neurons. In addition, we quantified spine density in the collaterals of the apical dendritic arbors of these neurons. We found differences between these strains in several morphological parameters. For example, CA1 apical dendrites were more branched and complex in LEW than in F344 rats, while the spine density in the collateral dendrites of the apical dendritic arbors was greater in F344 rats. Interestingly, cocaine self-administration in LEW rats augmented the spine density, an effect that was not observed in the F344 strain. These results reveal significant structural differences in CA1 pyramidal cells between these strains and indicate that cocaine self-administration has a distinct effect on neuron morphology in the hippocampus of rats with different genetic backgrounds.

Colocalization of α-actinin and Synaptopodin in the Pyramidal Cell Axon Initial Segment

The cisternal organelle that resides in the axon initial segment (AIS) of neocortical and hippocampal pyramidal cells is thought to be involved in regulating the Ca(2+) available to maintain AIS scaffolding proteins, thereby preserving normal AIS structure and function. Through immunocytochemistry and correlative light and electron microscopy, we show here that the actin-binding protein ?-actinin is present in the typical cistenal organelle of rodent pyramidal neurons as well as in a large structure in the AIS of a subpopulation of layer V pyramidal cells that we have called the "giant saccular organelle." Indeed, this localization of ?-actinin in the AIS is dependent on the integrity of the actin cytoskeleton. Moreover, in the cisternal organelle of cultured hippocampal neurons, ?-actinin colocalizes extensively with synaptopodin, a protein that interacts with both actin and ?-actinin, and they appear concomitantly during the development of these neurons. Together, these results indicate that ?-actinin and the actin cytoskeleton are important components of the cisternal organelle that are probably required to stabilize the AIS.

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.

Random positions of dendritic spines in human cerebral cortex

Dendritic spines establish most excitatory synapses in the brain and are located in Purkinje cell’s dendrites along helical paths, perhaps maximizing the probability to contact different axons. To test whether spine helixes also occur in neocortex, we reconstructed >500 dendritic segments from adult human cortex obtained from autopsies. With Fourier analysis and spatial statistics, we analyzed spine position along apical and basal dendrites of layer 3 pyramidal neurons from frontal, temporal, and cingulate cortex. Although we occasionally detected helical positioning, for the great majority of dendrites we could not reject the null hypothesis of spatial randomness in spine locations, either in apical or basal dendrites, in neurons of different cortical areas or among spines of different volumes and lengths. We conclude that in adult human neocortex spine positions are mostly random. We discuss the relevance of these results for spine formation and plasticity and their functional impact for cortical circuits.

Random positioning of dendritic spines in the human cerebral cortex

Dendritic spines establish most excitatory synapses in the brain and are located in Purkinje cell?s dendrites along helical paths, perhaps maximizing the probability to contact different axons. To test whether spine helixes also occur in neocortex, we reconstructed ?500 dendritic segments from adult human cortex obtained from autopsies. With Fourier analysis and spatial statistics, we analyzed spine position along apical and basal dendrites of layer 3 pyramidal neurons from frontal, temporal, and cingulate cortex. Although we occasionally detected helical positioning, for the great majority of dendrites we could not reject the null hypothesis of spatial randomness in spine locations, either in apical or basal dendrites, in neurons of different cortical areas or among spines of different volumes and lengths. We conclude that in adult human neocortex spine positions are mostly random. We discuss the relevance of these results for spine formation and plasticity and their functional impact for cortical circuits.

Caracterización y simulación de arborizaciones dentríticas con redes bayesianas incluyendo variables angulares

El funcionamiento interno del cerebro es todavía hoy en día un misterio, siendo su comprensión uno de los principales desafíos a los que se enfrenta la ciencia moderna. El córtex cerebral es el área del cerebro donde tienen lugar los procesos cerebrales de más alto nivel, cómo la imaginación, el juicio o el pensamiento abstracto. Las neuronas piramidales, un tipo específico de neurona, suponen cerca del 80% de los cerca de los 10.000 millones de que componen el córtex cerebral, haciendo de ellas un objetivo principal en el estudio del funcionamiento del cerebro. La morfología neuronal, y más específicamente la morfología dendrítica, determina cómo estas procesan la información y los patrones de conexión entre neuronas, siendo los modelos computacionales herramientas imprescindibles para el estudio de su rol en el funcionamiento del cerebro. En este trabajo hemos creado un modelo computacional, con más de 50 variables relativas a la morfología dendrítica, capaz de simular el crecimiento de arborizaciones dendríticas basales completas a partir de reconstrucciones de neuronas piramidales reales, abarcando desde el número de dendritas hasta el crecimiento los los árboles dendríticos. A diferencia de los trabajos anteriores, nuestro modelo basado en redes Bayesianas contempla la arborización dendrítica en su conjunto, teniendo en cuenta las interacciones entre dendritas y detectando de forma automática las relaciones entre las variables morfológicas que caracterizan la arborización. Además, el análisis de las redes Bayesianas puede ayudar a identificar relaciones hasta ahora desconocidas entre variables morfológicas. Motivado por el estudio de la orientación de las dendritas basales, en este trabajo se introduce una regularización L1 generalizada, aplicada al aprendizaje de la distribución von Mises multivariante, una de las principales distribuciones de probabilidad direccional multivariante. También se propone una distancia circular multivariante que puede utilizarse para estimar la divergencia de Kullback-Leibler entre dos muestras de datos circulares. Comparamos los modelos con y sin regularizaci ón en el estudio de la orientación de la dendritas basales en neuronas humanas, comprobando que, en general, el modelo regularizado obtiene mejores resultados. El muestreo, ajuste y representación de la distribución von Mises multivariante se implementa en un nuevo paquete de R denominado mvCircular.---ABSTRACT---The inner workings of the brain are, as of today, a mystery. To understand the brain is one of the main challenges faced by current science. The cerebral cortex is the region of the brain where all superior brain processes, like imagination, judge and abstract reasoning take place. Pyramidal neurons, a specific type of neurons, constitute approximately the 80% of the more than 10.000 million neurons that compound the cerebral cortex. It makes the study of the pyramidal neurons crucial in order to understand how the brain works. Neuron morphology, and specifically the dendritic morphology, determines how the information is processed in the neurons, as well as the connection patterns among neurons. Computational models are one of the main tools for studying dendritic morphology and its role in the brain function. We have built a computational model that contains more than 50 morphological variables of the dendritic arborizations. This model is able to simulate the growth of complete dendritic arborizations from real neuron reconstructions, starting with the number of basal dendrites, and ending modeling the growth of dendritic trees. One of the main diferences between our approach, mainly based on the use of Bayesian networks, and other models in the state of the art is that we model the whole dendritic arborization instead of focusing on individual trees, which makes us able to take into account the interactions between dendrites and to automatically detect relationships between the morphologic variables that characterize the arborization. Moreover, the posterior analysis of the relationships in the model can help to identify new relations between morphological variables. Motivated by the study of the basal dendrites orientation, a generalized L1 regularization applied to the multivariate von Mises distribution, one of the most used distributions in multivariate directional statistics, is also introduced in this work. We also propose a circular multivariate distance that can be used to estimate the Kullback-Leibler divergence between two circular data samples. We compare the regularized and unregularized models on basal dendrites orientation of human neurons and prove that regularized model achieves better results than non regularized von Mises model. Sampling, fitting and plotting functions for the multivariate von Mises are implemented in a new R packaged called mvCircular.

Volume Haptics and Dendritic Spine Editing

Esta tesis se ha desarrollado en el contexto del proyecto Cajal Blue Brain, una iniciativa europea dedicada al estudio del cerebro. Uno de los objetivos de esta iniciativa es desarrollar nuevos métodos y nuevas tecnologías que simplifiquen el análisis de datos en el campo neurocientífico. El presente trabajo se ha centrado en diseñar herramientas que combinen información proveniente de distintos canales sensoriales con el fin de acelerar la interacción y análisis de imágenes neurocientíficas. En concreto se estudiará la posibilidad de combinar información visual con información háptica. Las espinas dendríticas son pequeñas protuberancias que recubren la superficie dendrítica de muchas neuronas del cerebro. A día de hoy, se cree que tienen un papel clave en la transmisión de señales neuronales. Motivo por el cual, el interés por parte de la comunidad científica por estas estructuras ha ido en aumento a medida que las técnicas de adquisición de imágenes mejoraban hasta alcanzar una calidad suficiente para analizar dichas estructuras. A menudo, los neurocientíficos utilizan técnicas de microscopía con luz para obtener los datos que les permitan analizar estructuras neuronales tales como neuronas, dendritas y espinas dendríticas. A pesar de que estas técnicas ofrezcan ciertas ventajas frente a su equivalente electrónico, las técnicas basadas en luz permiten una menor resolución. En particular, estructuras pequeñas como las espinas dendríticas pueden capturarse de forma incorrecta en las imágenes obtenidas, impidiendo su análisis. En este trabajo, se presenta una nueva técnica, que permite editar imágenes volumétricas, mediante un dispositivo háptico, con el fin de reconstruir de los cuellos de las espinas dendríticas. Con este objetivo, en un primer momento se desarrolló un algoritmo que proporciona retroalimentación háptica en datos volumétricos, completando la información que provine del canal visual. Dicho algoritmo de renderizado háptico permite a los usuarios tocar y percibir una isosuperficie en el volumen de datos. El algoritmo asegura un renderizado robusto y eficiente. Se utiliza un método basado en las técnicas de “marching tetrahedra” para la extracción local de una isosuperficie continua, lineal y definida por intervalos. La robustez deriva tanto de una etapa de detección de colisiones continua de la isosuperficie extraída, como del uso de técnicas eficientes de renderizado basadas en un proxy puntual. El método de “marching tetrahedra” propuesto garantiza que la topología de la isosuperficie extraída coincida con la topología de una isosuperficie equivalente determinada utilizando una interpolación trilineal. Además, con el objetivo de mejorar la coherencia entre la información háptica y la información visual, el algoritmo de renderizado háptico calcula un segundo proxy en la isosuperficie pintada en la pantalla. En este trabajo se demuestra experimentalmente las mejoras en, primero, la etapa de extracción de isosuperficie, segundo, la robustez a la hora de mantener el proxy en la isosuperficie deseada y finalmente la eficiencia del algoritmo. En segundo lugar, a partir del algoritmo de renderizado háptico propuesto, se desarrolló un procedimiento, en cuatro etapas, para la reconstrucción de espinas dendríticas. Este procedimiento, se puede integrar en los cauces de segmentación automática y semiautomática existentes como una etapa de pre-proceso previa. El procedimiento está diseñando para que tanto la navegación como el proceso de edición en sí mismo estén controlados utilizando un dispositivo háptico. Se han diseñado dos experimentos para evaluar esta técnica. El primero evalúa la aportación de la retroalimentación háptica y el segundo se centra en evaluar la idoneidad del uso de un háptico como dispositivo de entrada. En ambos casos, los resultados demuestran que nuestro procedimiento mejora la precisión de la reconstrucción. En este trabajo se describen también dos casos de uso de nuestro procedimiento en el ámbito de la neurociencia: el primero aplicado a neuronas situadas en la corteza cerebral humana y el segundo aplicado a espinas dendríticas situadas a lo largo de neuronas piramidales de la corteza del cerebro de una rata. Por último, presentamos el programa, Neuro Haptic Editor, desarrollado a lo largo de esta tesis junto con los diferentes algoritmos ya mencionados. ABSTRACT This thesis took place within the Cajal Blue Brain project, a European initiative dedicated to the study of the brain. One of the main goals of this project is the development of new methods and technologies simplifying data analysis in neuroscience. This thesis focused on the development of tools combining information originating from distinct sensory channels with the aim of accelerating both the interaction with neuroscience images and their analysis. In concrete terms, the objective is to study the possibility of combining visual information with haptic information. Dendritic spines are thin protrusions that cover the dendritic surface of numerous neurons in the brain and whose function seems to play a key role in neural circuits. The interest of the neuroscience community toward those structures kept increasing as and when acquisition methods improved, eventually to the point that the produced datasets enabled their analysis. Quite often, neuroscientists use light microscopy techniques to produce the dataset that will allow them to analyse neuronal structures such as neurons, dendrites and dendritic spines. While offering some advantages compared to their electronic counterpart, light microscopy techniques achieve lower resolutions. Particularly, small structures such as dendritic spines might suffer from a very low level of fluorescence in the final dataset, preventing further analysis. This thesis introduces a new technique enabling the edition of volumetric datasets in order to recreate dendritic spine necks using a haptic device. In order to fulfil this objective, we first presented an algorithm to provide haptic feedback directly from volumetric datasets, as an aid to regular visualization. The haptic rendering algorithm lets users perceive isosurfaces in volumetric datasets, and it relies on several design features that ensure a robust and efficient rendering. A marching tetrahedra approach enables the dynamic extraction of a piecewise linear continuous isosurface. Robustness is derived using a Continuous Collision Detection step coupled with acknowledged proxy-based rendering methods over the extracted isosurface. The introduced marching tetrahedra approach guarantees that the extracted isosurface will match the topology of an equivalent isosurface computed using trilinear interpolation. The proposed haptic rendering algorithm improves the coherence between haptic and visual cues computing a second proxy on the isosurface displayed on screen. Three experiments demonstrate the improvements on the isosurface extraction stage as well as the robustness and the efficiency of the complete algorithm. We then introduce our four-steps procedure for the complete reconstruction of dendritic spines. Based on our haptic rendering algorithm, this procedure is intended to work as an image processing stage before the automatic segmentation step giving the final representation of the dendritic spines. The procedure is designed to allow both the navigation and the volume image editing to be carried out using a haptic device. We evaluated our procedure through two experiments. The first experiment concerns the benefits of the force feedback and the second checks the suitability of the use of a haptic device as input. In both cases, the results shows that the procedure improves the editing accuracy. We also report two concrete cases where our procedure was employed in the neuroscience field, the first one concerning dendritic spines in the human cortex, the second one referring to an ongoing experiment studying dendritic spines along dendrites of mouse cortical pyramidal neurons. Finally, we present the software program, Neuro Haptic Editor, that was built along the development of the different algorithms implemented during this thesis, and used by neuroscientists to use our procedure.

Pyramidal cells in prefrontal cortex: comparative observations reveal unparalleled specializations in neuronal structure among primate species

The most ubiquitous neuron in the cerebral cortex, the pyramidal cell, is characterized by markedly different dendritic structure among different cortical areas. The complex pyramidal cell phenotype in granular prefrontal cortex (gPFC) of higher primates endows specific biophysical properties and patterns of connectivity, which differ from those in other cortical regions. However, within the gPFC, data have been sampled from only a select few cortical areas. The gPFC of species such as human and macaque monkey includes more than 10 cortical areas. It remains unknown as to what degree pyramidal cell structure may vary among these cortical areas. Here we undertook a survey of pyramidal cells in the dorsolateral, medial, and orbital gPFC of cercopithecid primates. We found marked heterogeneity in pyramidal cell structure within and between these regions. Moreover, trends for gradients in neuronal complexity varied among species. As the structure of neurons determines their computational abilities, memory storage capacity and connectivity, we propose that these specializations in the pyramidal cell phenotype are an important determinant of species-specific executive cortical functions in primates.

Dendritic-Like Reliable Computation in Artificial Neurons

Dendritic computation is a term that has been in neuro physiological research for a long time [1]. It is still controversial and far for been clarified within the concepts of both computation and neurophysiology [2], [3]. In any case, it hasnot been integrated neither in a formal computational scheme or structure, nor into formulations of artificial neural nets. Our objective here is to formulate a type of distributed computation that resembles dendritic trees, in such a way that it shows the advantages of neural network distributed computation, mostly the reliability that is shown under the existence of holes (scotomas) in the computing net, without ?blind spots?.

A stereological study of synapse number in the epileptic human hippocampus

Hippocampal sclerosis is the most frequent pathology encountered in resected mesial temporal structures from patients with intractable temporal lobe epilepsy (TLE). Here, we have used stereological methods to compare the overall density of synapses and neurons between non-sclerotic and sclerotic hippocampal tissue obtained by surgical resection from patients with TLE. Specifically, we examined the possible changes in the subiculum and CA1, regions that seem to be critical for the development and/or maintenance of seizures in these patients. We found a remarkable decrease in synaptic and neuronal density in the sclerotic CA1, and while the subiculum from the sclerotic hippocampus did not display changes in synaptic density, the neuronal density was higher. Since the subiculum from the sclerotic hippocampus displays a significant increase in neuronal density, as well as a various other neurochemical changes, we propose that the apparently normal subiculum from the sclerotic hippocampus suffers profound alterations in neuronal circuits at both the molecular and synaptic level that are likely to be critical for the development or maintenance of seizure activity

Human midbrain precursors activate the expected developmental genetic program and differentiate long-term to functional A9 dopamine neurons in vitro. Enhancement by Bcl-XL

Understanding the molecular programs of the generation of human dopaminergic neurons (DAn) from their ventral mesencephalic (VM) precursors is of key importance for basic studies, progress in cell therapy, drug screening and pharmacology in the context of Parkinson's disease. The nature of human DAn precursors in vitro is poorly understood, their properties unstable, and their availability highly limited. Here we present positive evidence that human VM precursors retaining their genuine properties and long-term capacity to generate A9 type Substantia nigra human DAn (hVM1 model cell line) can be propagated in culture. During a one month differentiation, these cells activate all key genes needed to progress from pro-neural and prodopaminergic precursors to mature and functional DAn. For the first time, we demonstrate that gene cascades are correctly activated during differentiation, resulting in the generation of mature DAn. These DAn have morphological and functional properties undistinguishable from those generated by VM primary neuronal cultures. In addition, we have found that the forced expression of Bcl-XL induces an increase in the expression of key developmental genes (MSX1, NGN2), maintenance of PITX3 expression temporal profile, and also enhances genes involved in DAn long-term function, maintenance and survival (EN1, LMX1B, NURR1 and PITX3). As a result, Bcl-XL anticipates and enhances DAn generation.

Dyrk1A Influences Neuronal Morphogenesis Through Regulation of Cytoskeletal Dynamics in Mammalian Cortical Neurons

Down syndrome (DS) is the most frequent genetic cause of mental retardation. Cognitive dysfunction in these patients is correlated with reduced dendritic branching and complexity, along with fewer spines of abnormal shape that characterize the cortical neuronal profile of DS. DS phenotypes are caused by the disruptive effect of specific trisomic genes. Here, we report that overexpression of dual-specificity tyrosine phosphorylation-regulated kinase 1A, DYRK1A, is sufficient to produce the dendritic alterations observed in DS patients. Engineered changes in Dyrk1A gene dosage in vivo strongly alter the postnatal dendritic arborization processes with a similar progression than in humans. In cultured mammalian cortical neurons, we determined a reduction of neurite outgrowth and synaptogenesis. The mechanism underlying neurite dysgenesia involves changes in the dynamic reorganization of the cytoskeleton.

Developmental Expression of Kv Potassium Channels at the Axon Initial Segment of Cultured Hippocampal Neurons

Axonal outgrowth and the formation of the axon initial segment (AIS) are early events in the acquisition of neuronal polarity. The AIS is characterized by a high concentration of voltage-dependent sodium and potassium channels. However, the specific ion channel subunits present and their precise localization in this axonal subdomain vary both during development and among the types of neurons, probably determining their firing characteristics in response to stimulation. Here, we characterize the developmental expression of different subfamilies of voltage-gated potassium channels in the AISs of cultured mouse hippocampal neurons, including subunits Kv1.2, Kv2.2 and Kv7.2. In contrast to the early appearance of voltage-gated sodium channels and the Kv7.2 subunit at the AIS, Kv1.2 and Kv2.2 subunits were tethered at the AIS only after 10 days in vitro. Interestingly, we observed different patterns of Kv1.2 and Kv2.2 subunit expression, with each confined to distinct neuronal populations. The accumulation of Kv1.2 and Kv2.2 subunits at the AIS was dependent on ankyrin G tethering, it was not affected by disruption of the actin cytoskeleton and it was resistant to detergent extraction, as described previously for other AIS proteins. This distribution of potassium channels in the AIS further emphasizes the heterogeneity of this structure in different neuronal populations, as proposed previously, and suggests corresponding differences in action potential regulation.

Three-Dimensional Analysis of Spiny Dendrites Using Straightening and Unrolling Transforms

Current understanding of the synaptic organization of the brain depends to a large extent on knowledge about the synaptic inputs to the neurons. Indeed, the dendritic surfaces of pyramidal cells (the most common neuron in the cerebral cortex) are covered by thin protrusions named dendritic spines. These represent the targets of most excitatory synapses in the cerebral cortex and therefore, dendritic spines prove critical in learning, memory and cognition. This paper presents a new method that facilitates the analysis of the 3D structure of spine insertions in dendrites, providing insight on spine distribution patterns. This method is based both on the implementation of straightening and unrolling transformations to move the analysis process to a planar, unfolded arrangement, and on the design of DISPINE, an interactive environment that supports the visual analysis of 3D patterns.

Multiple attractors, long chaotic transients, and failure in small-world networks of excitable neurons.

We study the dynamical states of a small-world network of recurrently coupled excitable neurons, through both numerical and analytical methods. The dynamics of this system depend mostly on both the number of long-range connections or ?shortcuts?, and the delay associated with neuronal interactions. We find that persistent activity emerges at low density of shortcuts, and that the system undergoes a transition to failure as their density reaches a critical value. The state of persistent activity below this transition consists of multiple stable periodic attractors, whose number increases at least as fast as the number of neurons in the network. At large shortcut density and for long enough delays the network dynamics exhibit exceedingly long chaotic transients, whose failure times follow a stretched exponential distribution. We show that this functional form arises for the ensemble-averaged activity if the failure time for each individual network realization is exponen- tially distributed