Modulation of synaptic transmission in hippocampal CA1 neurons by a novel neurotoxin (beta-pompilidotoxin) derived from wasp venom

We examined the effects of beta-pompilidotoxin (beta-PMTX), a neurotoxin derived from wasp venom. on synaptic transmission in the mammalian central nervous system (CNS). Using hippocampal slice preparations of rodents, we made both extracellular and intracellular recordings from the CA1 pyramidal neurons in response to stimulation of the Schaffer collateral/commissural fibers. Application of 5-10 muM beta-PMTX enhanced excitatory postsynaptic potentials (EPSPs) but suppressed the fast component of the inhibitory postsynaptic potentials (IPSPs). In the presence of 10 muM bicuculline, beta-PMTX potentiated EPSPs that were composed of both non-NMDA and NMDA receptor-mediated potentials. Potentiation of EPSPs was originated by repetitive firings of the prosynaptic axons, causing Summation of EPSPs. In the presence of 10 muM CNQX and 50 muM APV, beta-PMTX suppressed GABA(A) receptor-mediated fast IPSPs but retained GABA(B) receptor-mediated slow IPSPs. Our results suggest that beta-PMTX facilitates excitatory synaptic transmission by a presynaptic mechanism and that it causes overexcitation followed by block of the activity of some population of interneurons which regulate the activity of GABA(A) receptors. (C) 2001 Published by Elsevier B.V. Ireland Ltd and the Japan Neuroscience Society.

Absence of effects of contralateral group I muscle afferents on presynaptic inhibition of Ia terminals in humans and cats

Mezzarane RA, Kohn AF, Couto-Roldan E, Martinez L, Flores A, Manjarrez E. Absence of effects of contralateral group I muscle afferents on presynaptic inhibition of Ia terminals in humans and cats. J Neurophysiol 108: 1176-1185, 2012. First published June 6, 2012; doi:10.1152/jn.00831.2011.-Crossed effects from group I afferents on reflex excitability and their mechanisms of action are not yet well understood. The current view is that the influence is weak and takes place indirectly via oligosynaptic pathways. We examined possible contralateral effects from group I afferents on presynaptic inhibition of Ia terminals in humans and cats. In resting and seated human subjects the soleus (SO) H-reflex was conditioned by an electrical stimulus to the ipsilateral common peroneal nerve (CPN) to assess the level of presynaptic inhibition (PSI_control). A brief conditioning vibratory stimulus was applied to the triceps surae tendon at the contralateral side (to activate preferentially Ia muscle afferents). The amplitude of the resulting H-reflex response (PSI_conditioned) was compared to the H-reflex under PSI_control, i.e., without the vibration. The interstimulus interval between the brief vibratory stimulus and the electrical shock to the CPN was -60 to 60 ms. The H-reflex conditioned by both stimuli did not differ from that conditioned exclusively by the ipsilateral CPN stimulation. In anesthetized cats, bilateral monosynaptic reflexes (MSRs) in the left and right L 7 ventral roots were recorded simultaneously. Conditioning stimulation applied to the contralateral group I posterior biceps and semitendinosus (PBSt) afferents at different time intervals (0-120 ms) did not have an effect on the ipsilateral gastrocnemius/soleus (GS) MSR. An additional experimental paradigm in the cat using contralateral tendon vibration, similar to that conducted in humans, was also performed. No significant differences between GS-MSRs conditioned by ipsilateral PBSt stimulus alone and those conditioned by both ipsilateral PBSt stimulus and contralateral tendon vibration were detected. The present results strongly suggest an absence of effects from contralateral group I fibers on the presynaptic mechanism of MSR modulation in relaxed humans and anesthetized cats.

Computation of direction selectivity in retinal starburst amacrine cell dendrites – studied using electrophysiological recordings and two-photon imaging

Neuronal circuits in the retina analyze images according to qualitative aspects such as color or motion, before the information is transmitted to higher visual areas of the brain. One example, studied for over the last four decades, is the detection of motion direction in ‘direction selective’ neurons. Recently, the starburst amacrine cell, one type of retinal interneuron, has emerged as an essential player in the computation of direction selectivity. In this study the mechanisms underlying the computation of direction selective calcium signals in starburst cell dendrites were investigated using whole-cell electrical recordings and two-photon calcium imaging. Analysis of the somatic electrical responses to visual stimulation and pharmacological agents indicated that the directional signal (i) is not computed presynaptically to starburst cells or by inhibitory network interactions. It is thus computed via a cell-intrinsic mechanism, which (ii) depends upon the differential, i.e. direction selective, activation of voltage-gated channels. Optically measuring dendritic calcium signals as a function of somatic voltage suggests (iii) a difference in resting membrane potential between the starburst cell’s soma and its distal dendrites. In conclusion, it is proposed that the mechanism underlying direction selectivity in starburst cell dendrites relies on intrinsic properties of the cell, particularly on the interaction of spatio-temporally structured synaptic inputs with voltage-gated channels, and their differential activation due to a somato-dendritic difference in membrane potential.

Zelluläre Kontrolle adulter Neurogenese - Expression und Funktion des VEGF-Rezeptor-1 (Flt-1) im adulten Säugerhirn

Neurale Stammzellen sind im adulten Säugerhirn in der Subventrikulären Zone (SVZ) der Lateralventrikel und dem Hippokampus lokalisiert. In der SVZ entstandene neurale Zellen migrieren entlang eines von Astrozyten umgebenen Pfades, dem Rostralmigratorischen Strom (RMS), zum Olfaktorischen Bulbus (OB), wo sie zu olfaktorischen Interneuronen differenzieren. Vaskuläre Wachstumsfaktoren, wie VEGF-A beeinflussen die adulte Neurogenese. Die vorliegende Arbeit beschreibt erstmalig detailliert die spezifische Expression des VEGF-Rezeptor-1 (VEGFR-1) in den Regionen olfaktorischer und hippokampaler Neurogenese des adulten ZNS. Die Ergebnisse zeigen, dass VEGFR-1 im adulten Hirn hauptsächlich in GFAP-positiven Zellen in der SVZ, dem RMS, dem OB, dem Corpus callosum und dem Hippokampus exprimiert ist. In vivo-Analysen transgener Mäuse (Flt-1TK-/-), denen die Signaltransduktionsdomäne des VEGFR-1 fehlt, demonstrieren hier erstmals eine Rolle des VEGFR-1 in adulter Neurogenese. Flt-1TK-/- weisen eine erhöhte Proliferation neuronaler Vorläuferzellen der SVZ auf. Im RMS ist jedoch 6 Tage nach BrdU-Administration die Anzahl markierter Zellen im Vergleich zum Wildtyp (wt) um 47,97% reduziert, ohne dass es zu einer Akkumulation in der SVZ kommt. Zusammen mit der in Kulturversuchen stark erhöhten Migrationsgeschwindigkeit von Neuroblasten der Flt-1TK-/- und einer verminderten Abwanderung von Zellen aus dem RMS ins Corpus callosum der Flt-1Tk-/-, weist dies auf eine gesteigerte Migration zum OB hin. Tatsächlich war der OB der Flt-1TK-/-, vor allem die Plexiform- und Periglomerulärzellschicht (PGL), signifikant vergrößert. Im OB der transgenen Tiere migrieren zudem signifikant mehr BrdU-markierte Zellen in die PGL. Dort differenzieren signifikant mehr Neurone als im wt. Subtypisierungen zeigen, zudem eine erhöhte Differenzierung in dopaminerge Interneurone in der PGL der Flt-1TK-/-. Im Gehirn Flt-1TK-/- war die Konzentration von VEGF-A erhöht. Intrazerebroventrikuläre Infusion von VEGF-A in wt-Tiere erbrachte den eindeutigen Nachweis, dass die Erhöhung der VEGF-A-Konzentration im Gehirn der Flt-1TK-/- ursächlich für die in diesen Tieren beobachtete Reduktion der BrdU-positiven Zellen im RMS ist. Dies ist gleichzeitig der erste Nachweis einer Wirkung von VEGF-A auf Neuroblasten im RMS in vivo unter physiologischen Bedingungen. Die erhöhte VEGF-A-Konzentration könnte auch den anderen hier dargelegten Effekten zugrunde liegen. VEGFR-1 ist somit ein regulatorischer Faktor für die adulte olfaktorische Neurogenese und spielt eine potentielle Rolle in der Differenzierung dopaminerger Interneurone.

Identification of target genes of the T-box proteins in Drosophila melanogaster

In a prior bioinformatic analysis by Hüyseyin Binbas, potential Tbx targets sequences in wing-related genes have been identified. Guided by this information, enhancer trap/reporter lacZ insertions were characterized by X-gal staining first in wildtype and then in l(1)omb imaginal discs.rnIn several lines I observed an increase in reporter expression in a l(1)omb mutant background. Since Omb is assumed to function predominantly as a transcriptional repressor, this may indicate direct regulation. Repression by Omb was observed e.g. for brk and tkv. These genes are negatively regulated by Dpp, while omb is induced by Dpp. Omb which mediates the effects of Dpp on proliferation could, thus, also mediate the Dpp effect on patterning of the wing disc. However, brk and tkv were not completely derepressed in l(1)omb indicating that Dpp represses these genes also by an Omb-independent mechanism.rnMore frequently I observed loss of reporter expression in an l(1)omb mutant background. In these cases, regulation by Omb presumably is indirect. For example, STAT92E-lacZ expression in the wildtype eye was symmetrically expressed at the dorsal and ventral margins. In l(1)omb, ventral expression was selectively lost. Loss of omb is known to cause ventral overproliferation of the eye by activation of the Jak/STAT pathway. STAT92E expression is negatively regulated by Jak/STAT signaling suggesting that loss of omb activates Jak/STAT further upstream in the pathway.rnRegional overproliferation of eye and wing in the l(1)omb mutant background proved a complicating issue in the search for Omb targets. This effect made it difficult to decide whether an expanded reporter expression pattern was due to tissue expansion or reporter gene derepression. For instance hth-lacZ appeared to expand along the ventral eye disc margin in l(1)omb. Without addtional experiments it cannot be concluded whether this is due to de-repression or to activation in association with the proliferative state. Parallel to my experiments, evidence accumulated in our laboratory that loss of omb may attenuate Wg and Hegehog signaling. Since these diffusible proteins are the main patterning molecules in the wing imaginal disc, with dpp being downstream of Hh, many of the observed effects could be secondary to reduced Wg and Hh activity. Examples are ab-lacZ, Dll-lacZ and vgBE-lacZ (reduced expression on the dorso-ventral boundary) and inv-lacZ (late larval expression in the anterior wing disc compartment is lost) or sal-lacZ. Epistasis experiment will be required to clarifiy these issues.rnFurthermore, loss of omb appeared to induce cell fate changes. It was reported previously that in an omb null mutant, the dorsal determinant apterous (ap) is ectopically expressed in the ventral compartment (an effect I did not observe with the strongly hypomorphic l(1)omb15, indicating strong dose dependence). Ventral repression of ap is maintained by epigenetic mechanisms. The patchy and variable nature of ectopic expression of ap or grn-1.1-lacZ points to an effect of omb on epigenetic stability.rnIn the second part of my thesis, an analysis of Omb expression in the Drosophila embryonic ventral nervous system was performed. Omb was found co-expressed with Eve in the medial aCC and RP2 motorneurons as well as the fpCC interneuron and the mediolateral CQ neurons. Additionally, Omb was detected in the Eg positive NB7-3 GW serotonergic motoneuron and the N2-4 neurons. Omb was not found in Repo positive glial cells. During embryonic stage 14, Omb showed some coepression with Dpn or Pros. At the embryonic stage 16, Omb was expressed in minor subset of Mid and Wg positive cells.

Synaptic regulation of spike firing in interneurons of the striatum in vivo

The striatum, the major input nucleus of the basal ganglia, is numerically dominated by a single class of principal neurons, the GABAergic spiny projection neuron (SPN) that has been extensively studied both in vitro and in vivo. Much less is known about the sparsely distributed interneurons, principally the cholinergic interneuron (CIN) and the GABAergic fast-spiking interneuron (FSI). Here, we summarize results from two recent studies on these interneurons where we used in vivo intracellular recording techniques in urethane-anaesthetized rats (Schulz et al., J Neurosci 31[31], 2011; J Physiol, in press). Interneurons were identified by their characteristic responses to intracellular current steps and spike waveforms. Spontaneous spiking contained a high proportion (~45%) of short inter-spike intervals (ISI) of <30 ms in FSIs, but virtually none in CINs. Spiking patterns in CINs covered a broad spectrum ranging from regular tonic spiking to phasic activity despite very similar unimodal membrane potential distributions across neurons. In general, phasic spiking activity occurred in phase with the slow ECoG waves, whereas CINs exhibiting tonic regular spiking were little affected by afferent network activity. In contrast, FSIs exhibited transitions between Down and Up states very similar to SPNs. Compared to SPNs, the FSI Up state membrane potential was noisier and power spectra exhibited significantly larger power at frequencies in the gamma range (55-95 Hz). Cortical-evoked inputs had faster dynamics in FSIs than SPNs and the membrane potential preceding spontaneous spike discharge exhibited short and steep trajectories, suggesting that fast input components controlled spike output in FSIs. Intrinsic resonance mechanisms may have further enhanced the sensitivity of FSIs to fast oscillatory inputs. Induction of an activated ECoG state by local ejection of bicuculline into the superior colliculus, resulted in increased spike frequency in both interneuron classes without changing the overall distribution of ISIs. This manipulation also made CINs responsive to a light flashed into the contralateral eye. Typically, the response consisted of an excitation at short latency followed by a pause in spike firing, via an underlying depolarization-hyperpolarization membrane sequence. These results highlight the differential sensitivity of striatal interneurons to afferent synaptic signals and support a model where CINs modulate the striatal network in response to salient sensory bottom-up signals, while FSIs serve gating of top-down signals from the cortex during action selection and reward-related learning.

Directed differentiation and functional maturation of cortical interneurons from human embryonic stem cells

Human pluripotent stem cells are a powerful tool for modeling brain development and disease. The human cortex is composed of two major neuronal populations: projection neurons and local interneurons. Cortical interneurons comprise a diverse class of cell types expressing the neurotransmitter GABA. Dysfunction of cortical interneurons has been implicated in neuropsychiatric diseases, including schizophrenia, autism, and epilepsy. Here, we demonstrate the highly efficient derivation of human cortical interneurons in an NKX2.1::GFP human embryonic stem cell reporter line. Manipulating the timing of SHH activation yields three distinct GFP+ populations with specific transcriptional profiles, neurotransmitter phenotypes, and migratory behaviors. Further differentiation in a murine cortical environment yields parvalbumin- and somatostatin-expressing neurons that exhibit synaptic inputs and electrophysiological properties of cortical interneurons. Our study defines the signals sufficient for modeling human ventral forebrain development in vitro and lays the foundation for studying cortical interneuron involvement in human disease pathology.

Functional and motor neuronal analysis of defensive siphon responses in {\it Aplysia californica}

One of the central goals of neuroscience research is to determine how networks of neurons control and modify behavior. One of the most influential model systems for this kind of analysis is the siphon and gill withdrawal reflex of the marine mollusc A. californica. In response to tactile stimulation, the siphon displays 3 different responses: (1) a posterior pointing and leveling (flaring) of the siphon in response to tail stimulation (the siphon T response), (2) constriction and anterior pointing to head stimulation (the siphon H response) and (3) constriction and withdrawal between the animal's parapodia (the siphon S response). The siphon S response is pseudoconditioned by a noxious tail stimulus to resemble the siphon T response. Behavioral and combined behavioral/intracellular studies were conducted to determine the motor neuronal control of these behaviors and to search for mechanisms of siphon response transformation following pseudoconditioning. The present studies have found that the flaring component of pseudoconditioned siphon S responses occurs during mantle pumping (MP) triggered by noxious tail stimulation. Siphon stimulation also triggers MP, as recorded in neurons of the Interneuron II pattern generator which commands MP. The 4 LF$\rm\sb{SB}$ siphon motor neurons (SMNs) were found necessary and sufficient for the siphon T response, while SMNs RD$\rm\sb S$ and LD$\rm\sb{S1}$ were found necessary and sufficient for the siphon H response. Following pseudoconditioning, there is an increase in the number of evoked spikes to the test stimulus for the LF$\rm\sb{SB}$ cells and a decreased number for RD$\rm\sb S.$ Siphon flaring occurring during the pseudoconditioned response correlates with increased LF$\rm\sb{SB}$ activity during triggered MP cycles. This suggests that psuedoconditioning is in part due to reconfiguration of the motor outputs of the Interneuron II network. These results suggest that these defensive responses are controlled and patterned by a well-defined, finite set of motor neurons and interneurons (Interneuron II) that are dedicated to specific behavioral functions, but also have parallel distributed properties. ^

Anatomical organization of the memory underlying sensitization in the siphon-withdrawal reflex circuit of {\it Aplysia californica}

Previous studies have shown that short-term sensitization of the Aplysia siphon-withdrawal reflex circuit results in multiple sites of change in synaptic efficacy. In this dissertation I have used a realistic modeling approach (using an integrate-and-fire scheme), in conjunction with electrophysiological experiments, to evaluate the contribution of each site of plasticity to the sensitized response.^ This dissertation contains a detailed description of methodology for the construction of the model circuit, consisting of the LFS motor neurons and ten interneurons known to convey excitatory input to them. The model replicates closely the natural motor neuron firing response to a brief tactile stimulus.^ The various circuit elements have different roles for producing circuit output. For example, the sensory connections onto the motor neuron are important for the production of the phasic response, while the polysynaptic interneuronal connections are important for producing the tonic response.^ The multiple sites of plasticity that produce changes in circuit output also have specialized roles. Presynaptic facilitation of the sensory neuron to LFS connection enhances only the phasic component of the motor neuron firing response. The sensory neuron to interneuron connections primarily enhance the tonic component of the motor neuron firing response. Also, the L29 posttetanic potentiation and the L30 presynaptic inhibition primarily enhance the tonic component of the motor neuron firing response. Finally, the information content at the various sites of plasticity can shift with changes in stimulus intensity. This suggests that while the sites of plasticity encoding memory are fixed, the information content at these sites can be dynamic, shifting in anatomical location with changes in the intensity of the test stimulus.^ These sites of plasticity also produce specific changes in the behavioral response. Sensory-LFS plasticity selectively increases the amplitude of the behavioral response, and has no effect on the duration of the behavioral response. Interneuronal plasticity (L29 and L30) affects both the amplitude and duration of the behavioral response. Other sensory plasticity also affect both the amplitude and duration of the behavioral response, presumably by increasing the recruitment of the interneurons, which provide all of the effect on duration of the behavioral response. ^

Ionotropic Glutamate Receptors Show Unique Distribution and Localization in the Dorsal Cochlear Nucleus of the Rhesus Monkey

The dorsal cochlear nucleus (DCN) receives auditory information via the auditory nerve coming from the cochlea. It is responsible for much of the integration of auditory information, and it projects this auditory information to higher auditory brain centers for further processing. This study focuses on the DCN of adult Rhesus monkeys to characterize two specific cell types, the fusiform and cartwheel cell, based on morphometric parameters and type of glutamate receptor they express. The fusiform cell is the main projection neuron, while the cartwheel cell is the main inhibitory interneuron. Expression of AMPA glutamate receptor subunits is localized to certain cell types. The activity of the CN depends on the AMPA receptor subunit composition and expression. Immunocytochemistry, using specific antibodies for AMPA glutamate receptor subunits GluR1, GluR2/3 and GluR4, was used in conjunction with morphometry to determine the location, morphological characteristics and expression of AMPA receptor subunits in fusiform and cartwheel cells in the primate DCN. Qualitative as well as quantitative data indicates that there are important morphological differences in cell location and expression of AMPA glutamate receptor subunits between the rodent DCN and that of primates. GluR2/3 is widely expressed in the primate DCN. GluR1 is also widely expressed in the primate DCN. GluR4 is diffusely expressed. Expression of GluR2/3 and GluR4 in the primate is similar to that of the rodent. However, expression of GluR1 is different. GluR1 is only expressed by cartwheel cells in the rodent DCN, but is expressed by a variety of cells, including fusiform cells, in the DCN of the primate.

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.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Abstract Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Interneuron classification is an important and long-debated topic in neuroscience. A recent study provided a data set of digitally reconstructed interneurons classified by 42 leading neuroscientists according to a pragmatic classification scheme composed of five categorical variables, namely, of the interneuron type and four features of axonal morphology. From this data set we now learned a model which can classify interneurons, on the basis of their axonal morphometric parameters, into these five descriptive variables simultaneously. Because of differences in opinion among the neuroscientists, especially regarding neuronal type, for many interneurons we lacked a unique, agreed-upon classification, which we could use to guide model learning. Instead, we guided model learning with a probability distribution over the neuronal type and the axonal features, obtained, for each interneuron, from the neuroscientists’ classification choices. We conveniently encoded such probability distributions with Bayesian networks, calling them label Bayesian networks (LBNs), and developed a method to predict them. This method predicts an LBN by forming a probabilistic consensus among the LBNs of the interneurons most similar to the one being classified. We used 18 axonal morphometric parameters as predictor variables, 13 of which we introduce in this paper as quantitative counterparts to the categorical axonal features. We were able to accurately predict interneuronal LBNs. Furthermore, when extracting crisp (i.e., non-probabilistic) predictions from the predicted LBNs, our method outperformed related work on interneuron classification. Our results indicate that our method is adequate for multi-dimensional classification of interneurons with probabilistic labels. Moreover, the introduced morphometric parameters are good predictors of interneuron type and the four features of axonal morphology and thus may serve as objective counterparts to the subjective, categorical axonal features.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types.

Importance of newly generated neurons in the adult olfactory bulb for odor discrimination

In adult rodents, neurons are continually generated in the subventricular zone of the forebrain, from where they migrate tangentially toward the olfactory bulb, the only known target for these neuronal precursors. Within the main olfactory bulb, they ascend radially into the granule and periglomerular cell layers, where they differentiate mainly into local interneurons. The functional consequences of this permanent generation and integration of new neurons into existing circuits are unknown. To address this question, we used neural cell adhesion molecule-deficient mice that have documented deficits in the migration of olfactory-bulb neuron precursors, leading to about 40% size reduction of this structure. Our anatomical study reveals that this reduction is restricted to the granule cell layer, a structure that contains exclusively γ-aminobutyric acid (GABA)ergic interneurons. Furthermore, mutant mice were subjected to experiments designed to examine the behavioral consequences of such anatomical alteration. We found that the specific reduction in the newly generated interneuron population resulted in an impairment of discrimination between odors. In contrast, both the detection thresholds for odors and short-term olfactory memory were unaltered, demonstrating that a critical number of bulbar granule cells is crucial only for odor discrimination but not for general olfactory functions.