The pyramidal cell in cognition: A comparative study in human and monkey

Here we present evidence that the pyramidal cell phenotype varies markedly in the cortex of different anthropoid species. Regional and species differences in the size of, number of bifurcations in, and spine density of the basal dendritic arbors cannot be explained by brain size. Instead, pyramidal cell morphology appears to accord with the specialized cortical function these cells perform. Cells in the prefrontal cortex of humans are more branched and more spinous than those in the temporal and occipital lobes. Moreover, cells in the prefrontal cortex of humans are more branched and more spinous than those in the prefrontal cortex of macaque and marmoset monkeys. These results suggest that highly spinous, compartmentalized, pyramidal cells (and the circuits they form) are required to perform complex cortical functions such as comprehension, perception, and planning

A Method for the Symbolic Representation of Neurons

The field of neuroanatomy has progressed considerably in recent decades, thanks to the emergence of novel methods which provide new insights into the organization of the nervous system. These new methods have produced a wealth of data that needs to be analyzed, shifting the bottleneck from the acquisition to the analysis of data. In other disciplines, such as in many engineering areas, scientists and engineers are dealing with increasingly complex systems, using hierarchical decompositions, graphical models and simplified schematic diagrams for analysis and design processes. This approach makes it possible for users to simultaneously combine global system views and very detailed representations of specific areas of interest, by selecting appropriate representations for each of these views. In this way, users can concentrate on specific details while also maintaining a general system overview — a capability that is essential for understanding structure and function whenever complexity is an issue. Following this approach, this paper focuses on a graphical tool designed to help neuroanatomists to better understand and detect morphological characteristics of neuronal cells. The method presented here, based on a symbolic representation that can be tailored to enhance a particular range of features of a neuron or neuron set, has proven to be useful for highlighting particular geometries that may be hidden due to the complexity of the analysis tasks and the richness of neuronal morphologies. A software tool has been developed to generate graphical representations of neurons from 3D computer-aided reconstruction files

Ultrastructure of Dendritic Spines: Correlation Between Synaptic and Spine Morphologies

Dendritic spines are critical elements of cortical circuits, since they establish most excitatory synapses. Recent studies have reported correlations between morphological and functional parameters of spines. Specifically, the spine head volume is correlated with the area of the postsynaptic density (PSD), the number of postsynaptic receptors and the ready-releasable pool of transmitter, whereas the length of the spine neck is proportional to the degree of biochemical and electrical isolation of the spine from its parent dendrite. Therefore, the morphology of a spine could determine its synaptic strength and learning rules

Dendritic size of pyramidal neurons differs among mouse cortical regions

12 pages, 7 figures, 3 tables.-- PMID: 16195469 [PubMed].-- Available online Sep 29, 2005.Neocortical circuits share anatomical and physiological similarities among different species and cortical areas. Because of this, a ‘canonical’ cortical microcircuit could form the functional unit of the neocortex and perform the same basic computation on different types of inputs. However, variations in pyramidal cell structure between different primate cortical areas exist, indicating that different cortical areas could be built out of different neuronal cell types. In the present study, we have investigated the dendritic architecture of 90 layer II/III pyramidal neurons located in different cortical regions along a rostrocaudal axis in the mouse neocortex, using, for the first time, a blind multidimensional analysis of over 150 morphological variables, rather than evaluating along single morphological parameters. These cortical regions included the secondary motor cortex (M2), the secondary somatosensory cortex (S2), and the lateral secondary visual cortex and association temporal cortex (V2L/TeA). Confirming earlier primate studies, we find that basal dendritic morphologies are characteristically different between different cortical regions. In addition, we demonstrate that these differences are not related to the physical location of the neuron and cannot be easily explained assuming rostrocaudal gradients within the cortex. Our data suggest that each cortical region is built with specific neuronal components.R.B.-P. thanks the ‘Comunidad de Madrid’ (01/0782/2000) and I.B.-Y. the MEC (AP2001-0671) for support. J.D. is supported by the Spanish Ministry of Education and Science (BFI2003-02745) and the Comunidad de Madrid (08.5/0027/2001.1). R.Y. thanks the NEI (EY11787) and the John Merck Fund for support and the Cajal Institute for hosting him as a visiting professor.Peer reviewe

Pyramidal Cells in Prefrontal Cortex of Primates: Marked Differences in Neuronal Structure Among 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

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

Bayesian network classifiers for categorizing cortical gABAergic interneurons

Abstract An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1–F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts’ assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell’s label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1–F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52 % accuracy, and single out the number of branches at 180 µm from the soma, the convex hull 2D area, and axonal features F1–F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons

Método para la generación de modelos realistas en tres dimensiones de células neuronales

Esta invención presenta un método para la generación de modelos tridimensionales de células neuronales partiendo de la información morfológica incompleta extraída mediante métodos de muestreo estándar. Los modelos generados incluyen un soma realista, árboles dendríticos y axonales, y espinas dendríticas, pudiendo ser generados a diferentes niveles de resolución. La invención propone una técnica innovadora que permite obtener una forma realista del soma partiendo de una definición simple del mismo (tal como centro y radio) que resulta incompleta para reconstruir la forma 3D original. El método propuesto se basa en la deformación de una forma inicial, guiada por restricciones establecidas de acuerdo a las propiedades morfológicas de los árboles dendríticos y axonales (por ejemplo: posición y grosor de las dendritas de primer orden). La distribución de un conjunto de espinas a lo largo de las dendritas completa el modelo, generando un modelo tridimensional adecuado para su visualización en una amplia gama de entornos 3D.Peer reviewedUniversidad Rey Juan Carlos, Consejo Superior de Investigaciones CientíficasA1 Solicitud de patente con informe sobre el estado de la técnic

Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas

Unraveling pyramidal cell structure is crucial to understanding cortical circuit computations. Although it is well known that pyramidal cell branching structure differs in the various cortical areas, the principles that determine the geometric shapes of these cells are not fully understood. Here we analyzed and modeled with a von Mises distribution the branching angles in 3D reconstructed basal dendritic arbors of hundreds of intracellularly injected cortical pyramidal cells in seven different cortical regions of the frontal, parietal, and occipital cortex of the mouse. We found that, despite the differences in the structure of the pyramidal cells in these distinct functional and cytoarchitectonic cortical areas, there are common design principles that govern the geometry of dendritic branching angles of pyramidal cells in all cortical areas

Dendritic and axonal wiring optimization of cortical GABAergic interneurons

The way in which a neuronal tree expands plays an important role in its functional and computational characteristics. We aimed to study the existence of an optimal neuronal design for different types of cortical GABAergic neurons. To do this, we hypothesized that both the axonal and dendritic trees of individual neurons optimize brain connectivity in terms of wiring length. We took the branching points of real three-dimensional neuronal reconstructions of the axonal and dendritic trees of different types of cortical interneurons and searched for the minimal wiring arborization structure that respects the branching points. We compared the minimal wiring arborization with real axonal and dendritic trees. We tested this optimization problem using a new approach based on graph theory and evolutionary computation techniques. We concluded that neuronal wiring is near-optimal in most of the tested neurons, although the wiring length of dendritic trees is generally nearer to the optimum. Therefore, wiring economy is related to the way in which neuronal arborizations grow irrespective of the marked differences in the morphology of the examined interneurons