A model for generating synthetic dendrites of cortical neurons

One of the main challenges in neuroscience is to define the detailed structural design of the nervous system. This challenge is one of the first steps towards understanding how neural circuits contribute to the functional organization of the nervous system. In the cerebral cortex pyramidal neurons are key elements in brain function as they represent the most abundant cortical neuronal type and the main source of cortical excitatory synapses. Therefore, many researchers are interested in the analysis of the microanatomy of pyramidal cells since it constitutes an excellent tool for better understanding cortical processing of information. Computational models of neuronal networks based on real cortical circuits have become useful tools for studying certain aspects of the functional organization of the neocortex. Neuronal morphologies (morphological models) represent key features in these functional models. For these purposes, synthetic or virtual dendritic trees can be generated through a morphological model of a given neuronal type based on real morphometric parameters obtained from intracellularly-filled single neurons. This paper presents a new method to construct virtual dendrites by means of sampling a branching model that represents the dendritic morphology. This method has been contrasted using complete basal dendrites from 374 layer II/III pyramidal neurons of the mouse neocortex

One rule to grow them all: A general theory of neuronal branching and its practical application

Understanding the principles governing axonal and dendritic branching is essential for unravelling the functionality of single neurons and the way in which they connect. Nevertheless, no formalism has yet been described which can capture the general features of neuronal branching. Here we propose such a formalism, which is derived from the expression of dendritic arborizations as locally optimized graphs. Inspired by Ramon y Cajal's laws of conservation of cytoplasm and conduction time in neural circuitry, we show that this graphical representation can be used to optimize these variables. This approach allows us to generate synthetic branching geometries which replicate morphological features of any tested neuron. The essential structure of a neuronal tree is thereby captured by the density profile of its spanning field and by a single parameter, a balancing factor weighing the costs for material and conduction time. This balancing factor determines a neuron's electrotonic compartmentalization. Additions to this rule, when required in the construction process, can be directly attributed to developmental processes or a neuron's computational role within its neural circuit. The simulations presented here are implemented in an open-source software package, the "TREES toolbox," which provides a general set of tools for analyzing, manipulating, and generating dendritic structure, including a tool to create synthetic members of any particular cell group and an approach for a model-based supervised automatic morphological reconstruction from fluorescent image stacks. These approaches provide new insights into the constraints governing dendritic architectures. They also provide a novel framework for modelling and analyzing neuronal branching structures and for constructing realistic synthetic neural networks

Preserving neural function under extreme scaling

Important brain functions need to be conserved throughout organisms of extremely varying sizes. Here we study the scaling properties of an essential component of computation in the brain: the single neuron. We compare morphology and signal propagation of a uniquely identifiable interneuron, the HS cell, in the blowfly (Calliphora) with its exact counterpart in the fruit fly (Drosophila) which is about four times smaller in each dimension. Anatomical features of the HS cell scale isometrically and minimise wiring costs but, by themselves, do not scale to preserve the electrotonic behaviour. However, the membrane properties are set to conserve dendritic as well as axonal delays and attenuation as well as dendritic integration of visual information. In conclusion, the electrotonic structure of a neuron, the HS cell in this case, is surprisingly stable over a wide range of morphological scales

Behavioral and Other Phenotypes in a Cytoplasmic Dynein Light Intermediate Chain 1 Mutant Mouse

The cytoplasmic dynein complex is fundamentally important to all eukaryotic cells for transporting a variety of essential cargoes along microtubules within the cell. This complex also plays more specialized roles in neurons. The complex consists of 11 types of protein that interact with each other and with external adaptors, regulators and cargoes. Despite the importance of the cytoplasmic dynein complex, we know comparatively little of the roles of each component protein, and in mammals few mutants exist that allow us to explore the effects of defects in dynein-controlled processes in the context of the whole organism. Here we have taken a genotype-driven approach in mouse (Mus musculus) to analyze the role of one subunit, the dynein light intermediate chain 1 (Dync1li1). We find that, surprisingly, an N235Y point mutation in this protein results in altered neuronal development, as shown from in vivo studies in the developing cortex, and analyses of electrophysiological function. Moreover, mutant mice display increased anxiety, thus linking dynein functions to a behavioral phenotype in mammals for the first time. These results demonstrate the important role that dynein-controlled processes play in the correct development and function of the mammalian nervous system

Spatial embedding of neuronal trees modeled by diffusive growth

The relative importance of the intrinsic and extrinsic factors determining the variety of geometric shapes exhibited by dendritic trees remains unclear. This question was addressed by developing a model of the growth of dendritic trees based on diffusion-limited aggregation process. The model reproduces diverse neuronal shapes (i.e., granule cells, Purkinje cells, the basal and apical dendrites of pyramidal cells, and the axonal trees of interneurons) by changing only the size of the growth area, the time span of pruning, and the spatial concentration of 'neurotrophic particles'. Moreover, the presented model shows how competition between neurons can affect the shape of the dendritic trees. The model reveals that the creation of complex (but reproducible) dendrite-like trees does not require precise guidance or an intrinsic plan of the dendrite geometry. Instead, basic environmental factors and the simple rules of diffusive growth adequately account for the spatial embedding of different types of dendrites observed in the cortex. An example demonstrating the broad applicability of the algorithm to model diverse types of tree structures is also presented. Key words: Diffusion-limited aggregation; Neuronal morphology; Dendrites; Growth model; tree; dendritic geometry.Comment: 9 pages, 6 figure

The Laminar Architecture of Visual Cortex and Image Processing Technology

The mammalian neocortex is organized into layers which include circuits that form functional columns in cortical maps. A major unsolved problem concerns how bottom-up, top-down, and horizontal interactions are organized within cortical layers to generate adaptive behaviors. This article summarizes a model, called the LAMINART model, of how these interactions help visual cortex to realize: (1) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex stably grows and tunes its circuits to match environmental constraints. Such Laminar Computing completes perceptual groupings that realize the property of Analog Coherence, whereby winning groupings bind together their inducing features without losing their ability to represent analog values of these features. Laminar Computing also efficiently unifies the computational requirements of preattentive filtering and grouping with those of attentional selection. It hereby shows how Adaptive Resonance Theory (ART) principles may be realized within the laminar circuits of neocortex. Applications include boundary segmentation and surface filling-in algorithms for processing Synthetic Aperture Radar images.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); Office of Naval Research (N00014-95-1-0657

Pathological progression induced by the frontotemporal dementia-associated R406W tau mutation in patient-derived iPSCs

Mutations in the microtubule-associated protein tau (MAPT) gene are known to cause familial frontotemporal dementia (FTD). The R406W tau mutation is a unique missense mutation whose patients have been reported to exhibit Alzheimer\u27s disease (AD)-like phenotypes rather than the more typical FTD phenotypes. In this study, we established patient-derived induced pluripotent stem cell (iPSC) models to investigate the disease pathology induced by the R406W mutation. We generated iPSCs from patients and established isogenic lines using CRISPR/Cas9. The iPSCs were induced into cerebral organoids, which were dissociated into cortical neurons with high purity. In this neuronal culture, the mutant tau protein exhibited reduced phosphorylation levels and was increasingly fragmented by calpain. Furthermore, the mutant tau protein was mislocalized and the axons of the patient-derived neurons displayed morphological and functional abnormalities, which were rescued by microtubule stabilization. The findings of our study provide mechanistic insight into tau pathology and a potential for therapeutic intervention

Context-aware modeling of neuronal morphologies

© 2014 Torben-Nielsen and De Schutter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these termsNEURONAL MORPHOLOGIES ARE PIVOTAL FOR BRAIN FUNCTIONING: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.Peer reviewe

Behavioral and other phenotypes in a cytoplasmic Dynein light intermediate chain 1 mutant mouse

The cytoplasmic dynein complex is fundamentally important to all eukaryotic cells for transporting a variety of essential cargoes along microtubules within the cell. This complex also plays more specialized roles in neurons. The complex consists of 11 types of protein that interact with each other and with external adaptors, regulators and cargoes. Despite the importance of the cytoplasmic dynein complex, we know comparatively little of the roles of each component protein, and in mammals few mutants exist that allow us to explore the effects of defects in dynein-controlled processes in the context of the whole organism. Here we have taken a genotype-driven approach in mouse (Mus musculus) to analyze the role of one subunit, the dynein light intermediate chain 1 (Dync1li1). We find that, surprisingly, an N235Y point mutation in this protein results in altered neuronal development, as shown from in vivo studies in the developing cortex, and analyses of electrophysiological function. Moreover, mutant mice display increased anxiety, thus linking dynein functions to a behavioral phenotype in mammals for the first time. These results demonstrate the important role that dynein-controlled processes play in the correct development and function of the mammalian nervous system