3D Slicer is: A software platform for the analysis (including registration and interactive segmentation) and visualization (including volume rendering) of medical images and for research in image guided therapy. A free, open source software available on multiple operating systems: Linux, MacOSX and Windows Extensible, with powerful plug-in capabilities for adding algorithms and applications. Features include: Multi organ: from head to toe. Support for multi-modality imaging including, MRI, CT, US, nuclear medicine, and microscopy. Bidirectional interface for devices. There is no restriction on use, but Slicer is not approved for clinical use and intended for research. Permissions and compliance with applicable rules are the responsibility of the user.
NeuroScheme uses schematic representations, such as icons and glyphs to encode attributes of neural structures (i.e. neurons, columns, layers, populations, etc.). This abstraction alleviates problems with displaying, navigating, and analysing, large datasets. It has been designed specifically to manage hierarchically organised neural structures; one can navigate through the levels of the hierarchy, and hone in on their desired level of details. NeuroScheme works using what we call "domains". These domains specify which entities, attributes and relationships are going to be used for a specific use case. NeuroScheme currently has two built-in domains: “cortex” and “congen”. The “cortex” domain is designed for navigating and analysing cerebral cortex structures (i.e. neurons, micro-columns, columns, layers, etc.). The “congen” domain can be used to define the properties of both cells and connections, create circuits composed of neurons, and build populations. Groups of populations can be easily moved to a higher level of abstraction (such as column or layer), allowing one to create complex networks with little effort. These circuits can be exported afterwards and used for further analysis and simulations.
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A MATLAB® toolbox that given a three-dimensional spine reconstruction computes a set of characteristic morphological measures that unequivocally determine the spine shape.
Dendritic spines of pyramidal neurons are the targets of most excitatory synapses in the cerebral cortex and their morphology appears to be critical from the functional point of view. Thus, characterizing this morphology is necessary to link structural and functional spine data and thus interpret and make them more meaningful. We have used a large database of more than 7,000 individually 3D reconstructed dendritic spines from human cortical pyramidal neurons that is first transformed into a set of 54 quantitative features characterizing spine geometry mathematically. The resulting data set is grouped into spine clusters based on a probabilistic model with Gaussian finite mixtures. We uncover six groups of spines whose discriminative characteristics are identified with machine learning methods as a set of rules. The clustering model allows us to simulate accurate spines from human pyramidal neurons to suggest new hypotheses of the functional organization of these cells.