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.
AnyWave is a free, multi-platform software that can be used to visualize electrophysiological data, as
well as being used as a development framework in order to build custom plug-ins.
AnyWave uses plug-ins to load or write files formats. A set of reader and writer plug-ins is bundled
with AnyWave and brings the possibility to read several EEG or MEG manufacturers’ formats.
The plug-ins are also used to add entirely new signal processing, data analysis and visualization
capabilities to AnyWave.
AnyWave opens and displays the contents of EEG or MEG files. Acquired signals are then displayed by
AnyWave as well as markers that might be stored in the file.
Markers can be read from a file, added by the user or even by a signal processing plug-in.
Markers can be saved to or loaded from a specific AnyWave format.
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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.
A Structure Tensor Analysis (STA) tool for the characterization of local 3D orientation in TIFF image stacks. This tool is based on the evaluation of local image intensity gradients. In addition to the local 3D orientation, it also provides a full analysis of local gradient strength, structure disarray, shape and fractional anisotropy indices.