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26 27 </head> 28 <body><div id="wrapper"><div id="content"><div id="page"><div class="content-div"><div class="navbar navbar-default" id="navbar-top-neurosuite" role="navigation"><div class="menu_block"><div class="container clearfix" id="menu-neurosuites"><div class="logo pull-left"><a class="link-no-decorated" href="/"><span><img alt="NeuroSuites logo" id="neuroSuite_logo_withText" src="/static/neurosuite/img/neurosuites_logo_withText_small.png"/></span></a> <span id="version-number-menu"><a class="link-no-decorated" href="/#what_is_new" id="version-number-menu-link">v1.3 - 22/06/2020</a></span></div><div class="pull-right"><nav class="navmenu center"><ul><li class="active"><a href="/morpho/"><span>Machine learning</span></a></li><li class=""><a href="/micro/"><span>Microscopy images</span></a></li><li class=""><a href="/about/"><span>About NeuroSuites</span></a></li></ul></nav></div></div></div></div></div></div><div id="content-neurosuite"><meta charset="utf-8"/><title>Morphometric analyzer of neurons | NeuroSuites</title><meta content="You can upload your own neurons files or you can select the neurons in the NeuroMorpho.org database. 29 Then you can use our multiple online tools to analyze the neurons." name="description"/>
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29<link href="/static/neurosuite/css/flex-3-columns.css" rel="stylesheet" type="text/css"/><link href="/static/neurosuite/css/global.css" rel="stylesheet" type="text/css"/><div class="column-sidebar"><nav class="sidebar-neurosuite" id="sidebar-select-neurons"><div class="sidebar-header"><h4>Step 1</h4><h3>Select the source of the dataset</h3></div><ul class="list-unstyled components not-collapse"><li id="neuromorpho"><a href="/morpho/select_neuromorpho_neurons">NeuroMorpho.org</a></li><li id="upload_neurons"><a href="/morpho/select_upload_neurons">Upload dataset from your computer</a></li><li id="continue_without_neurons"><a href="/morpho/select_without_neurons">Continue without selecting a dataset</a></li><li id="demo"><a href="/morpho/select_demo_neurons">Demo</a></li></ul><ul class="list-unstyled CTAs not-collapse"><li id="instructions"><a href="/morpho/">Instructions</a></li></ul></nav></div>
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37<div class="column-content dark-background"><div class="container-center"><h2>Instructions</h2></div><div class="row dark-blue-background"><div class="col-xs-12 col-sm-12 col-md-6 col-lg-12"><div class="container-center"><br/></div><div class="col-xs-12 col-sm-12 col-md-4 col-lg-8"><div class="row"><div class="container-center"><span><img alt="NeuroSuites logo" class="instructions_logo" src="/static/neurosuite/img/neuroSuite_logo_negative.png"/></span></div></div></div><div class="col-xs-12 col-sm-12 col-md-4 col-lg-4"><div class="container-center"><span><a href="/morpho/select_upload_neurons"><button class="btn btn-success instruction_button" type="button">Upload your own dataset</button></a></span> <br/> <span>The following file types are supported: swc, dat, asc or json</span> <br/> <br/> <br/> <span><a href="/morpho/select_without_neurons"><button class="btn btn-info instruction_button" type="button">Continue without a dataset</button></a></span> <span>if you want to use the following software without selecting neurons: <a href="/morpho/morpho_spineSimulation">3DspineS</a>, <a href="/morpho/morpho_somaMS">3DSomaMS</a>, <a href="/morpho/morpho_synapsesSA">3DSynapsesSA</a>, <a href="/morpho/morpho_hbp_dendrite_arborization_simulation">Dendrite arborization simulation</a></span> <br/> <br/> <span><a href="/morpho/select_demo_neurons"><button class="btn btn-info instruction_button" type="button">Demo data</button></a></span> <span>to analyze some random neurons in order to understand how NeuroSuites works.</span> <br/> <br/></div></div></div></div><ul class="list-group"><li class="list-group-item"><strong><a href="/morpho/select_neuromorpho_neurons">NeuroMorpho.org</a>:</strong> search through the NeuroMorpho.org database to get neurons.</li></ul><hr/><div class="row"><div class="col-xs-12 col-sm-12 col-md-2 col-lg-2"></div><div class="col-xs-12 col-sm-12 col-md-4 col-lg-4 steps-div"><div class="container-center step-div"><svg class="bi bi-file-check" fill="currentColor" height="8em" viewBox="0 0 16 16" width="8em" xmlns="http://www.w3.org/2000/svg"><path d="M9 1H4a2 2 0 0 0-2 2v10a2 2 0 0 0 2 2h8a2 2 0 0 0 2-2V8h-1v5a1 1 0 0 1-1 1H4a1 1 0 0 1-1-1V3a1 1 0 0 1 1-1h5V1z"></path><path d="M15.854 2.146a.5.5 0 0 1 0 .708l-3 3a.5.5 0 0 1-.708 0l-1.5-1.5a.5.5 0 0 1 .708-.708L12.5 4.793l2.646-2.647a.5.5 0 0 1 .708 0z" fill-rule="evenodd"></path></svg><br/><h4>Step 1</h4>Select or not a dataset</div></div><div class="col-xs-12 col-sm-12 col-md-4 col-lg-4 steps-div"><div class="container-center step-div"><svg class="bi bi-clipboard-data" fill="currentColor" height="8em" viewBox="0 0 16 16" width="8em" xmlns="http://www.w3.org/2000/svg"><path d="M4 1.5H3a2 2 0 0 0-2 2V14a2 2 0 0 0 2 2h10a2 2 0 0 0 2-2V3.5a2 2 0 0 0-2-2h-1v1h1a1 1 0 0 1 1 1V14a1 1 0 0 1-1 1H3a1 1 0 0 1-1-1V3.5a1 1 0 0 1 1-1h1v-1z" fill-rule="evenodd"></path><path d="M9.5 1h-3a.5.5 0 0 0-.5.5v1a.5.5 0 0 0 .5.5h3a.5.5 0 0 0 .5-.5v-1a.5.5 0 0 0-.5-.5zm-3-1A1.5 1.5 0 0 0 5 1.5v1A1.5 1.5 0 0 0 6.5 4h3A1.5 1.5 0 0 0 11 2.5v-1A1.5 1.5 0 0 0 9.5 0h-3z" fill-rule="evenodd"></path><path d="M4 11a1 1 0 1 1 2 0v1a1 1 0 1 1-2 0v-1zm6-4a1 1 0 1 1 2 0v5a1 1 0 1 1-2 0V7zM7 9a1 1 0 0 1 2 0v3a1 1 0 1 1-2 0V9z"></path></svg><br/><h4>Step 2</h4>Select an application and analyze the data</div></div></div><br/><div class="container-center"><h2>List of applications</h2></div><div aria-label="..." class="btn-group btn-group-justified" role="group"><div class="btn-group" role="group"><button class="btn btn-primary btn-lg" id="all" type="button">All</button></div><div class="btn-group" role="group"><button class="btn btn-primary btn-lg" id="neuro" type="button">Neuroscience</button></div><div class="btn-group" role="group"><button class="btn btn-primary btn-lg" id="general" type="button">General Purpose</button></div><div class="btn-group" role="group"><button class="btn btn-primary btn-lg" id="with-data" type="button">With dataset</button></div><div class="btn-group" role="group"><button class="btn btn-primary btn-lg" id="no-data" type="button">Without dataset</button></div></div><div class="dark-blue-background"><div class="container-flex-box"><div class="item-flex-box panel panel-primary neuro with-data app"><div class="panel-heading"><h4 class="" id="">L-Measure - Extract morphological measurements</h4></div><div class="panel-body"><p>This tool allows researchers to extract quantitative morphological measurements from neuronal reconstructions.
37<br/><br/> Neuronal reconstructions are typically obtained from brightfield or fluorescence microscopy preparations using applications such as Neurolucida, Eutectic, or Neuron_Morpho, or can be synthesized via computational simulations.</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="http://cng.gmu.edu:8080/Lm/help/index.htm" target="_blank">L-Measure documentation</a></span></p></div><img src="/static/neurosuite/img/l_measure_all_demo.png"/></div></div><div class="item-flex-box panel panel-primary neuro with-data app"><div class="panel-heading"><h4 class="" id="">NeuroViewer - 3D Neuron reconstruction</h4></div><div class="panel-body"><p>3D Neuron reconstruction visualization package</p><p>NeuroSTR was originally created by Luis Rodriguez-Lujan.</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://github.com/lrodriguezlujan/neuroviewer" target="_blank">NeuroViewer repository</a></span></p></div><img src="/static/neurosuite/img/neuroviewer_individual_demo.png"/></div></div><div class="item-flex-box panel panel-primary neuro with-data app"><div class="panel-heading"><h4 class="" id="">NeuroSTR - Validator, format converter</h4></div><div class="panel-body"><p>NeuroSTR is a neuroanatomy toolbox for C++. It reads and processes three-dimensional neuron reconstructions in the most common file formats and offers a huge set of functions and utilities to work with them.</p><p>NeuroSTR was originally created by Luis Rodriguez-Lujan and is currently maintained by Bojan Mihaljevic.</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://computationalintelligencegroup.github.io/neurostr/doc/tools.html#validator" target="_blank">NeuroSTR validator documentation.</a><br/></span> <span class="link-box-rounded"><a href="https://computationalintelligencegroup.github.io/neurostr/doc/tools.html#converter" target="_blank">NeuroSTR format converter documentation.</a></span></p></div><img src="/static/neurosuite/img/neurostr_validator_neuron_demo.png"/><img src="/static/neurosuite/img/neurostr_converter_demo.png"/></div></div><div class="item-flex-box panel panel-primary neuro with-data app"><div class="panel-heading"><h4 class="" id="">3DBasalRM - Repair cut-points in the basal arborization</h4></div><div class="panel-body"><p>Data-driven repairing model that detects cut-points in the basal arborization and then repairs them using a growth model built from complete three-dimensional neuron reconstructions.<br/> As result a neuron in JSON format is returned</p><p>
373DBasalRM was originally created by Sergio Luengo.</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://github.com/ComputationalIntelligenceGroup/3DBasalRM/blob/master/vignettes/2.Reparation.Rmd" target="_blank">3DBasalRM documentation.</a></span></p></div><img src="/static/neurosuite/img/3dbasalrm_demo.png"/></div></div><div class="item-flex-box panel panel-primary neuro with-data app"><div class="panel-heading"><h4 class="" id="">GabaClassifier - Interneuron classifier</h4></div><div class="panel-body"><p>Classifies the given interneuron morphology into one of the 7 possible classes.<br/><br/> The model has been trained with layer L2/3 to layer L6 interneurons and thus only interneurons from those layers are allowed as input.<br/> Click <a href="https://doi.org/10.1038/nrn1519">here</a> to learn more about the interneuron classes)</p><p>GabaClassifier was originally created by Bojan Mihaljevic</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://github.com/ComputationalIntelligenceGroup/gabaclassifier" target="_blank">GabaClassifier documentation.</a></span></p></div><img src="/static/neurosuite/img/gabaclassifier_output.png"/></div></div><div class="item-flex-box panel panel-primary general with-data app"><div class="panel-heading"><h4 class="" id="">Statistics engine</h4></div><div class="panel-body"><p>Discrete and continuous data are supported. <br/> <br/> Descriptive statistics:univariate, bivariate and multivariate analysis and visualization. <br/> Inferential statistics: confidence intervals, hypothesis testing (one sample t-test, two dependent and independent samples t-test), find fittest distribution. <br/> <br/> Interactive plots with <a href="https://plot.ly/#/">Plotly</a> (histograms, probability density functions, box plots, 2D and 3D scatter plots, Chernoff faces, Radar charts, Parallel coordinates, Andrew curves and much more!), custom options, exporting formats, etc. <br/> Everything online. <br/> <br/> Check out the Data stats or L-Measure tool in the Morphometric Analyzer to see it in action.</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://plot.ly/python/" target="_blank">Plotly documentation for Python</a></span></p></div><div class="container-center"><img src="/static/neurosuite/img/probability_density_functions_plot.png"/></div></div></div><div class="item-flex-box panel panel-primary general with-data app"><div class="panel-heading"><h4 class="" id="">Machine learning</h4></div><div class="panel-body"><p><a href="/morpho/ml_bayesian_networks/" target="_blank">Bayesian Networks</a>: structure and parameters learning for continuous and discrete datasets. Full visualization and inference for continuous BNs. <br/> <br/> <a href="/morpho/ml_probabilistic_clustering" target="_blank">Probabilistic clustering graphical models</a>: full visualization and inference for continuous models. <br/> <br/> Everything online. <br/> <br/> Check out the Machine learning section in the Morphometric Analyzer to see it in action.</p><div class="container-center"><img src="/static/neurosuite/img/bns/net1_hubs_prunning.png"/></div></div></div><div class="item-flex-box panel panel-primary neuro no-data app"><div class="panel-heading"><h4 class="" id=""><a class="link-heading-panel" href="/morpho/morpho_spineSimulation">3DspineS - Dendritic spine simulation</a></h4></div><div class="panel-body"><p>This mathematical approach could provide a useful tool for theoretical predictions on the functional features of human pyramidal neurons based on the morphology of dendritic spines.<br/> This tool was trained with human cortical pyramidal neurons.</p><p>
373DspineS was originally created by Sergio Luengo.</p><p><i>Luengo-Sanchez, S., Fernaud-Espinosa, I., Bielza, C., Benavides-Piccione, R., Larrañaga, P., & DeFelipe, J. (2018). <br/> 3D morphology-based clustering and simulation of human pyramidal cell dendritic spines. <br/> PLOS Computational Biology, 14(6), e1006221. <a href="https://doi.org/10.1371/journal.pcbi.1006221">https://doi.org/10.1371/journal.pcbi.1006221</a></i></p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://github.com/ComputationalIntelligenceGroup/3DspineS" target="_blank">3DspineS documentation</a></span></p></div><img src="/static/neurosuite/img/3dspine3.png"/></div></div><div class="item-flex-box panel panel-primary neuro no-data app"><div class="panel-heading"><h4 class="" id=""><a class="link-heading-panel" href="/morpho/morpho_somaMS">3DSomaMS - Delimit the neuronal soma</a></h4></div><div class="panel-body"><p>The definition of the soma is fuzzy, as there is no clear line demarcating the soma of the labeled neurons and the origin of the dendrites and axon. Thus, the morphometric analysis of the neuronal soma is highly subjective.</p><p>This software provides a mathematical definition and an automatic segmentation method to delimit the neuronal soma. We applied this method to the characterization of pyramidal cells, which are the most abundant neurons in the cerebral cortex. Thus, this software is a means of characterizing pyramidal neurons in order to objectively compare the morphometry of the somata of these neurons in different cortical areas and species.</p><p>
373DSomaMS was originally created by Sergio Luengo and Luis Rodriguez-Lujan (GUI).</p><p><i>Luengo-Sanchez, S., Bielza, C., Benavides-Piccione, R., Fernaud-Espinosa, I., DeFelipe, J., & Larrañaga, P. (2015). <br/> A univocal definition of the neuronal soma morphology using Gaussian mixture models. <br/> Frontiers in Neuroanatomy, 9, 137. <a href="https://doi.org/10.3389/fnana.2015.00137">https://doi.org/10.3389/fnana.2015.00137</a></i></p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href,="https://github.com/ComputationalIntelligenceGroup/3DSomaMS" target="_blank">3DSomaMS repository</a></span> <span class="link-box-rounded"><a href="/static/neurosuite/docs/3DSomaMS/3DSomaMS_User_Guide.pdf" target="_blank">3DSomaMS User's guide</a></span> <span class="link-box-rounded"><a href="/static/neurosuite/docs/3DSomaMS/3DSomaMS_tutorial_v1.0.pptx" target="_blank">3DSomaMS Tutorial</a></span></p></div><img src="/static/neurosuite/img/3DSomaMS_demo3D.png"/></div></div><div class="item-flex-box panel panel-primary neuro no-data app"><div class="panel-heading"><h4 class="" id=""><a class="link-heading-panel" href="/morpho/morpho_synapsesSA">3DSynapsesSA - Analyze spatial distribution of cortical synapses</a></h4></div><div class="panel-body"><p>3DSynapsesSA is a tool designed to process and analyze patterns in the three-dimensional spatial distribution of cortical synapses. It brings a variety of both innovative and well-known techniques from the spatial statistics field.</p><p>This tool allows you to: <br/> <i class="fa fa-square icon-margin-right"></i>Process and visualize data from cortical synapses for error checking<br/> <i class="fa fa-square icon-margin-right"></i>Model the spatial distribution of the synapses<br/> <i class="fa fa-square icon-margin-right"></i>Replicate, via simulation, samples of cortical synapses<br/> <i class="fa fa-square icon-margin-right"></i>Compare several indicators obtained from data of different layers<br/></p><p>3DSynapsesSA was originally created by Laura Antón-Sánchez and Luis Rodriguez-Lujan (GUI).</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://github.com/ComputationalIntelligenceGroup/3DSynapsesSA" target="_blank">3DSynapsesSA repository</a></span> <span class="link-box-rounded"><a href="/static/neurosuite/docs/3DSynapsesSA/3DSynapsesSA_users_guide_v1.1.pdf" target="_blank">
373DSynapsesSA User's guide</a></span> <span class="link-box-rounded"><a href="/static/neurosuite/docs/3DSynapsesSA/3DSynapsesSA_tutorial_v1.1.pptx" target="_blank">3DSynapsesSA Tutorial</a></span></p></div><div class="container-center"><img src="/static/neurosuite/img/3DSynapsesSA_demo3D.png"/></div></div></div><div class="item-flex-box panel panel-primary single-element-in-row neuro no-data app"><div class="panel-heading"><h4 class="" id=""><a class="link-heading-panel" href="/morpho/morpho_hbp_dendrite_arborization_simulation">Dendrite arborization simulation - Generate synthetic dendrite arborization</a></h4></div><div class="panel-body"><p>Generation of synthetic neurons with soma and dendrites.</p><p>Dendrite arborization simulation was originally created by Pablo Fernández González</p><div class="container-center"><p class="p-no-margin"><span class="link-box-rounded"><a href="https://github.com/ComputationalIntelligenceGroup/hbp-dendrite-arborization-simulation" target="_blank">Dendrite arborization simulation repository</a></span></p></div><div class="container-center"><img class="img-hbp-dendrite-arborization-simulation-demo3D" src="/static/neurosuite/img/hbp-dendrite-arborization-simulation-demo3D.png"/></div></div></div></div></div></div><button class="btn btn-info navbar-btn" id="sidebarCollapse" type="button"><i class="glyphicon glyphicon-align-left"></i> <span>Menu</span></button></div><footer></footer></div>
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