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http://www.neuron.yale.edu

NEURON is a simulation environment for modeling individual neurons and networks of neurons. It provides tools for conveniently building, managing, and using models in a way that is numerically sound and computationally efficient. It is particularly well-suited to problems that are closely linked to experimental data, especially those that involve cells with complex anatomical and biophysical properties. NEURON has benefited from judicious revision and selective enhancement, guided by feedback from the growing number of neuroscientists who have used it to incorporate empirically-based modeling into their research strategies. NEURON's computational engine employs special algorithms that achieve high efficiency by exploiting the structure of the equations that describe neuronal properties. It has functions that are tailored for conveniently controlling simulations, and presenting the results of real neurophysiological problems graphically in ways that are quickly and intuitively grasped. Instead of forcing users to reformulate their conceptual models to fit the requirements of a general purpose simulator, NEURON is designed to let them deal directly with familiar neuroscience concepts. Consequently, users can think in terms of the biophysical properties of membrane and cytoplasm, the branched architecture of neurons, and the effects of synaptic communication between cells. * helps users focus on important biological issues rather than purely computational concerns * has a convenient user interface * has a user-extendable library of biophysical mechanisms * has many enhancements for efficient network modeling * offers customizable initialization and simulation flow control * is widely used in neuroscience research by experimentalists and theoreticians * is well-documented and actively supported * is free, open source, and runs on (almost) everything

Proper citation: NEURON (RRID:SCR_005393) Copy   

•   Source: SciCrunch Registry

http://pubbrain.org/

A literature search and visualization tool that allows end users to enter any PubMed query and see that query rendered as a heatmap illustrating which regions of interest are most commonly mentioned within the search results. To use PubBrain, simply enter any valid PubMed search in the search box.

Proper citation: PubBrain (RRID:SCR_005387) Copy   

•   Source: SciCrunch Registry

http://www.neurobureau.org/

A neuroscience collaboratory that supports open neuroscience, which basically encompasses the unrestricted sharing of: analytic tools, computational resources, data, and knowledge. Its goal is to establish a spirit and forum for open neuroscience, and to facilitate the translation of that ethos into action by conducting successful large open interdisciplinary collaborative efforts such as releasing the preprocessed version of the ADHD-200 competition dataset. The Brain-Art Competition is likewise an effort to bring attention to the more aesthetically-oriented aspects of their field, and to publicize and encourage creative developments taking place at the nexus of art and neuroimaging.

Proper citation: Neuro Bureau (RRID:SCR_005357) Copy   

•   Source: SciCrunch Registry

http://enigma.ini.usc.edu/

Network that brings together researchers in imaging genomics, to understand brain structure and function, based on MRI, DTI, fMRI and genomewide association scan (GWAS) data. The ENIGMA Network has several goals: * to create a network of like-minded individuals, interested in pushing forward the field of imaging genetics * to ensure promising findings are replicated via member collaborations, in order to satisfy the mandates of most journals * to share ideas, algorithms, data, and information on promising findings or methods * to facilitate training, including workshops and conferences on key methods and emerging directions in imaging genetics. Data sharing with other members of the ENIGMA Network is optional and by no means a requirement of joining the network. Genetics and Imaging Protocols are available.

Proper citation: ENIGMA: Enhancing Neuro Imaging Genetics Through Meta-Analysis (RRID:SCR_005515) Copy   

•   Source: SciCrunch Registry

http://neuromorphometrics.com

Neuromorphometrics provides brain labeling and measurement services. Given raw MRI brain scans, we make precise quantitative measurements of the volume, shape, and location of specific neuroanatomical structures. Web tool for brain measurement services. Used for modeling living human brain and make quantitative measurements of volume, shape, and location of specific neuroanatomical structures using given MRI brain scans. Automated analyses are manually guided, inspected and certified by a neuroanatomical expert. Resource of neuroanatomically labeled MRI brain scans database. Resource for neuroanatomical localization and identification: NeuAtlas.

Proper citation: Neuromorphometrics (RRID:SCR_005656) Copy   

•   Source: SciCrunch Registry

http://afni.nimh.nih.gov/afni/

Set of (mostly) C programs that run on X11+Unix-based platforms (Linux, Mac OS X, Solaris, etc.) for processing, analyzing, and displaying functional MRI (FMRI) data defined over 3D volumes and over 2D cortical surface meshes. AFNI is freely distributed as source code plus some precompiled binaries.

Proper citation: Analysis of Functional NeuroImages (RRID:SCR_005927) Copy   

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http://www.nitrc.org/projects/nyu_trt/

EPI-images of 25 participants gathered during rest as well as anonymized anatomical images of the same participants. The resting-state fMRI images were collected on several occasions: # the first resting-state scan in a scan session # 5-11 months after the first resting-state scan # about 30 (< 45) minutes after 2. Each scan occasion is released as a new version release of the resource. ---Caution: Participants here are part of the NewYork_a contribution to the 1000 Functional Connectomes Project. DO NOT combine datasets.

Proper citation: NYU CSC TestRetest (RRID:SCR_005975) Copy   

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http://martinos.org/mne/

Software suite for processing magnetoencephalography and electroencephalography data. Open source Python software for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, ECoG . Implements all functionality of MNE Matlab tools in Python and extends capabilities of MNE Matlab tools to, e.g., frequency-domain and time-frequency analyses and non-parametric statistics.

Proper citation: MNE software (RRID:SCR_005972) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/art

ART ''''acpcdetect'''' program for automatic detection of the AC and PC landmarks and the mid-sagittal plane on 3D structural MRI scans. ART ''''brainwash'''' program for automatic multi-atlas skull-stripping of 3D structural MRI scans. ART ''''3dwarper'''' program of non-linear inter-subject registration of 3D structural MRI scans. Software (art2) for linear rigid-body intra-subject inter-modality (MRI-PET) image registration. Data resource: The ART projects makes available corpus callosum segmentations of 316 normal subjects from the OASIS cross-sectional database. ART ''''yuki'''' program for fast, robust, and fully automatic segmentation of the corpus callosum on 3D structural MRI scans.

Proper citation: Automatic Registration Toolbox (RRID:SCR_005993) Copy   

•   Source: SciCrunch Registry

https://analyzedirect.com/

Software toolkit for the visualization, segmentation, registration and analysis of multidimensional biomedical imaging data. With Analyze, both anatomic structure and associated function can be studied and fused together for synergistic display and measurement of important structure-to-function relationships.

Proper citation: Analyze Software System (RRID:SCR_005988) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/arctic

An end-to-end application allowing individual regional analysis of cortical thickness. This cross-platform tool can be run within Slicer3 as an external module, or directly as a command line. * Operating System: MacOS, Linux * Programming Language: C++ * Supported Data Format: ANALYZE, Nrrd, Other Format * build requires: Insight Toolkit

Proper citation: ARCTIC (RRID:SCR_005989) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/toads-cruise/

A collection of software plug-ins developed for the automatic segmentation of magnetic resonance brain images. The tools include multiple published algorithms developed at Johns Hopkins University. The SPECTRE algorithm performs brain extraction. The TOADS algorithm generates a topology-preserving tissue classification into cortical, subcortical, and cerebellar structures. The CRUISE algorithm produces inner, central, and outer cortical surfaces suitable for computing thickness and other geometric measures. Tools are also included for performing gyral labeling, lesion segmentation, thickness computation, surface visualization, and surface file conversion. All tools are released as plug-ins for the MIPAV software package and were developed using the Java Image Science Toolkit (both available at NITRC: http://nitrc.org). They are therefore cross-platform and compatible with a wide variety of file formats.

Proper citation: TOADS-CRUISE Brain Segmentation Tools (RRID:SCR_005977) Copy   

•   Source: SciCrunch Registry

http://mousetracker.jbfreeman.net

A free, user-friendly software package that allows researchers to record and analyze hand movements traveling toward potential responses on the screen (via the x, y coordinates of the computer mouse). By looking at the dynamics of how participants' hand movements settle into a response alternative--and how they may be partially pulled toward other alternatives--researchers glean valuable information about real-time cognitive processing. It's like opening up a single reaction time into a continuous stream of rich cognitive output. MouseTracker has impressive temporal resolution, comparable to eye-tracking and event-related brain potential (ERP) measures. Experiments can incorporate images, letter strings, sounds, and videos. Once recorded, mouse trajectories can be visualized, averaged, and explored, and measures of attraction/curvature, complexity, velocity, and acceleration can be computed. Precise characterizations of mouse trajectories' temporal and spatial dynamics are available, and these can shed light on a variety of important empirical questions across psychology, cognitive science, and beyond.

Proper citation: MouseTracker (RRID:SCR_005979) Copy   

•   Source: SciCrunch Registry

http://www.mitk.org/DiffusionImaging

A selection of image analysis algorithms for the processing of diffusion-weighted MR images. Features & Highlights * Tensor and q-ball reconstruction * Glyph visualization * Quantification and partial volume clustering of tensor and q-ball images * Global fiber tractography, visualization, and tract post-processing * Brain network statistics and visualization (connectomics) * Interactive exploration of Tract-based spatial statistics (TBSS) results * Intra-voxel incoherent motion (IVIM) estimation * Synthetic data generation Additional system specific requirements: * Windows: If you have problems running the Windows application, please install the Microsoft Redistributable Packages for VS 2008: 32 bit or 64 bit * Linux: the Qt framework, version 4.6.2 or later Tested systems: Windows 7, Windows Vista; Ubuntu 12.04 and newer; OS X 10.6 (Snow Leopard), OS X 10.8 (Mountain Lion) The OS X 10.6 installer is compatible with OS X 10.7 (Lion) so there is no dedicated disk image build under 10.7. The MITK Diffusion application is based on the MITK research platform and the most of it is open-source. The available code is embedded into the source code of MITK as a module and can be accessed through the public git repository.

Proper citation: MITK Diffusion (RRID:SCR_006846) Copy   

•   Source: SciCrunch Registry

http://www.brainvoyager.com/products/brainviewer.html

Software that supports browsing and inspecting essential BrainVoyager data files as well as the header and content of DICOM files. The Viewer supports standard image files (JPEG, GIF, PNG, TIFF, BMP) allowing to inspect snapshots, figures or photos. Users can prepare a folder with selected data of a subject (VMRs, SRFs, Maps, snapshot images), which allows participants of fMRI measurements to browse their brain data and to show it to others. The Viewer can be handed over to colleagues not having a BrainVoyager license together with relevant data. This will allow them to view and explore your analyzed data files.

Proper citation: BrainVoyager Brain Viewer (RRID:SCR_006755) Copy   

•   Source: SciCrunch Registry

http://brainmaps.org

An interactive multiresolution brain atlas that is based on over 20 million megapixels of sub-micron resolution, annotated, scanned images of serial sections of both primate and non-primate brains and integrated with a high-speed database for querying and retrieving data about brain structure and function. Currently featured are complete brain atlas datasets for various species, including Macaca mulatta, Chlorocebus aethiops, Felis catus, Mus musculus, Rattus norvegicus, Tyto alba and many other vertebrates. BrainMaps is currently accepting histochemical, immunocytochemical, and tracer connectivity data, preferably whole-brain. In addition, they are interested in EM, MRI, and DTI data.

Proper citation: BrainMaps.org (RRID:SCR_006878) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/mabmis

This software package implements an algorithm for accurate and consistent segmentation / labeling on a group of images. The images should be in Analyze format with paired header and image files. All images should be preprocessed so that they have been affinely aligned together.

Proper citation: MABMIS: Multi-Atlas Based Multi-Image Segmentation (RRID:SCR_006975) Copy   

•   Source: SciCrunch Registry

http://www.brain.org.au/software/mrtrix/

A set of tools to perform diffusion-weighted MRI white matter tractography in the presence of crossing fibres, using Constrained Spherical Deconvolution (Tournier et al.. 2004; Tournier et al. 2007), and a probabilisitic streamlines algorithm (e.g. Behrens et al., 2003; Parker et al., 2003). These applications have been written from scratch in C++, using the functionality provided by the GNU Scientific Library, and gtkmm. The software is currently capable of handling DICOM, NIfTI and AnalyseAVW image formats, amongst others. Installation * Unix/Linux * Microsoft Windows * Mac Os X, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MRtrix (RRID:SCR_006971) Copy   

•   Source: SciCrunch Registry

http://www.ebire.org/hcnlab/software/vamca.html

A stand-alone, open source human cortical meta-analysis and visualization toolbox for MatLab. It projects stereotaxic coordinates to a mean cortical surface by using an anatomical database of 60 young adults to provide multiple mappings of normalized cortical surfaces into MNI space. VAMCA performs the following analyses: # Multi-Fiducial Projection Mapping: Map stereotaxic 3D coordinates to the normalized cortical location for each of 60 database subjects. # Computing Centroid Locations for groups of foci both on a mean cortical surface and in MNI space. # Comparing Two Groups of Foci for differences in location (surface or 3D) of their group centroids and computing the groups' overlap extent using permutation tests. # Detecting Significant Densities of Foci or Density Differences of Two Groups within anatomical ROIs on a mean cortical surface by using Monte Carlo analyses. Coordinate weights allow fixed or random effects type analyses.

Proper citation: VAMCA (RRID:SCR_007028) Copy   

•   Source: SciCrunch Registry

http://www.mlnl.cs.ucl.ac.uk/pronto/

A software toolbox based on pattern recognition techniques for the analysis of neuroimaging data. Statistical pattern recognition is a field within the area of machine learning which is concerned with automatic discovery of regularities in data through the use of computer algorithms, and with the use of these regularities to take actions such as classifying the data into different categories. In PRoNTo, brain scans are treated as spatial patterns and statistical learning models are used to identify statistical properties of the data that can be used to discriminate between experimental conditions or groups of subjects (classification models) or to predict a continuous measure (regression models).

Proper citation: PRoNTo (RRID:SCR_006908) Copy   

•   Source: SciCrunch Registry