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http://www.biological-networks.org/p/outliers/

Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011). This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.

Proper citation: DONE: Detection of Outlier NEurons (RRID:SCR_005299) Copy   

•   Source: SciCrunch Registry

http://ccr.coriell.org/Sections/Collections/NINDS/?SsId=10

Open resource of biological samples (DNA, cell lines, and other biospecimens) and corresponding phenotypic data to promote neurological research. Samples from more than 34,000 unique individuals with cerebrovascular disease, dystonia, epilepsy, Huntington's Disease, motor neuron disease, Parkinsonism, and Tourette Syndrome, as well as controls (population control and unaffected relatives) have been collected. The mission of the NINDS Repository is to provide 1) genetics support for scientists investigating pathogenesis in the central and peripheral nervous systems through submissions and distribution; 2) information support for patients, families, and advocates concerned with the living-side of neurological disease and stroke.

Proper citation: NINDS Repository (RRID:SCR_004520) Copy   

•   Source: SciCrunch Registry

http://od1n.sourceforge.net

A C++ software framework to develop, simulate and run magnetic resonance sequences on different platforms.

Proper citation: Object-Oriented Development Interface for NMR (RRID:SCR_005974) Copy   

•   Source: SciCrunch Registry

http://mcx.sf.net/mmc/

A Monte Carlo (MC) solver for photon migration in 3D turbid media. Different from existing MC software designed for layered (such as MCML) or voxel-based media (such as MMC or tMCimg), MMC can represent a complex domain using a tetrahedral mesh. This not only greatly improves the accuracy of the solutions when modeling objects with smooth/complex boundaries, but also gives an efficient way to sample the problem domain to use less memory. The current version of MMC support multi-threaded programming and can give a almost proportional speed-up when using multiple CPU cores.

Proper citation: Mesh-based Monte Carlo (MMC) (RRID:SCR_006950) Copy   

•   Source: SciCrunch Registry

http://cmic.cs.ucl.ac.uk/mig/index.php?n=Tutorial.NODDImatlab

This MATLAB toolbox implements a data fitting routine for Neurite Orientation Dispersion and Density Imaging (NODDI). NODDI is a new diffusion MRI technique for imaging brain tissue microstructure. Compared to DTI, it has the advantage of providing measures of tissue microstructure that are much more direct and hence more specific. It achieves this by adopting the model-based strategy which relates the signals from diffusion MRI to geometric models of tissue microstructure. In contrast to typical model-based techniques, NODDI is much more clinically feasible and can be acquired on standard MR scanners with an imaging time comparable to DTI.

Proper citation: NODDI Matlab Toolbox (RRID:SCR_006826) Copy   

•   Source: SciCrunch Registry

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

A pure JavaScript medical research image viewer, compatible across a range of popular web browsers. The orthogonal viewer supports NIFTI and DICOM files, overlays and atlas labels. It requires Firefox (7+), Chrome (7+), Safari (6+), MobileSafari (iOS 6+), or IE (10+).

Proper citation: Papaya (RRID:SCR_014188) Copy   

•   Source: SciCrunch Registry

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

A Matlab implementation for efficient permutation testing by using matrix completion.

Proper citation: Efficient Permutation Testing (RRID:SCR_014104) Copy   

•   Source: SciCrunch Registry

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

An effort to provide a set of quasi-probabilistic atlases for established functional ROIs in the human neuroimaging literature. Many atlases exist for various anatomical parcellation schemes, such as the Brodmann areas, the structural atlases, tissue segmentation atlases, etc. To date, however, there is no atlas for so-called functional ROIs. Such fROIs are typically associated with an anatomical label of some kind (e.g. the _fusiform_ face area), but these labels are only approximate and can be misleading inasmuch as fROIs are not constrained by anatomical landmarks, whether cytoarchitectonic or based on sulcal and gyral landmarks. The goal of this project is to provide quasi-probabilistic atlases for fROIs that are based on published coordinates in the neuroimaging literature. This is an open-ended enterprise and the atlas can grow as needed. Members of the neuroscience and neuroimaging community interested in contributing to the project are encouraged to do so.

Proper citation: Functional ROI Atlas (RRID:SCR_009481) Copy   

•   Source: SciCrunch Registry

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

This atlas takes advantage of ultra-high resolution 7T MRI to provide unprecedented levels of detail on structures of the basal ganglia in-vivo. The atlas includes probability maps of the Subthalamic Nucleus (STh) using T2*-imaging. For now it has been created on 13 young healthy participants with a mean age of 24.38 (range: 22-28, SD: 2.36). We recently also created atlas STh probability maps from 8 middle-aged participants with a mean age of 50.67 (range: 40-59, SD: 6.63), and 9 elderly participants with a mean age of 72.33 (range: 67-77, SD: 2.87). You can find more details about the creation of these maps in the following papers: Young: http://www.ncbi.nlm.nih.gov/pubmed/22227131 Middle-aged & Elderly: http://www.ncbi.nlm.nih.gov/pubmed/23486960 Participating institutions are the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, and the Cognitive Science Center Amsterdam, University of Amsterdam, the Netherlands.

Proper citation: Atlasing of the basal ganglia (RRID:SCR_009431) Copy   

•   Source: SciCrunch Registry

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

An atlas intended to provide accurate data in terms of specific uptake location to make the BP quantitation. The VOIs were manually drawn with software Analyze 9.0 (Mayo Clinic) in 18F-DOPA brain image after spatial normalization with a 18F-DOPA Template. Each striatum was divided into 6 sub-regions: ventral caudate, anterior dorsal caudate, posterior dorsal caudate, ventral putamen, anterior dorsal putamen and posterior dorsal putamen.

Proper citation: Striatal Subregional VOImap (RRID:SCR_014173) Copy   

•   Source: SciCrunch Registry

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

The Brain Coactivation Map describes all the coactivation networks in the human brain based on the meta-analysis of more than 5,400 neuroimaging articles (from NeuroSynth) containing more than 16,000 individual experiments. The map can be browsed interactively (CoactivationMap.app on GitHub) or queried from a shell using a command line tool (cmtool on GitHub).

Proper citation: Brain Coactivation Map (RRID:SCR_014172) Copy   

•   Source: SciCrunch Registry

https://as.nyu.edu/research-centers/cbi/resources/Software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Software which converts DICOM images to NIfTI format.

Proper citation: dinifti (RRID:SCR_000303) Copy   

•   Source: SciCrunch Registry

http://www.sci.utah.edu/cibc/software/131-shapeworks.html

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on September 2, 2022. Software that is an open-source distribution of a new method for constructing compact statistical point-based models of ensembles of similar shapes that does not rely on any specific surface parameterization. The method requires very little preprocessing or parameter tuning, and is applicable to a wide range of shape analysis problems, including nonmanifold surfaces and objects of arbitrary topology. The proposed correspondence point optimization uses an entropy-based minimization that balances the simplicity of the model (compactness) with the accuracy of the surface representations. The ShapeWorks software includes tools for preprocessing data, computing point-based shape models, and visualizing the results.

Proper citation: ShapeWorks (RRID:SCR_000424) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/

Biomedical technology resource center specializing in novel approaches and tools for neuroimaging. It develops novel strategies to investigate brain structure and function in their full multidimensional complexity. There is a rapidly growing need for brain models comprehensive enough to represent brain structure and function as they change across time in large populations, in different disease states, across imaging modalities, across age and sex, and even across species. International networks of collaborators are provided with a diverse array of tools to create, analyze, visualize, and interact with models of the brain. A major focus of these collaborations is to develop four-dimensional brain models that track and analyze complex patterns of dynamically changing brain structure in development and disease, expanding investigations of brain structure-function relations to four dimensions.

Proper citation: Laboratory of Neuro Imaging (RRID:SCR_001922) Copy   

•   Source: SciCrunch Registry

https://www.nitrc.org/projects/nitrc_es

An on-demand, cloud based computational virtual machine pre-installed with popular NITRC neuroimaging tools built using NeuroDebian. For a listing of current NITRC-CE packages visit: http://www.nitrc.org/ce-packages. You can also use the "public Amazon Machine Interface (AMI)" to conduct your analyses on the Amazon EC2 platform.

Proper citation: NITRC Computational Environment (RRID:SCR_002171) Copy   

•   Source: SciCrunch Registry

http://sccn.ucsd.edu/wiki/BCILAB

Open Source MATLAB toolbox and EEGLAB plugin for the design, prototyping, testing, experimentation with, and evaluation of Brain-Computer Interfaces (BCIs), and other systems in the same computational framework. It facilitates the design and development of new methods for cognitive state estimation and their use in both offline data analysis and real-time applications. BCILAB includes an easily extensible collection of currently over 100 methods from the literature (covering signal processing, machine learning and BCI-specific methods). Aside from supporting advanced BCI research, a special aim of BCILAB is to facilitate the adoption of machine learning and advanced statistical modeling for functional neuroimaging purposes in tandem with the EEGLAB platform. The toolbox offers multiple different interfaces which link to the same backend functionality, including a GUI, scripting support (MATLAB-based), APIs for real-time processing, and a variety of extension component interfaces. MATLAB programming is not strictly necessary, as most BCILAB features can be accessed from the GUI, although it is required for batch scripting and custom extensions. The strength of MATLAB-based software lies in its resources for leading-edge scientific computing, as well as in the good support for rapid prototyping, but BCI systems developed in it can be used for real-time out-of-lab experimentation, and can in principle be deployed without the need for a MATLAB license. However, due to the complexity and overhead of the MATLAB environment, the system is best used as a research platform, and not as a product development environment -- end-user software is ideally re-implemented in a compiled language, after a suitable approach has been identified and extensively tested. The process of identifying and testing an approach involves more than just computation, but also data exploration and investigation - an area which is helped by the deep integration with the EEGLAB platform. In the future, this integration will be further strengthened, bringing rich statistical learning and signal processing into routine EEG analysis workflows. The toolbox has been developed by C. Kothe at the Swartz Center, inspired by the preceding PhyPA BCI toolbox created by C. Kothe and T. Zander at the Chair for Human-Machine Systems, Berlin Institute of Technology.

Proper citation: BCILAB (RRID:SCR_007013) Copy   

•   Source: SciCrunch Registry

http://www.remedyinformatics.com/

Software to harmonize the data that you have in different Excel files, databases, repositories, biospecimen applications, etc. and maps it to one common registry. Remedy Informatics' platform aggregates data from multiple sources, harmonizes the data via Ontology, and provides data visualization and pattern recognition and querying tools.

Proper citation: Registry Builder Data Harmonization and Aggregation Tool (RRID:SCR_006559) Copy   

•   Source: SciCrunch Registry

http://www.pstnet.com/eprime.cfm

A suite of applications to fulfill all of your computerized experiment needs. Used by more than 15,000 professionals in the research community, E-Prime provides a truly easy-to-use environment for computerized experiment design, data collection, and analysis. E-Prime provides millisecond precision timing to ensure the accuracy of your data. E-Prime's flexibility to create simple to complex experiments is ideal for both novice and advanced users. The E-Prime suite of applications includes: * E-Studio ? Drag and drop graphical interface for experiment design * E-Basic ? Underlying scripting language of E-Prime * E-Run ? Once the experiment is generated with a single click, E-Run affords you the millisecond precision of stimulus presentation, synchronizations, and data collection. * E-Merge ? Merges your single session data files for group analysis * E-DataAid ? Data management utility * E-Recovery ? Recovers data files

Proper citation: E-Prime (RRID:SCR_009567) Copy   

•   Source: SciCrunch Registry

https://github.com/hjmjohnson/DTIPrep

DTIPrep performs a Study-specific Protocol based automatic pipeline for DWI/DTI quality control and preparation. This is both a GUI and command line tool. The configurable pipeline includes image/diffusion information check, padding/Cropping of data, slice-wise, interlace-wise and gradient-wise intensity and motion check, head motion and Eddy current artifact correction, and DTI computing.

Proper citation: DWI/DTI Quality Control Tool: DTIPrep (RRID:SCR_009562) Copy   

•   Source: SciCrunch Registry

http://genome.sph.umich.edu/wiki/Mach2dat:_Association_with_MACH_output

Software that performs logistic regression, using imputed SNP dosage data and adjusting for covariates.

Proper citation: Mach2dat (RRID:SCR_009599) Copy   

•   Source: SciCrunch Registry