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

High quality data, open and freely available to everyone to celebrate the diversity of the vertebrate brain. Do you have data that you would like to share? Do not hesitate to contact them! The Brain Catalogue is developed by Florencia Grisanti (Taxidermy Workshop of the Natural History Museum in Paris) and Roberto Toro (Neuroscience Department of the Institut Pasteur). Many of our specimens come from the Vertebrate Brain Collection of the Jardin des Plantes, curated by Marc Herbin, and are scanned at the Institut du Cerveau et de la Moelle (ICM) by Mathieu Santin and Alexandra Petiet, from the CENIR laboratory, with financial and methodological support kindly provided by Olivier Colliot, head of the Cogimage team at the ICM.

Proper citation: Brain Catalogue (RRID:SCR_009442) Copy   

•   Source: SciCrunch Registry

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

Human DTI brain atlases have been generated at UNC-Chapel Hill for several age groups, by iterative joint deformable registration of training datasets into a single unbiased DTI average image. Atlases packages include an atlas DTI tensor image, atlas DTI property images (FA, MD, AD, RD), and single tensor tractography based fiber tracts of major tracts with related 3D planes for fiber profile information: genu, splenium, anterior and posterior limb of internal capsule, uncinate fasciculus.

Proper citation: UNC Human DTI Brain Atlas (RRID:SCR_009516) Copy   

•   Source: SciCrunch Registry

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

A nice sign of bias field correction (nonuniformity) in medical images. This tool is fast and efficient. Technical details can be found at http://zheng.vision.googlepages.com/biasCorrection_miccai09_Zheng.pdf

Proper citation: NICE-SIGN (RRID:SCR_009629) Copy   

•   Source: SciCrunch Registry

http://www.gtec.at/Products/Software/g.BSanalyze-Specs-Features

An interactive environment for multimodal biosignal data processing and analysis in the fields of clinical research and life sciences. It is the most comprehensive package to analyze non-invasive and invasive brain-, heart- and muscle-functions and dysfunctions. It includes many functions such as support vector machines, event-related ECG, support for P300 and SSVEP/SSSEP BCIs, zero class detection for BCIs, compressed spectral array, minimum energy, and more! g.BSanalyze consists of a base version for data import, visualization, transformation and pre-processing and has several dedicated toolboxes. The package comes with many sample biosignal data-sets, including P300, SSVEP, motor imagery, CSP BCIs, Tilt-Table, EPs, multi-unit activity, CFM, and ERD/ERS.

Proper citation: g.BSanalyze (RRID:SCR_009625) Copy   

•   Source: SciCrunch Registry

http://www.xinapse.com/

A medical image display package that allows easy viewing and analysis of Magnetic Resonance, x-ray CT and other types of medical image. Jim is an up-to-the-minute design with a familiar user-interface.

Proper citation: Jim (RRID:SCR_009589) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/hispeedpackets/

HI-SPEED Software Packets contain # unconstrained and constrained nonlinear least squares diffusion tensor estimation techniques, # 2-dimensional and 3-dimensional analytical (Shepp-Logan) magnetic resonance imaging phantoms in both the Fourier and image domains, # techniques for reporting the underlying signal-to-noise ratio in magnetic resonance (MR) images, # Probabilistic Identification and EStimation of NOise (PIESNO)---a technique for identifying noise-only pixels and estimating the underlying noise standard deviation in MR images, and # a signal-transformational technique for breaking the noise floor in MR images. Many more computational tools will be shared with users and developers as they become available.

Proper citation: HI-SPEED Software Packets (RRID:SCR_009585) Copy   

•   Source: SciCrunch Registry

http://www.bsl.ece.vt.edu/index.php?page=ara-dataset

Dataset of structural MR images of 70 subjects collected during 2008-2010 across a wide range of ages. The dataset also contains resting state fMRI for most subjects. The structural images are T1 weighted, T2 weighted-FLAIR, 25 direction DTI, and the T1 mapping DESPOT [1] sequence. Reconstructed T1 maps for each subject are also available. The aquisition protocol was designed to study structural differences between young and older adults including both shape and intensity changes. Anonymized DICOM image sessions and processed images for each subject are available. The data is licensed under the Creative Commons Attribution License. It may be used freely for commercial, academic, or other use, as long as the original source is properly cited. http://www.bsl.ece.vt.edu/index.php?page=ara-dataset

Proper citation: Age Related Atrophy Dataset (RRID:SCR_009528) Copy   

•   Source: SciCrunch Registry

http://amilab.org

An opensource software for image analysis, processing and visualization. It provides convenient visualization tools for 2D and 3D images and it is highly extensible through its own scripting language. At visualization level, AMILab includes a 2D/3D image viewer, a 3D polygon viewer based on OpenGL, a 2D Curve viewer to visualize 2D curves, histograms and color/opacity transfer functions, and a GPU-enabled raycasting script for Volume Rendering based on VTK. The software includes an automatic C++ wrapping system which permits fast development of new visualization tools and image processing algorithms. This wrapping system currently wraps about 200 classes from wxwidgets library and about 100 classes from VTK.

Proper citation: AMILab (RRID:SCR_009525) Copy   

•   Source: SciCrunch Registry

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

A data analysis environment for diffuse optical tomography (DOT) functional neuroimaging data. Developed to process data from steady-state time-series measurements, it allows for maximal flexibility in the number and positions of optodes. The central component is an application called NAVI. Features include: # An electronic ledger (records metadata for all data transformations). # Data conditioning (e.g., frequency-filtering, selection of data on the basis of signal-to-noise ratio.) # 2D or 3D image formation and display. # Interpretation: atlas-based mapping; automated anatomical labeling; GLM; data-driven methods (e.g., PCA, ICA); model-based (e.g., dynamic causal modeling) and data-driven (e.g., correlation) connectivity analysis. Another important component is the Brain Model Generator, which includes FEM meshes for all parts of the head accessible to DOT measurements. The user can input the numbers of optodes, and manually specify their locations or input tracking-system data.

Proper citation: fNIRS Data Analysis Environment (RRID:SCR_009522) Copy   

•   Source: SciCrunch Registry

https://compumedicsneuroscan.com/products/by-name/curry/

Processing software for multimodal neuroimaging centered on combining functional data such as EEG and MEG with imaging data from MRI and CT to optimize source reconstruction. They are now combining Curry's strength with the acquisition and signal processing features of the SCAN software for a comprehensive EEG acquisition, data analysis, source localization and source imaging package.

Proper citation: CURRY (RRID:SCR_009546) Copy   

•   Source: SciCrunch Registry

http://www.cis.hut.fi/projects/ica/fastica/

General-purpose unsupervised data-analysis tool, most often used for brain imaging data.

Proper citation: FastICA (RRID:SCR_013110) Copy   

•   Source: SciCrunch Registry

https://github.com/NIRALUser/DTIAtlasBuilder

This tool creates an Atlas image as an average of several DTI images that will be registered. The registration will be done in two steps : - Affine Registration with BRAINSFit - Non Linear Registration with GreedyAtlas A final step will apply the transformations to the original DTI images so that the final average can be computed. The main function writes a python script that will be executed to compute the Atlas. By running DTIAtlasBuilder, you will need to fill in informations in a Graphical User Interface, and then compute the Atlas. You can also run the tool in command line without using the GUI. Using the GUI, you will be able to save or load a dataset file or a parameter file. The tool needs these other tools to work, so be sure to have these installed on your computer: - ImageMath - ResampleDTIlogEuclidean - CropDTI - dtiprocess - BRAINSFit - GreedyAtlas - dtiaverage - DTI-Reg - unu - MriWatcher If you download the package, be sure to have the glut library installed.

Proper citation: DTI Atlas Builder (RRID:SCR_013112) Copy   

•   Source: SciCrunch Registry

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

A tool that implements a graph-based connectivity assessment method. This method uses a multi-directional graph propagation method applied to sampled orientation distribution function (ODF), which can be computed directly from the original diffusion imaging data.

Proper citation: COST (RRID:SCR_014098) Copy   

•   Source: SciCrunch Registry

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

A software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. If BROCCOLI is running on a GPU, it can perform non-linear spatial normalization to a 1 mm brain template in 4-6 s and run a second level permutation test with 10,000 permutations.

Proper citation: BROCCOLI (RRID:SCR_014093) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/afni_3dsvm/

A command-line program and plugin for AFNI built around SVM-Light. It performs support vector machine (SVM) analysis on fMRI data and runs on Unix+X11+Motif systems, including SGI, Solaris, Linux, and Mac OS X.

Proper citation: 3dsvm (RRID:SCR_014083) Copy   

•   Source: SciCrunch Registry

http://www.softpedia.com/get/Science-CAD/BrainCSI.shtml

A tool for analysis of Magnetic Resonance Spectroscopy (MRS) data by registering it to anatomical images. BrainCSI imports LCModel results to calculate absolute metabolite concentrations using tissue water. Corrections to LCModel metabolite concentrations for partial volume of tissues are accomplished by tissue classification of the anatomical images.

Proper citation: BrainCSI (RRID:SCR_013244) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/ntu-dsi-122/

A diffusion spectrum imaging (DSI) template constructed in the standard ICBM-152 space from 122 healthy adults. The template was built through incorporating the macroscopic anatomical information using high-resolution T1-weighted images and the microscopic structural information obtained from DSI datasets, rendering it to achieve a high anatomical matching to the ICBM-152 space. This template can serve as a representative DSI dataset for a healthy adult population. It is released in its original DWI format.

Proper citation: NTU-DSI-122: a DSI template in ICBM-152 space (RRID:SCR_014155) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-10/search?q=*&l=

A virtual database currently indexing software and tools from the SciCrunch Registry, Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC), Visiome Platform, Cerebellar Platform, Brain Machine Interface Platform, and Genetic Analysis Software (GAS).

Proper citation: Integrated Software (RRID:SCR_004745) Copy   

•   Source: SciCrunch Registry

http://fcon_1000.projects.nitrc.org/indi/enhanced/

Dataset of 1000 characterized community-ascertained participants using state-of-the-art multiband imaging-based resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI), genetics, and a deep phenotyping protocol from a large cross-sectional sample of brain development, maturation and aging (ages 6 - 85 yrs). The Center for Magnetic Resonance Research (CMRR), University of Minnesota, provided the NKI-RS effort with the latest version of the Multiband EPI sequence (Xu et al. 2012) and associated image reconstruction algorithms, enabling the acquisition of state-of-the-art imaging datasets for this large-scale imaging effort. The enhanced NKI-RS expands upon the phenotypic protocol of the original NKI-RS and captures a broad range of behavioral and cognitive phenomenology relevant to psychiatric health and illness. The validity and value of assessments were evaluated by consulting leaders in the field of psychiatric phenotyping.

Proper citation: NKI-RS Enhanced Sample (RRID:SCR_010461) Copy   

•   Source: SciCrunch Registry

http://www.radiologyresearch.org/HippocampusSegmentation.aspx

This dataset contains T1-weighted MR images of 50 subjects, 40 of whom are patients with temporal lobe epilepsy and 10 are nonepileptic subjects. Hippocampus labels are provided for 25 subjects for training. The users may submit their segmentation outcomes for the remaining 25 testing images to get a table of segmentation metrics.

Proper citation: MRI Dataset for Hippocampus Segmentation (RRID:SCR_009597) Copy   

•   Source: SciCrunch Registry