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http://mind.loni.usc.edu

The MiND: Metadata in NIfTI for DWI framework enables data sharing and software interoperability for diffusion-weighted MRI. This site provides specification details, tools, and examples of the MiND mechanism for representing important metadata for DWI data sets at various stages of post-processing. MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users. To assist both users and developers in working with MiND-formatted files, we provide a number of software tools for download. * MiNDHeader A utility for inspecting MiND-extended files. * I/O Libraries Programming libraries to simplify writing and parsing MiND-formatted data. * Sample Files Example files for each MiND schema. * DIRAC LONI''s Diffusion Imaging Reconstruction and Analysis Collection is a DWI processing suite which utilizes the MiND framework.

Proper citation: LONI MiND (RRID:SCR_004820) Copy   

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http://ki.se/meb/star

Large, ongoing, multifactorial study based on nation-wide ascertainment of patients with schizophrenia and bipolar disorder through the Swedish Twin Registry to include both neuroimaging data, neurocognitive function, molecular genetic data and early adverse environmental factors in the same model in a genetic sensitive design. Swedish schizophrenia research will benefit from this large study database of in total 240 affected and healthy twin pairs collected over a 5 year period. The specific aims are: * To elucidate neural endophenotypes for schizophrenia and bipolar disorder and to clarify the extent of overlap in these features between the two syndromes. * To investigate candidate genes and genomic regions for linkage and association with neural endophenotypes for schizophrenia and bipolar disease. * To determine the contributions of adverse prenatal and perinatal conditions to neural changes associated with schizophrenia and bipolar disease. Types of samples * EDTA whole blood * DNA * RNA Number of sample donors: 251 (June 2010)

Proper citation: KI Biobank - STAR (RRID:SCR_005923) Copy   

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http://www.pymvpa.org

A Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run. Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. This Python-based, cross-platform, open-source software toolbox software toolbox for the application of classifier-based analysis techniques to fMRI datasets makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.

Proper citation: PyMVPA (RRID:SCR_006099) Copy   

•   Source: SciCrunch Registry

https://brain-specimenportal.org/

Web application that tracks the status of the BICAN consortium tissue samples and related data.NIMP is developed under NIH BRAIN Initiative's BICAN U24MH130988 award as a part of the coordinating unit for biostatistics, informatics, and engagement (CUBIE) for the BRAIN Initiative Cell Atlas Network (BICAN) program.NIMP consists of two portals for BICAN collaborative data generation: the Specimen Portal and the Sequence Library (SeqLib) Portal. The Specimen Portal focuses on tissue management from donors to brain slabs and annotated brain samples. The SeqLib Portal manages the workflow starting from tissue, all the way downstream to track data deposition to assay-dependent, data-modality-specific archives. Both portals work in tandem to generate multimodal genomic data that can be traced back to their anatomical origins using the Allen Brain Atlas. The portals provide multiple types of data interfaces through dashboards, APIs, faceted queries, and batch data ingestion and exporting. All of the underlying functionalities are achieved through a robust agile development strategy using NHash resource identifiers, metadata standardization, active combinatorial dashboarding, resource provenance linkage and rendering (e.g. Sankey diagrams), and dedicated interfaces with NIH Neuro Biobank, sequencing centers, NeMO, and the larger BICAN data ecosystem.

Proper citation: NIMP: Neuroanatomy-anchored Information Management Platform for Collaborative BICAN Data Generation (RRID:SCR_024684) Copy   

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http://mus.well.ox.ac.uk/gscandb/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Database / display tool of genome scans, with a web interface that lets the user view the data. It does not perform any analyses - these must be done by other software, and the results uploaded into it. The basic features of GSCANDB are: * Parallel viewing of scans for multiple phenotypes. * Parallel analyses of the same scan data. * Genome-wide views of genome scans * Chromosomal region views, with zooming * Gene and SNP Annotation is shown at high zoom levels * Haplotype block structure viewing * The positions of known Trait Loci can be overlayed and queried. * Links to Ensembl, MGI, NCBI, UCSC and other genome data browsers. In GSCANDB, a genome scan has a wide definition, including not only the usual statistical genetic measures of association between genetic variation at a series of loci and variation in a phenotype, but any quantitative measure that varies along the genome. This includes for example competitive genome hybridization data and some kinds of gene expression measurements.

Proper citation: WTCHG Genome Scan Viewer (RRID:SCR_001635) Copy   

•   Source: SciCrunch Registry

http://qnl.bu.edu/obart

Tool that provides an interactive method to examine quantitative relationships between brain regions defined by different digital atlases or parcellation methods. Its current focus is for human brain imaging, though the techniques generalize to other domains. The method offers a quantitative answer to the nomenclature problem in neuroscience by comparing brain parts on the basis of their geometrical definitions rather than on the basis of name alone. Thus far these tools have been used to quantitatively compare eight distinct parcellations of the International Consortium for Brain Mapping (ICBM) single-subject template brain, each created using existing atlasing methods. This resources provides measures of global and regional similarity, and offers visualization techniques that allow users to quickly identify the correspondences (or lack of correspondences) between regions defined by different atlases.

Proper citation: OBART (RRID:SCR_001903) Copy   

•   Source: SciCrunch Registry

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

A versatile 1D, 2D and 3D data viewer geared for cross-platform visualization of stereotactic brain data. It is a 3-D viewer that allows volumetric data display and manipulation of axial, sagittal and coronal views. It reads Analyze, Raw-binary and NetCDF volumetric data, as well as, Multi-Contour Files (MCF), LWO/LWS surfaces, atlas hierarchical brain-region labelings ( Brain Trees). It is a portable Java-based software, which only requires a Java interpreter and a 64 MB of RAM memory to run on any computer architecture. LONI_Viz allows the user to interactively overlay and browse through several data volumes, zoom in and out in the axial, sagittal and coronal views, and reports the intensities and the stereo-tactic voxel and world coordinates of the data. Expert users can use LONI_Viz to delineate structures of interest, e.g., sulcal curves, on the 3 cardinal projections of the data. These curves then may be use to reconstruct surfaces representing the topological boundaries of cortical and sub-cortical regions of interest. The 3D features of the package include a SurfaceViewer and a full real-time VolumeRenderer. These allow the user to view the relative positions of different anatomical or functional regions which are not co-planar in any of the axial, sagittal or coronal 2D projection planes. The interactive part of LONI_Viz features a region drawing module used for manual delineation of regions of interest. A series of 2D contours describing the boundary of a region in projection planes (axial, sagittal or coronal) could be used to reconstruct the surface-representation of the 3D outer shell of the region. The latter could then be resliced in directions complementary to the drawing-direction and these complementary contours could be loaded in all tree cardinal views. In addition the surface object could be displayed using the SurfaceViewer. A pre-loading data crop and sub-sampling module allows the user to load and view practically data of any size. This is especially important when viewing cryotome, histological or stained data-sets which may reach 1GB (109 bytes) in size. The user could overlay several pre-registered volumes, change intensity colors and ranges and the inter-volume opacities to visually inspect similarities and differences between the different subjects/modalities. Several image-processing aids provide histogram plotting, image-smoothing, etc. Specific Features: * Region description DataBase * Moleculo-genetic database * Brain anatomical data viewer * BrainMapper tool * Surface (LightWave objects/scenes) and Volume rendering tools * Interactive Contour Drawing tool Implementation Issues: * Applet vs. Application - the software is available as both an applet and a standalone application. The former could be used to browse data from within the LONI database, however, it imposes restrictions on file-size, Internet connection and network-bandwidth and client/server file access. The later requires a local install and configuration of the LONI_Viz software * Extendable object-oriented code (Java), computer architecture independent * Complete online software documentation is available at http://www.loni.ucla.edu/LONI_Viz and a Java-Class documentation is available at http://www.loni.ucla.edu/~dinov/LONI_Vis.dir/doc/LONI_Viz_Java_Docs.html

Proper citation: LONI Visualization Tool (RRID:SCR_000765) Copy   

•   Source: SciCrunch Registry

http://neuromorphometrics.org:8080/nvm/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6, 2023. Software tool for quantitative neuroanatomical measurements in volumetric image data. Used to draw regions of interest for subsequent fMRI analysis.

Proper citation: NVM (RRID:SCR_000600) Copy   

•   Source: SciCrunch Registry

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

A free workflow application primarily aimed at neuroimaging researchers that allows users to easily describe their executables in a graphical user interface (ie. create a module) and connect them together to create complex analyses all without having to code a single line in a scripting language. The Pipeline Client runs on your PC/Mac/Linux computer upon which you can create sophisticated processing workflows using a variety of commonly available executable tools (e.g. FSL, AIR, FreeSurfer, AFNI, Diffusion Toolkit, etc). The Distributed Pipeline Server can be installed on your Linux cluster and you can submit processing jobs directly to your own compute systems. Once you����??ve created a module for use in the LONI Pipeline, you can save it into your personal library and reuse it in other workflows you create by simply dragging and dropping it in. Because the LONI Pipeline is written in Java, you can work in whatever operating system suits you best. If there are tools that you need that can only work on another operating system, you can install a Pipeline server on that computer and connect from your client to do processing and analysis remotely.

Proper citation: LONI Pipeline Processing Environment (RRID:SCR_001161) Copy   

•   Source: SciCrunch Registry

http://mbl.org

Collection of high resolution images and databases of brains from many genetically characterized strains of mice with aim to systematically map and characterize genes that modulate architecture of mammalian CNS. Includes detailed information on genomes of many strains of mice. Consists of images from approximately 800 brains and numerical data from just over 8000 mice. You can search MBL by strain, age, sex, body or brain weight. Images of slide collection are available at series of resolutions. Apple's QuickTime Plugin is required to view available MBL Movies.

Proper citation: Mouse Brain Library (RRID:SCR_001112) Copy   

•   Source: SciCrunch Registry

http://clarityresourcecenter.org/

Protocols and other training materials related to the CLARITY protocol, a technique for the transformation of intact tissue into a nanoporous hydrogel-hybridized form (crosslinked to a three-dimensional network of hydrophilic polymers) that is fully assembled but optically transparent and macromolecule-permeable.

Proper citation: Clarity resources (RRID:SCR_001387) Copy   

•   Source: SciCrunch Registry

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

A database which contains longitudinal structural MRIs, spectroscopy, DTI and correlated clinical/behavioral data from approximately 500 healthy, normally developing children, ages newborn to young adult.

Proper citation: NIH Pediatric MRI Data Repository (RRID:SCR_014149) Copy   

•   Source: SciCrunch Registry

http://wings-workflows.org

A software application which assists scientists with designing computational experiments. WINGS is a semantic workflow system which incorporates semantic constraints about datasets and workflow components into its workflow representations. The workflow system has an open modular design and can be easily integrated with other existing workflow systems and execution frameworks to extend them with semantic reasoning capabilities. WINGS also allows users to express high-level descriptions of their analysis goals, and assists them by automatically and systematically generating possible workflows that are consistent with that request. In cases where privacy or off-line use are important, WINGS can submit workflows in a scripted format for execution in the local host. It uses Pegasus or OODT as the execution engine for large-scale distributed workflow execution.

Proper citation: WINGS (RRID:SCR_013997) Copy   

•   Source: SciCrunch Registry

http://pklab.med.harvard.edu/scde/pagoda.links.html

Software tool for analyzing transcriptional heterogeneity to detect statistically significant ways in which measured cells can be classified. Used to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Proper citation: PAGODA (RRID:SCR_017099) Copy   

•   Source: SciCrunch Registry

https://github.com/FeeLab/seqNMF

Software tool for unsupervised discovery of sequential structure. Used to detect sequences in neural data generated by internal behaviors, such as animal thinking or sleeping. Used for unsupervised discovery of temporal sequences in high dimensional datasets in neuroscience without reference to external markers.

Proper citation: seqNMF (RRID:SCR_017068) Copy   

•   Source: SciCrunch Registry

https://github.com/epurdom/clusterExperiment

Software open source R package for executing, evaluating and visualizing different clusterings of experimental data, including data from single cell RNA-Seq studies. Software for running and comparing different clusterings of single cell sequencing data.

Proper citation: clusterExperiment (RRID:SCR_017439) Copy   

•   Source: SciCrunch Registry

http://neuroproteomics.scs.illinois.edu/microMS.htm

Software Python platform for image guided Mass Spectrometry profiling. Provides graphical user interface for automatic cell finding and point based registration from whole slide images. Simplifies single cell analysis with feature rich image processing.

Proper citation: microMS (RRID:SCR_017443) Copy   

•   Source: SciCrunch Registry

https://github.com/YosefLab/FastProject

Software Python tool for low dimensional analysis of single-cell RNA-Seq data. Software package for two dimensional visualization of single cell data. Analyzes gene expression matrix and produces output report in which two-dimensional of data can be explored.

Proper citation: FastProject (RRID:SCR_017462) Copy   

•   Source: SciCrunch Registry

http://www.jwatcher.ucla.edu

Software Java tool for quantitative analysis of behavior. Used to address any theoretical problem that requires complex sequence of actions to be scored by human observer. Runs on microcomputer providing Java Virtual Machine[TM] and has been tested on Windows[TM] and Macintosh[TM] systems. Legacy version (version 0.9) works on older systems (Macintosh OS-9 and Windows-98), while Version 1.0 works well on Macintosh OS-X and Windows XP systems. JWatcher Video works best on Windows XP systems and has reduced functionality running in Macintosh OS-X. JWatcher-Palm can be used to acquire data on Palm OS[TM] equipped device and analyze it on your main computer.

Proper citation: JWatcher (RRID:SCR_017595) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/pubmed/28653482

Software tool to facilitate tractography based deep brain stimulation (DBS) electrode targeting within patient specific stereotactic coordinate system used in operating room.

Proper citation: StimVision (RRID:SCR_017457) Copy   

•   Source: SciCrunch Registry