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http://www.biological-networks.org/pubs/suppl/sinomo/

Analysis-tool which identifies singular node motifs in a network. Network nodes can be described by node-motifs. It is an improvement to the method described in Costa et al. (2009).

Proper citation: SINOMO (RRID:SCR_005286) Copy   

•   Source: SciCrunch Registry

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

A set of tools for deriving region of interest (ROI) atlases by whole brain clustering of task or resting state data. This resource also contains several atlases derived by parcellating publicly available resting state fMRI datasets. The initial release will include python scripts and ROI atlases developed to perform the analyses described in Craddock et. al., A whole brain fMRI atlas generated via spatially constrained spectral clustering, which is currently in revision in Human Brain Mapping. The scripts provide all of the tools necessary to derive an ROI atlases using spatially constrained Ncut spectral clustering. The scripts require python, numpy and scipy to run. Source code and parcellations now available! Go to http://ccraddock.github.io/cluster_roi/ for more information.

Proper citation: Spatially Constrained Parcellation (RRID:SCR_002198) Copy   

•   Source: SciCrunch Registry

http://datacite.labs.orcid-eu.org

Service (Beta) that allows users to search the DataCite Metadata Store, and add their research outputs including datasets, software, and others to their ORCID profile. This should increase the visibility of these research data, and will make it easier to use these data citations in applications that connect to the ORCID Registry. In addition, the service is also providing formatted citations in several popular citation styles, supports COinS, links to related resources, and displays the attached Creative Commons license where this information is available. The DataCite Metadata Store of course also contains many text documents from academic publishers and services such as figshare or PeerJ Preprints, and these works can also be claimed. This tool is a collaborative effort by ORCID, CrossRef and DataCite.

Proper citation: ODIN (RRID:SCR_001386) Copy   

•   Source: SciCrunch Registry

http://www.mouseconnectome.org/

Three-dimensional digital connectome atlas of the C57Black/6J mouse brain and catalog of neural tracer injection cases, which will eventually cover the entire brain. Serial sections of each case are available to view at 10x magnification in the interactive iConnectome viewer. The Image Gallery provides a glimpse into some of the highlights of their data set. Representative images of multi-fluorescent tracer labeling can be viewed, while more in depth examination of these and all other cases can be performed in the iConnectome viewer. Phase 1 of this project involves generating a physical map of the basic global wiring diagram by applying proven, state of the art experimental circuit tracing methods systematically, uniformly, and comprehensively to the structural organization of all major neuronal pathways in the mouse brain. Connectivity imaging data for the whole mouse brain at cellular resolution will be presented within a standard 3D anatomic frame available through the website and accompanied by a comprehensive searchable online database. A Phase 2 goal for the future will allow users to view, search, and generate driving direction-like roadmaps of neuronal pathways linking any and all structures in the nervous system. This could be looked on as a pilot project for more ambitious projects in species with larger brains, such as human, and for providing a reliable framework for more detailed local circuitry mapping projects in the mouse.

Proper citation: Mouse Connectome Project (RRID:SCR_004096) Copy   

•   Source: SciCrunch Registry

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

Tool box for arterial spin labeled perfusion MRI data processing. It is based on SPM and Matlab. More detailed documentation can be found in asl_perf_subtract.m, the main function for calculating CBF value. It supports 3D or 4D Analyze or Nifiti format and PASL, CASL, and PCASL data. It contains the code for calculating CBF and a set of SPM batch scripts for preprocessing and statistical analysis.

Proper citation: ASL data processing tool box (RRID:SCR_005997) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/Software/LONI-Inspector

A Java application for reading, displaying, searching, comparing, and exporting metadata from medical image files: AFNI, ANALYZE, DICOM, ECAT, GE, Interfile, MINC, and NIFTI.

Proper citation: LONI Inspector (RRID:SCR_004923) Copy   

•   Source: SciCrunch Registry

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

Pipeline developed for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. FADTTS can be used to facilitate understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modelling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands.

Proper citation: Functional Analysis of Diffusion Tensor (RRID:SCR_008888) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/fiber-sig/

Used to analyze the fibers produced by ukf tractography

Proper citation: Fiber Tracking Tool (RRID:SCR_009474) Copy   

•   Source: SciCrunch Registry

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

These are the scripts used for the analyses reported in: Craddock RC, Holtzheimer PE, 3rd, Hu XP, Mayberg HS. (2009): Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619-28. Specifically included are scripts for performing t-test filter, reliability filter, recursive feature elimination, and reliability recursive feature elimination feature selection methods. These make use of wrappers that perform .632 bootstrap and k-fold cross validation strategies. The scripts are written in matlab and require the Bioinformatics toolbox. If you do not have the bioinformatics toolbox, the scripts can be easily modified to run with other matlab SVM toolboxes (i.e., libsvm, svmlight, shogun, etc.).

Proper citation: Disease State Prediction (RRID:SCR_009467) Copy   

•   Source: SciCrunch Registry

http://www.nmr.mgh.harvard.edu/DOT/resources/homer2/home.htm

Software matlab scripts used for analyzing fNIRS data to obtain estimates and maps of brain activation. Graphical user interface (GUI) for visualization and analysis of functional near-infrared spectroscopy (fNIRS) data.

Proper citation: Homer2 (RRID:SCR_009586) Copy   

•   Source: SciCrunch Registry

http://www.iit.edu/~mri/

Atlas that contains new anatomical, DTI, HARDI templates and probabilistic gray matter labels of the adult human brain in ICBM-152 space. Artifact-free MRI data from 72 human subjects was used in the development of the atlas. All diffusion MRI data collection was conducted using Turboprop, and spatial normalization was accomplished in a population-based fashion. A description of the contents of the atlas can be found in the Downloads link. NOTE: The files of the older IIT2 DTI Brain Template are still available. However, the new DTI template of the IIT Human Brain Atlas (v.3) is of superior quality and allows more accurate registration across subjects.

Proper citation: IIT Human Brain Atlas (RRID:SCR_009587) Copy   

•   Source: SciCrunch Registry

http://hermes.ctb.upm.es/

A toolbox for the Matlab environment designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. This toolbox may be very helpful to all the researchers working in the emerging field of brain connectivity analysis.

Proper citation: HERMES (RRID:SCR_009584) Copy   

•   Source: SciCrunch Registry

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

This is a command line tool which allows the user to study the behavior of water diffusion (using DTI data) along the length of the white matter fiber-tracts. Various tract-oriented scalar diffusion measures obtained from DTI brain images, are treated as a continuous function of white matter fibers'' arc-length. To analyze the trend along a given fiber tract, a command line tool performs kernel regression on this data. The idea is to try out different noise models and maximum likelihood estimates within kernel windows (along the tract), such that they best represent the data and are robust to noise and Partial Volume effect. The package contains several command line based modules and an GUI based tool called DTIAtlasFiberAnalyzer to access most functions. The features available in the tool currently, its use and input / output formats and other relevant details are provided in the first draft of the documentation. (http://www.na-mic.org/Wiki/index.php/Projects:dtistatisticsfibers).

Proper citation: DTI Fiber Tract Statistics (RRID:SCR_009460) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/inia19/

Primate brain atlas created from over 100 structural MR scans of 19 rhesus macaque animals. The atlas currently comprises high-resolution T1-weighted average MR images with and without skull stripping, tissue probability maps, and a detailed parcellation map based on the NeuroMaps atlas.

Proper citation: INIA19 Primate Brain Atlas (RRID:SCR_009498) Copy   

•   Source: SciCrunch Registry

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

A set of MATLAB scripts for analysis of networks derived from neuroimaging data. Some of these scripts are entirely original, while some are adapted (or just copied) from the Brain Connectivity Toolbox (https://sites.google.com/a/brain-connectivity-toolbox.net/bct) The source code is available via git: git clone ssh://user

Proper citation: Graphtools (RRID:SCR_009490) Copy   

•   Source: SciCrunch Registry

https://www.bci2000.org/

BCI2000 is a general-purpose system for brain-computer interface (BCI) and adaptive neurotechnology research. It can also be used for data acquisition, stimulus presentation, and brain monitoring applications. The mission of the BCI2000 project is to facilitate research and applications in the areas described. Their vision is that BCI2000 will become a widely used software tool for diverse areas of real-time biosignal processing. In order to achieve this vision, BCI2000 system is available for free for non-profit research and educational purposes. BCI2000 supports a variety of data acquisition systems, brain signals, and study/feedback paradigms. During operation, BCI2000 stores data in a common format (BCI2000 native or GDF), along with all relevant event markers and information about system configuration. BCI2000 also includes several tools for data import/conversion (e.g., a routine to load BCI2000 data files directly into Matlab) and export facilities into ASCII. BCI2000 also facilitates interactions with other software. For example, Matlab scripts can be executed in real-time from within BCI2000, or BCI2000 filters can be compiled to execute as stand-alone programs. Furthermore, a simple network-based interface allows for interactions with external programs written in any programming language. For example, a robotic arm application that is external to BCI2000 may be controlled in real time based on brain signals processed by BCI2000, or BCI2000 may use and store along with brain signals behavioral-based inputs such as eye-tracker coordinates. Because it is based on a framework whose services can support any BCI implementation, the use of BCI2000 provides maximum benefit to comprehensive research programs that operate multiple BCI2000 installations to collect data for a variety of studies. The most important benefits of the system in such situations are: - A Proven Solution - Facilitates Operation of Research Programs - Facilitates Deployment in Multiple Sites - Cross-Platform and Cross-Compiler Compatibility - Open Resource Sponsors: BCI2000 development is sponsored by NIH/NIBIB R01 and NIH/NINDS U24 grants. Keywords: General, Purpose, Systems, Brain, Computer, Interface, Research, Application, Brain, Diverse, Educational, Laboratory, Software, Network, Signals, Behavioral, Eye, Tracker,

Proper citation: Brain Computer Interface 2000 Software Package (RRID:SCR_007346) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/factory4t1ndti/

Tools to make easier on using spm, pipedream, dti-tk, and other softwares to analyze t1 or dti images.

Proper citation: factory t1 dti (RRID:SCR_009523) Copy   

•   Source: SciCrunch Registry

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

A graph theoretical network analysis toolbox which allows researchers to perform comprehensive analysis on the topology of brain connectome by integrating the most of network measures studied in current neuroscience field.

Proper citation: GRETNA (RRID:SCR_009487) Copy   

•   Source: SciCrunch Registry

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

A platform for any Principal Component Analysis (PCA)-based analysis on functional neuroimaging data (PET and fMRI). Includes: * Ordinal Trend Canonical Variance Analysis for parametric designs (C. Habeck et al. A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis. Neural Computation 2005; 17: 1602-1645) * Partial Least Squares for any design matrix * Subprofile Scaling Model for cross-sectional designs (JR. Moeller, Strother SC. A regional covariance approach to the analysis of functional patterns in positron emission tomographic data.J Cereb Blood Flow Metab. 1991 Mar;11(2):A121-35.)

Proper citation: Generalized Covariance Analysis (RRID:SCR_009488) Copy   

•   Source: SciCrunch Registry

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

GAMMA suite is an open-source cross-platform data mining software package designed to analyze neuroimaging data. A neuroimaging study often focuses on biomarker detection and classification. We designed and implemented a Bayesian, multivariate, nonparametric suite of algorithms for analyzing neuroimaging data. The GAMMA suite can be used for brain morphometric analysis, lesion-deficit analysis, and functional MR data analysis.

Proper citation: GAMMA (RRID:SCR_009484) Copy   

•   Source: SciCrunch Registry