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

This resource was created to host descriptions of protocols, definitions and rules for the reliable identification and localization of human brain anatomy and discussions of best practices in brain labeling. Project for manual anatomical labeling of human brain MRI data, and the visual presentation of labeled brain images.

Proper citation: BrainColor: Collaborative Open Labeling Online Resource (RRID:SCR_006377) 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/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://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/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

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

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

A novel C++ based application developped at UNC-Chapel Hill that performs automatic brain tissue classification and structural segmentation. AutoSeg is designed for use with human and non-human primate pediatric, adolescent and adult data. AutoSeg uses a BatchMake pipeline script that includes the main steps of the framework entailing N4 bias field correction, rigid registration to a common coordinate image, tissue segmentation, skull-stripping, intensity rescaling, atlas-based registration, subcortical segmentation and lobar parcellation, regional cortical thickness and intensity statistics. AutoSeg allows efficient batch processing and grid computing to process large datasets and provides quality control visualizations via Slicer3 MRML scenes.

Proper citation: AutoSeg (RRID:SCR_009438) Copy   

•   Source: SciCrunch Registry

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

A collection of data processing and image analysis tools for data in BXH or XCEDE format. This includes data format encapsulation/conversion, event-related analysis, QA tools, and more. These tools form the basis of the fBIRN QA procedures and are also distributed as part of the fBIRN Data Upload Scripts.

Proper citation: BXH/XCEDE Tools (RRID:SCR_009439) Copy   

•   Source: SciCrunch Registry

http://dsi-studio.labsolver.org

A software for diffusion MR images analysis. The provided functions include reconstruction (DTI, QBI, DSI, and GQI), deterministic fiber tracking, and 3D visualization. It has a window-based interface and operates on Microsoft Windows system.

Proper citation: DSI Studio (RRID:SCR_009557) Copy   

•   Source: SciCrunch Registry

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

A set of deformable modeling algorithms for shape analysis and structure-specific normalization. Applications of cm-reps include structure-specific fMRI analysis, DTI analysis, and structural brain mor

Proper citation: cmrep (RRID:SCR_009434) Copy   

•   Source: SciCrunch Registry

http://www.dian-info.org/default.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. An international research partnership of leading scientists determined to understand a rare form of Alzheimers disease that is caused by a gene mutation and to establish a research database and tissue repository to support research on Alzheimers disease by other investigators around the world. One goal of DIAN is to study possible brain changes that occur before Alzheimers disease is expressed in people who carry an Alzheimers disease mutation. Other family members without a mutation will serve as a comparison group. People in families in which a mutation has been identified will be tracked in order to detect physical or mental changes that might distinguish people who inherited the mutation from those who did not. DIAN currently involves eleven outstanding research institutions in the United States, United Kingdom, and Australia. John C. Morris, M.D., Friedman Distinguished Professor of Neurology at Washington University School of Medicine in St. Louis, is the principal investigator of the project., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DIAN - Dominantly Inherited Alzheimer Network (RRID:SCR_000812) Copy   

•   Source: SciCrunch Registry