Tools   Select Another Resource Report Type

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

An end-to-end pipeline for corpus callosum processing that provides automated midsagittal alignment, CC segmentation with a quality control tool, and thickness profile generation. Groupwise analysis is facilitated by permutation testing with FWER and FDR multiple comparison correction. Results display is facilitated by a display script that shows p-values on a 3D pipe representation of a CC. This pipeline is implemented in MATLAB and requires the Image Processing Toolbox. There are plans to implement it completely in Python.

Proper citation: Corpus Callosum Thickness Profile Analysis Pipeline (RRID:SCR_003575) Copy   

•   Source: SciCrunch Registry

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

Software that allows user to do faceted search on an ontology and enables visualization of the search results on the 3D digital atlas. Currently supports faceted search of functional neuroanatomy.

Proper citation: Faceted Search Based Ontology Visualizer (RRID:SCR_000124) Copy   

•   Source: SciCrunch Registry

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

Scribe encodes papers to populate the BrainMap Database

Proper citation: Scribe (RRID:SCR_000423) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/bash-rs-fcmri

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 23, 2015. Of note: most functions have been integrated in 1000 Functional Connectomes Project (www.nitrc.org/projects/fcon_1000). This package is not updated, thus please visit 1000 Functional Connectomes Project site and download relevant bash scripts. BASH Scripts for a resting-state functional MRI study. The functions include seed-based correlation analysis, amplitude analysis and independent component analysis. Note: this tool is just a plug-in for FSL, AFNI and FreeSurfer. Thus, you need have them before you use BASH4RfMRI.

Proper citation: BASH4RfMRI (RRID:SCR_005970) Copy   

•   Source: SciCrunch Registry

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

Software tool as MRI brain segmentation system to obtain automatically volumetric brain information from RI data. Works in automatic manner and is able to provide brain structure volumes without any human interaction.

Proper citation: volBrain (RRID:SCR_021020) Copy   

•   Source: SciCrunch Registry

http://cocomac.g-node.org/main/index.php?

Online access (html or xml) to structural connectivity ("wiring") data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies. The main database, contains data from tracing studies on anatomical connectivity in the macaque cerebral cortex. Also available are a variety of tools including a graphical simulation workbench, map displays and the CoCoMac-Paxinos-3D viewer. Submissions are welcome. To overcome the problem of divergent brain maps ORT (Objective Relational Transformation) was developed, an algorithmic method to convert data in a coordinate- independent way based on logical relations between areas in different brain maps. CoCoMac data is used to analyze the organization of the cerebral cortex, and to establish its structure- function relationships. This includes multi-variate statistics and computer simulation of models that take into account the real anatomy of the primate cerebral cortex. This site * Provides full, scriptable open access to the data in CoCoMac (you must adhere to the citation policy) * Powers the graphical interface to CoCoMac provided by the Scalable Brain Atlas * Sports an extensive search/browse wizard, which automatically constructs complex search queries and lets you further explore the database from the results page. * Allows you to get your hands dirty, by using the custom SQL query service. * Displays connectivity data in tabular form, through the axonal projections service. CoCoMac 2 was initiated at the Donders Institute for Brain, Cognition and Behaviour, and is currently supported by the German neuroinformatics node and the Computational and Systems Neuroscience group at the Juelich research institute.

Proper citation: CoCoMac (RRID:SCR_007277) Copy   

•   Source: SciCrunch Registry

http://www.oasis-brains.org/

Project aimed at making neuroimaging data sets of brain freely available to scientific community. By compiling and freely distributing neuroimaging data sets, future discoveries in basic and clinical neuroscience are facilitated.

Proper citation: Open Access Series of Imaging Studies (RRID:SCR_007385) Copy   

•   Source: SciCrunch Registry

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

A software toolkit that performs multiple imputation for group level, single sample t-tests. Whole brain group level statistic maps from fMRI rarely cover the entire brain as a result of missing data. Missingness between subjects in fMRI datasets can result from susceptibility artifacts, bounding box (acquisition parameters), and small differences in post-normalized morphology. The toolkit consists of several interactive command line scripts that guide the user to map the spatial distribution of missing data across contrast images, calculate spatial neighborhood averages that help impute values, perform conventional and multiple imputed t-statistics, save the results to brain maps, and create result tables. The toolkit contains an instruction manual (pdf), two Matlab scripts and one R-Statistics script, which depend on functions defined in the popular SPM toolbox and functions defined in the MICE package for (R).

Proper citation: Group Level Imputation of Statistic Maps (RRID:SCR_002397) Copy   

•   Source: SciCrunch Registry

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

This toolbox is an EEGLAB plugin for performing Measure Projection Analysis. Measure Projection Analysis (MPA) is a novel probabilistic multi-subject inference method that overcomes EEG Independent Component (IC) clustering issues by abandoning the notion of distinct IC clusters. Instead, it searches voxel by voxel for brain regions having event-related IC process dynamics that exhibit statistically significant consistency across subjects and/or sessions as quantified by the values of various EEG measures. Local-mean EEG measure values are then assigned to all such locations based on a probabilistic model of IC localization error and inter-subject anatomical and functional differences.

Proper citation: Measure Projection Toolbox (RRID:SCR_002429) Copy   

•   Source: SciCrunch Registry

https://pydicom.github.io/

Software Python package for working with DICOM files, made for inspecting and modifying DICOM data in an easy pythonic way. The modifications can be written again to a new file. As a pure python package, it should run anywhere python runs without any other requirements.

Proper citation: pydicom (RRID:SCR_002573) Copy   

•   Source: SciCrunch Registry

http://www.lead-dbs.org/

MATLAB toolbox for deep-brain-stimulation (DBS) electrode reconstructions and visualizations based on postoperative MRI and computed tomography (CT) imaging. The toolbox also facilitates visualization of localization results in 2D/3D, analysis of DBS-electrode placement's effects on clinical results, simulation of DBS stimulations, diffusion tensor imaging (DTI) based connectivity estimates, and fiber-tracking from the VAT to other brain regions (connectomic surgery).

Proper citation: LEAD-DBS (RRID:SCR_002915) 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://www.brain-connectivity-toolbox.net

A large selection of complex network measures in Matlab that are increasingly used to characterize structural and functional brain connectivity datasets. Several people have contributed to the toolbox, and if you wish to contribute with a new function or set of functions, please contact Olaf Sporns. All efforts have been made to avoid errors, but users are strongly urged to independently verify the accuracy and suitability of toolbox functions for the chosen application. Please report bugs or substantial improvements.

Proper citation: Brain Connectivity Toolbox (RRID:SCR_004841) 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.sr-research.com

THIS RESOURCE IS NO LONGER AVAILABLE,documented on February 1st, 2022. Instrument supplier providing eye tracking capabilities for behavioral labs as well as for MRI, MEG, and EEG research environments.

Proper citation: SR Research EyeLink Eye Trackers (RRID:SCR_009602) Copy   

•   Source: SciCrunch Registry

http://www.fnirdevices.com

fNIR Imager 1100 is a new generation portable functional near-infrared (fNIR) imaging research tool capable of monitoring brain?s hemodynamics and thereby the cognitive state of the subject in natural environments. Neuroimaging Solution for Natural Environments: * fNIR is the only stand-alone and field-deployable technology able to determine localized brain activity. * fNIR can be readily integrated with other physiological and neurobehavioral measures that assess human brain activity, including eye tracking, pupil reflex, respiration and electrodermal activity. fNIR can also complement other techniques. * Studies have shown a positive correlation between a participant's performance and fNIR responses as a function of task load. * It has also been shown that fNIR can effectively monitor attention and working memory in real-life situations.

Proper citation: fNIR Devices (RRID:SCR_009623) Copy   

•   Source: SciCrunch Registry

https://vpixx.com/products/viewpixx-3d/

VIEWPixx /3D (VPixx Technologies) is a 1920x1080 resolution, 120 Hz, calibrated research-grade LCD monitor. It is designed for stereoscopic (3D) stimulus presentation and other high-dynamic vision-science paradigms where deterministic timing and synchronized I/O are critical. It pairs fast-response industrial TN LCD glass with a custom VPixx panel/video controller and a scanning direct-RGB LED backlight engineered to reduce motion artifacts/ghosting/crosstalk, and to improve spatial uniformity, while bypassing consumer “enhancement” processing for predictable experimental output. For stereoscopic workflows, VIEWPixx /3D supports 120 Hz frame-sequential 3D (60 Hz/eye) when used with 3DPixx active shutter glasses (RF emitter + glasses kit), and it can provide a dual-link DVI console output to mirror the participant's view without adding GPU load. The system is also a synchronized display + acquisition toolbox: integrated button-box interface, 24-channel TTL triggers, stereo audio I/O, and a full analog I/O subsystem are implemented on the same board as video control to enable microsecond-precision synchronization to video refresh—useful for EEG triggers, reaction-time tasks, and other timing-sensitive paradigms.In terms of bit depth, the VIEWPixx /3D is native 8 bits per colour, with support fot 10-bit resolution per RGB channel via custom video modes.

Proper citation: VPixx: VIEWPixx /3D (RRID:SCR_009646) Copy   

•   Source: SciCrunch Registry

http://ibvd.virtualbrain.org/

A database of brain neuroanatomic volumetric observations spanning various species, diagnoses, and structures for both individual and group results. A major thrust effort is to enable electronic access to the results that exist in the published literature. Currently, there is quite limited electronic or searchable methods for the data observations that are contained in publications. This effort will facilitate the dissemination of volumetric observations by making a more complete corpus of volumetric observations findable to the neuroscience researcher. This also enhances the ability to perform comparative and integrative studies, as well as metaanalysis. Extensions that permit pre-published, non-published and other representation are planned, again to facilitate comparative analyses. Design strategy: The principle organizing data structure is the "publication". Publications report on "groups" of subjects. These groups have "demographic" information as well as "volume" information for the group as a whole. Groups are comprised of "individuals", which also have demographic and volume information for each of the individuals. The finest-grained data structure is the "individual volume record" which contains a volume observation, the units for the observation, and a pointer to the demographic record for individual upon which the observation is derived. A collection of individual volumes can be grouped into a "group volume" observation; the group can be demographically characterized by the distribution of individual demographic observations for the members of the group.

Proper citation: Internet Brain Volume Database (RRID:SCR_002060) Copy   

•   Source: SciCrunch Registry

http://neuromorphometrics.com/?page_id=23

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

Proper citation: Manually Labeled MRI Brain Scan Database (RRID:SCR_009604) Copy   

•   Source: SciCrunch Registry

http://pingstudy.ucsd.edu/

A large multi-site pediatric MRI and genetics data resource to facilitate studies of the genomic landscape of the developing human brain. It includes information about the developing mental and emotional functions of the children to understand the genetic basis of individual differences in brain structure and connectivity, cognition, and personality. Investigators on the project are studying 1400 children between the ages of 3 and 20 years so that links between genetic variation and developing patterns of brain connectivity can be examined. Investigators interested in the effects of a particular gene will be able to search the database for any brain areas or connections between areas that differ as a function of variation in a particular gene, and also to determine if the genes appear to affect the course of brain development at some point during childhood. A data exploration tool has been created for mapping and analyzing MRI data sets collected for PING and related developmental studies. Approved investigators will be able to view raw image sets and derived 3D brain maps of MRI and DTI data, conduct hypothesis testing, and graph brain area measures as they change across the time course of development. PING Cores * Coordinating Core: Functions include project management, screening of participants and maintaining the database * Neuroimaging Core: applying a standardized high-resolution structural MRI protocol involving 3-D T1-weighted scans, a T2-weighted volume, and a set of diffusion-weighted scans with multiple b values and diffusion directions, scans to estimate MRI relaxation rates, and gradient echo EPI scans for resting state fMRI. Importantly, adaptive motion compensation, using ����??PROMO����??, a novel real-time motion correction algorithm will be used. Specific PING protocols for each scanner manufacturer: ** PING MRI Protocol - GE ** PING MRI Protocol - Philips ** PING MRI Protocol - Siemens * Assessment Core: Cognitive assessments for the PING project are conducted using the NIH Toolbox for Cognition. * Genomics Core: functions as a central repository for receipt of saliva samples collected for each study participant. Once received, samples are catalogued, maintained, and DNA is extracted using state-of-the-field laboratory techniques. Ultimately, genome-wide genotyping is performed on the extracted DNA using the Illumina Human660W-Quad BeadChip. PING involves 10 sites throughout the country including UCSD, University of Hawaii, Scripps Genomics, UCLA, UC Davis, Kennedy Krieger Institute/Johns Hopkins, Sacker Institute/Cornell University, University of Massachusetts, Massachusetts General Hospital/Harvard, and Yale. Families who may want to participate in the study, or others who want to know more about it, may email questions to ping (at) ucsd.edu.

Proper citation: Pediatric Imaging Neurocognition and Genetics (RRID:SCR_008953) Copy   

•   Source: SciCrunch Registry