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

This resource provides simulated DW-MRI brain images and quantitative tools for evaluating the performance of diffusion analysis methods in terms of fiber orientation estimation and false-positive/-negative fiber rates, which are of fundamental importance to tractography based studies. DW data was generated using a multi-tensor model at SNRs of 9, 18 and 36, for sets of 20, 30, 40, 60, 90 and 120 gradient directions. For each combination of SNR and gradient direction set, 10 realizations of data are provided. All data is simulated with a diffusion-weighting of b=1000, as is common for clinical acquisitions.

Proper citation: Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations (RRID:SCR_014168) Copy   

•   Source: SciCrunch Registry

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

This algorithm corrects for spikes in fMRI data, typically caused by abrupt head motion during scanning. It identifies outliers using Principal Component Analysis (PCA) in a sliding time-window; it is sensitive to global motion artifact, and stable against non-stationary signal changes.

Proper citation: SPIKECOR: fMRI tool for automated correction of head motion spikes (RRID:SCR_014169) Copy   

•   Source: SciCrunch Registry

http://www.theuais.org

A topical portal for the UAIS Lab of Lanzhou University which researches predicting depression and schizophrenia based on demographics and physiological information (EEG, ERPs, Genetics, MRI, fMRI, etc.). It also researches wearable bio-signal sensors and antennas, bio-signal processing, speech analysis, pervasive mental health, psycho-physiological computing, bioinformatics and multimodal data fusion and modeling.

Proper citation: Prediction and Diagnosis for Depression and Schizophrenia (RRID:SCR_014161) Copy   

•   Source: SciCrunch Registry

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

Graphical user interface that has been implemented as a 3D Slicer plugin (scripted module). It serves to display a corresponding set of cortical regions from functional connectivity matrix in an explorable 3D scene that represents brain anatomical environment. In addition to grey matter regions, MultiXplore automatically finds and extracts deterministic fiber bundles which exist between selected region(s) and adds them to the 3D environment. This feature helps in generating region-based fiber bundles given a desired whole-brain tractography data.

Proper citation: MultiXplore (RRID:SCR_014814) Copy   

•   Source: SciCrunch Registry

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

Integrated pipeline for tractography-based brain parcellation with automatic processing and massive parallel computing. ATPP offers a CLI version for parcellating multiple brain regions and a GUI version for parcellating a specific brain region. " ATPP completely follows the scientific cultural shift to open science, which aims at making scientific research including journal papers, lab notes, data, and, of course, workflow tools, accessible and transparent to all levels of society. ATPP is publicly accessible in Neuroimaging Informatics Tools and Resources Clearinghouse8 (NITRC) (https://www.nitrc.org/projects/atpp). Its source codes are hosted in GitHub9 (https://github.com/haililihai/ATPP_CLI; https://github.com/haililihai/ATPP_GUI), under the GNU generic purpose license version 310 (GPLv3), and are welcome to download and fork. The Digital Object Identifiers (DOIs) providing a persistent way to make digital data easily and uniquely citable was created from Zenodo11 platform with those GitHub repositories (ATPP CLI v2.0.0, doi: https://doi.org/10.5281/zenodo.239702; ATPP GUI v2.0.0, doi: https://doi.org/10.5281/zenodo.239705). "

Proper citation: Automatic Tractography-based Parcellation Pipeline (RRID:SCR_014815) Copy   

•   Source: SciCrunch Registry

https://github.com/BlueBrain/BluePyOpt

An extensible framework for data-driven model parameter optimization that wraps and standardizes several existing open-source tools. BluePyOpt abstracts the optimization and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. It also provides methods for setting up both small- and large-scale optimizations on a variety of platforms.

Proper citation: BluePyOpt (RRID:SCR_014753) Copy   

•   Source: SciCrunch Registry

http://www.ant-neuro.com/products/asa

A highly flexible EEG/ERP and MEG analysis package with a variety of source reconstruction, signal analysis and MRI processing features. ASA combines functional brain imaging with the visualization and incorporation of morphological information obtained from MRI or CT. ASA is a highly interactive and flexible software tool that can be applied to neuro-physiological and clinical brain research. ASA gives a realistic impression of your experimental configuration together with topographical mapping of EEG and MEG and the results of your analysis. ASA is developed for and by people dedicated to brain research. The concept of flexibility and openness covers even most complex analysis demands. The ASA environment is particularly attractive for those that wish to develop their own methods in third party packages like Matlab and use ASA for pre-processing and visualization purposes.

Proper citation: ASA - Advanced Source Analysis (RRID:SCR_012867) Copy   

•   Source: SciCrunch Registry

https://github.com/BRAINSia/BRAINSTools/tree/master/BRAINSConstellationDetector

This program will find the mid-sagittal plane, the AC, PC, and mpj points in an image, and create an AC/PC aligned data set with the AC point at the center of the voxel lattice (la beled at the origin of the image physical space.) This work is an extention of the algorithms originally described by Dr. Babak A. Ardekani, Alvin H. Bachman, Model-based automatic detection of the anterior and posterior commissures on MRI scans, N euroImage, Volume 46, Issue 3, 1 July 2009, Pages 677-682, ISSN 1053-8119, DOI: 10.1016/j.neuroimage.2009.02.030. (http://www.sciencedirect.com/science/article/B6WNP-4VRP25C-4/2/8207b962a38aa83c822c6379bc43fe4c)

Proper citation: BRAINSConstellationDetector (RRID:SCR_012856) Copy   

•   Source: SciCrunch Registry

http://treestoolbox.org/

Software package, written in Matlab (Mathworks, Natick, MA), providing tools to automatically reconstruct neuronal branching from microscopy image stacks and to generate synthetic axonal and dendritic trees. It provides the basic tools to edit, visualize and analyze dendritic and axonal trees, methods for quantitatively comparing branching structures between neurons, and tools for exploring how dendritic and axonal branching depends on local optimization of total wiring and conduction distance.

Proper citation: TREES toolbox (RRID:SCR_010457) Copy   

•   Source: SciCrunch Registry

http://www.harvard.edu/

Institution of higher education in the United States. Private Ivy League research university in Cambridge, Massachusetts.

Proper citation: Harvard University; Cambridge; United States (RRID:SCR_011273) Copy   

•   Source: SciCrunch Registry

https://vpixx.com/products/viewpixx/

Research-grade, CRT-replacement LCD display system for vision science and psychophysics. It combines a 22.5″ 1920×1200 industrial LCD (wide 176°/176° viewing angles) with a custom panel/video controller engineered for deterministic stimulus timing and synchronized acquisition. The display supports 12-bit intensity resolution per RGB channel via custom video modes. It uses a scanning RGB LED backlight to improve temporal precision (e.g., crisp frame transitions and reduced motion artifacts) while bypassing consumer “enhancement” processing to keep output predictable for experiments. VIEWPixx also integrates microsecond-synchronized peripherals commonly needed in timing-sensitive paradigms—24-channel TTL I/O (triggers), stereo audio I/O, analog I/O, and a button-box interface—implemented on the same board as the video pipeline for tight hardware-to-video synchronization.

Proper citation: VPixx: VIEWPixx (RRID:SCR_013271) Copy   

•   Source: SciCrunch Registry

https://vpixx.com/hardware/projector/

Unique DLP LED projector which has been designed to be the most flexible display solution for vision research and neuroscience research. The PROPixx features a native resolution of 1920 x 1080, and can be driven with refresh rate up to 500Hz with deterministic timing. The PROPixx uses high brightness LEDs as a light source, giving a wide colour gamut and much longer lifetime than halogen light sources. It features high-bit depth, up to 12-bit per color for high-frequency full colour stimulation. For stereo vision applications, our high-speed ferro-electric circular polarizer can project stereoscopic stimuli with the use of passive glasses at up to 400Hz. In addition the PROPixx includes an array of peripherals which often need to be synchronized to video during an experiment, and with perfect microsecond precision.

Proper citation: VPixx: PROPixx (RRID:SCR_013299) Copy   

•   Source: SciCrunch Registry

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

The FP-CIT SPECT brain template has been created using a fully automatic procedure involving posterization of the source image to three levels: background, brain and striatum. We performed a spatial affine registration of these 40 posterized source images to a posterized reference image in the MNI space. The intensity values of the transformed images is normalized linearly, assuming that the histogram of the intensity values follows an alpha-stable distribution. Lastly, we built the [123I]FP-CIT SPECT template by the mean of the transformed and normalized images. More info: 1) Salas-Gonzalez et al. Building a FP-CIT SPECT brain template using a posterization approach. Accepted in Neuroinformatics. 2) Salas-Gonzalez et al. Linear intensity normalization of FP-CIT SPECT brain images using the alpha-stable distribution. NeuroImage, Volume 65, 2013, pp. 449-455. http://dx.doi.org/10.1016/j.neuroimage.2...

Proper citation: FP-CIT SPECT brain template in MNI space (RRID:SCR_013668) Copy   

•   Source: SciCrunch Registry

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

A toolbox that implements several EEG/MEG source localization techniques within the Maximum Entropy on the Mean (MEM) framework. These methods are particularly dedicated to estimate accurately the source of EEG/MEG generators together with their spatial extent along the cortical surface.

Proper citation: Brain Entropy in space and time (BEst) (RRID:SCR_014090) Copy   

•   Source: SciCrunch Registry

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

Brainnetome Atlas Viewer shows the anatomical connectivity-based parcellation results, including the maximum probabilistic maps, probabilistic maps and both the anatomical and functional connectivity patterns, which have been developed in Brainnetome Center, CASIA. The atlas is based on the analysis of connectional architecture with in vivo multi-modal MRI data.

Proper citation: Brainnetome Atlas Viewer (RRID:SCR_014091) Copy   

•   Source: SciCrunch Registry

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

A clinical toolbox useful for normalizing data from individuals with brain injury and/or modalities popular in the clinical environment (CT). It supports either enantiomorphic or lesion-masked normalization. It can be either scripted or used with SPM's simple graphical interface.

Proper citation: Clinical Toolbox for SPM (RRID:SCR_014096) Copy   

•   Source: SciCrunch Registry

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

A software application that calculates and runs a GLM on graph theory properties derived from brain networks. The GLM accepts continuous and categorical between-participant predictors and categorical within-participant predictors. Significance is determined via non-parametric permutation tests. Both fully connected and thresholded networks are tested. The toolbox also provides a data processing path for resting state and (block design) task fMRI data. Options for partialing nuisance signals include local and total white matter signal and PCA of white matter/ventricular signal. For task fMRI, connectivity matrices are computed for each condition by dividing up the timeseries. To compensate for HDR-related delay, the timeseries is deconvolved, allowing for division at the actual onset/offset times.

Proper citation: Graph Theory GLM (GTG) MATLAB Toolbox (RRID:SCR_014075) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/bic-mni-models/

Anatomical brain template library which includes models from ICBM 2009 template.Number of unbiased non-linear averages of MNI152 database have been generated that combines attractions of both high-spatial resolution and signal-to-noise while not being subject to vagaries of any single brain. Procedure involved multiple iterations of process where, at each iteration, individual native MRIs were non-linearly fitted to the average template from previous iteration, beginning with MNI152 linear template.

Proper citation: bic-mni-models (RRID:SCR_014087) Copy   

•   Source: SciCrunch Registry

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

A tool for visualizing displacement fields estimated in association with image registration. Based on the displacement vector field, a mesh is generated for visualization. The mesh can be color mapped with the jacobian determinant at each point for better localization of regions that undergo compression or expansion. Other key features include: view synchronization, adjustable mesh resolution, and conversion from deformation and HAMMER displacement fields.

Proper citation: Displacement Field Viewer (RRID:SCR_014101) Copy   

•   Source: SciCrunch Registry

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

Portal for platforms available to conduct and compete in challenges aiming to improve scientific progress. Challenges allow researchers to share their research and problems with other subject matter experts for collaborative progress.

Proper citation: Challenge Competitions Collection (RRID:SCR_015650) Copy   

•   Source: SciCrunch Registry