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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://www.nitrc.org/projects/vini/

Software Python tool as viewer for MRI data and numpy arrays.

Proper citation: vini: A viewer for fMRI data (RRID:SCR_017250) Copy   

•   Source: SciCrunch Registry

https://github.com/Neural-Systems-at-UIO/MeshView-for-Brain-Atlases

Web application for real time 3D display of surface mesh data representing structural parcellations and generation of user defined cut planes from volumetric atlases.

Proper citation: MeshView (RRID:SCR_017222) Copy   

•   Source: SciCrunch Registry

http://trackvis.org/dtk/

Software as set of commandline tools with GUI frontend that performs data reconstruction and fiber tracking on diffusion MR images. It does preparation work for TrackVis. Software Package for diffusion imaging data processing and tractography.

Proper citation: Diffusion Toolkit (RRID:SCR_017345) Copy   

•   Source: SciCrunch Registry

https://github.com/bheAI/MonkeyCBP_CLI

Software toolbox for connectivity based parcellation of monkey brain. Integrated pipeline realizing tractography based brain parcellation with automatic processing and massive parallel computing. Highly automated process and high throughput performance supported by GPU option makes toolbox ready to be used by research community.

Proper citation: MonkeyCBP (RRID:SCR_017640) Copy   

•   Source: SciCrunch Registry

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

Software tool as deep neural network for predicting FreeSurfer segmentations of structural MRI volumes. This tool is implemented as both Docker and Singularity containers. Used for brain parcellation and uncertainty estimation.

Proper citation: Knowing what you know (kwyk) - Bayesian Brain Parcellation (RRID:SCR_017470) Copy   

•   Source: SciCrunch Registry

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

Software tool to simplify creation and management of computing environments in Neuroimaging.

Proper citation: ReproMan (RRID:SCR_018468) 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

https://scicrunch.org/scicrunch/data/source/nlx_154697-10/search?q=*&l=

A virtual database currently indexing software and tools from the SciCrunch Registry, Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC), Visiome Platform, Cerebellar Platform, Brain Machine Interface Platform, and Genetic Analysis Software (GAS).

Proper citation: Integrated Software (RRID:SCR_004745) Copy   

•   Source: SciCrunch Registry

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

A multi-day event hosted by the Organization for Human Brain Mapping which features collaborative and open neuroscience projects in data analysis and methods development. Locations change annually.

Proper citation: HBM Hackathon (RRID:SCR_014119) Copy   

•   Source: SciCrunch Registry

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

An international symposium held initially to assess the new technology and innovation in the various established fields of genetics and imaging, and to facilitate the transdisciplinary fusion needed to optimize the development of the emerging field of Imaging Genetics. This annual conference features presentations from investigators world-wide and places emphasis on facilitating in-depth discussions among the participants and presenters.

Proper citation: International Imaging Genetics Conference (RRID:SCR_014125) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/mni2orfromxyz/

Input either normalized MNI coordinates from a 3D image, or input real world XYZ matrix coordinates, and this code will convert coordinates of one type to the other.

Proper citation: Convert MNI coordinates to or from XYZ (RRID:SCR_000406) Copy   

•   Source: SciCrunch Registry

http://physics.ucsd.edu/neurophysics/links.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 16,2023. Software suite for custom-built multiphoton microscopes available as freeware for the Wintel platform. The MPScope package features the acquisition software MPScan, analysis program MPView and several software utilities.

Proper citation: MPScope (RRID:SCR_000065) Copy   

•   Source: SciCrunch Registry

http://ccb.loni.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 31, 2022. Center focused on the development of computational biological atlases of different populations, subjects, modalities, and spatio-temporal scales with 3 types of resources: (1) Stand-alone computational software tools (image and volume processing, analysis, visualization, graphical workflow environments). (2) Infrastructure Resources (Databases, computational Grid, services). (3) Web-services (web-accessible resources for processing, validation and exploration of multimodal/multichannel data including clinical data, imaging data, genetics data and phenotypic data). The CCB develops novel mathematical, computational, and engineering approaches to map biological form and function in health and disease. CCB computational tools integrate neuroimaging, genetic, clinical, and other relevant data to enable the detailed exploration of distinct spatial and temporal biological characteristics. Generalizable mathematical approaches are developed and deployed using Grid computing to create practical biological atlases that describe spatiotemporal change in biological systems. The efforts of CCB make possible discovery-oriented science and the accumulation of new biological knowledge. The Center has been divided into cores organized as follows: - Core 1 is focused on mathematical and computational research. Core 2 is involved in the development of tools to be used by Core 3. Core 3 is composed of the driving biological projects; Mapping Genomic Function, Mapping Biological Structure, and Mapping Brain Phenotype. - Cores 4 - 7 provide the infrastructure for joint structure within the Center as well as the development of new approaches and procedures to augment the research and development of Cores 1-3. These cores are: (4)Infrastructure and Resources, (5) Education and Training, (6) Dissemination, and (7) Administration and Management. The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

Proper citation: Center for Computational Biology at UCLA (RRID:SCR_000334) Copy   

•   Source: SciCrunch Registry

https://www.rad.upenn.edu/sbia/software/index.html#hammer

Software package that performs high-dimensional warping of brain images. Standard voxel-based analysis can be applied to these tissue density maps, in order to examine regional volumetrics, effects of disease, or correlations with clinical measurements. In order to make HAMMER as robust as possible to different acquisition protocols and conditions, they provide a distribution that assumes that images have been skull-stripped and segmented into gray matter, white matter, and ventricular CSF. We have other software tools that can perform these steps, including skull stripping, reorientation and reslicing, and segmentation tools. Importantly, they use 250 for WM, 150 for GM, 50 for Ventricles and 10 for CSF in the tissue-segmented brain images. Current modules used for group analysis: Labeling subject brain using a manually-labeled brain Model; Generating RAVENS map for each tissue (WM, GM, VN); Normalizing subject brain images

Proper citation: Hierarchical Attribute Matching Mechanism for Elastic Registration (RRID:SCR_001960) Copy   

•   Source: SciCrunch Registry

http://www.math.mcgill.ca/keith/fmristat/

A Matlab toolbox for the statistical analysis of fMRI data. The fMRI data was first converted to percentage of whole volume. The statistical analysis of the percentages was based on a linear model with correlated errors. The design matrix of the linear model was first convolved with a hemodynamic response function modelled as a difference of two gamma functions timed to coincide with the acquisition of each slice. Temporal drift was removed by adding a cubic spline in the frame times to the design matrix (one covariate per 2 minutes of scan time), and spatial drift was removed by adding a covariate in the whole volume average. The correlation structure was modelled as an autoregressive process of degree 1. At each voxel, the autocorrelation parameter was estimated from the least squares residuals using the Yule-Walker equations, after a bias correction for correlations induced by the linear model. The autocorrelation parameter was first regularized by spatial smoothing, then used to "whiten" the data and the design matrix. The linear model was then re-estimated using least squares on the whitened data to produce estimates of effects and their standard errors. In a second step, runs, sessions and subjects were combined using a mixed effects linear model for the effects (as data) with fixed effects standard deviations taken from the previous analysis. This was fitted using ReML implemented by the EM algorithm. A random effects analysis was performed by first estimating the the ratio of the random effects variance to the fixed effects variance, then regularizing this ratio by spatial smoothing with a Gaussian filter. The variance of the effect was then estimated by the smoothed ratio multiplied by the fixed effects variance. The amount of smoothing was chosen to achieve 100 effective degrees of freedom. The resulting T statistic images were thresholded using the minimum given by a Bonferroni correction and random field theory, taking into account the non-isotropic spatial correlation of the errors.

Proper citation: FMRISTAT - A general statistical analysis for fMRI data (RRID:SCR_001830) Copy   

•   Source: SciCrunch Registry

http://connectir.projects.nitrc.org

An R-based package to conduct brain connectivity analyses with a focus on a novel approach to conducting Connectome-Wide Association Studies (CWAS) using functional connectivity.

Proper citation: Connectir (RRID:SCR_000863) Copy   

•   Source: SciCrunch Registry

http://www.egi.com/clinical-division/clinical-division-geodesic-eeg-components/clinical-division-net-station

APIs for Net Station data files. APIs are available for C++, C#, and Java.

Proper citation: Net Station API (RRID:SCR_000867) Copy   

•   Source: SciCrunch Registry

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

This matlab script and associated files will take resultant statistical images and essentially output everything you could ever want to know. It can work off of images that were previously corrected for multiple comparisons, but it can actually do the correction itself. This is because the cluster_correct script is incorporated within. It will iterate through atlases (borrowed from other software) to tell you the location of significant results. It outputs an extremely detailed report as well as a summary table for quick investigation. In addition, it will output statistics for each surviving cluster, and the image as a whole. Feedback would be much appreciated.

Proper citation: Cluster reporter (RRID:SCR_002218) Copy   

•   Source: SciCrunch Registry

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

Software for the analysis of multiple diffusion properties along fiber bundle as functions in an infinite dimensional space and their association with a set of covariates of interest, such as age, diagnostic status and gender, in real applications. The resulting analysis pipeline can be used for understanding normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles.

Proper citation: Functional Regression Analysis of DTI Tract Statistics (RRID:SCR_002293) Copy   

•   Source: SciCrunch Registry