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http://www.nitrc.org/projects/segadapter/
An open source learning-based software that automatically learns how to transfer the output of a host segmentation tool closer to the user's manual segmentation using the image data and manual segmentation provided by the user. The motivation of this project is to bridge the gap between the segmentation tool developer and the tool users such that the existing segmentation tools can more effectively serve the community. More and more automatic segmentation tools are publicly available to today's researchers. However, when applied by their end-users, these segmentation tools usually can not achieve the performance that the tool developer reported. Discrepancies between the tool developer and its users in manual segmentation protocols and imaging modalities are the main reasons for such inconsistency.
Proper citation: Automatic Segmentation Tool Adapter (RRID:SCR_002481) Copy
http://www.nitrc.org/projects/pestica/
Software tool to detect physiologic signals from the data itself as well as an adaptive physiologic noise removal tool (Impulse Response Function or IRF-RETROICOR) that zooms in on noise with only 6 regressors, getting all the noise that 5th order RETROICOR gets. These tools will allow you to correct your data for physiologic noise with what you currently have. These signals are equivalent to a parallel monitored pulse signal and a respiratory chest-bellows signal. Do you have 3D+time EPI data (BOLD or perfusion) but no usable physio signals for pulse and respiration? Are you concerned about the effect of physio noise on your data but don't know what to do but regress data-derived signals that mix unknown functional signal with possible physio noise signal? Are you concerned about the number of regressors you're incorporating once you add 5th order RETROICOR (20 more regressors!)? This is for you.
Proper citation: PESTICA fMRI Physio Detection/Correction (RRID:SCR_002513) Copy
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
http://openmeeg.gforge.inria.fr
A C++ package for low-frequency bio-electromagnetism solving forward problems in the field of EEG and MEG with very high accuracy.
Proper citation: OpenMEEG (RRID:SCR_002510) Copy
http://www.crl.med.harvard.edu/software/STAPLE/index.php
An algorithm for the Simultaneous Truth and Performance Level Estimation, which estimates a reference standard and segmentation generator performance from a set of segmentations. It has been widely applied for the validation of image segmentation algorithms, and to compare the performance of different algorithms and experts. It has also found application in the identification of a consensus segmentation, by combination of the output of a group of segmentation algorithms, and for segmentation by registration and template fusion., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: STAPLE (RRID:SCR_002590) Copy
A package for writing fMRI analysis pipelines and interfacing with external analysis packages (SPM, FSL, AFNI). Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. However, this has resulted in a heterogeneous collection of specialized applications without transparent interoperability or a uniform operating interface. Nipype, an open-source, community-developed initiative under the umbrella of Nipy, is a Python project that solves these issues by providing a uniform interface to existing neuroimaging software and by facilitating interaction between these packages within a single workflow. Nipype provides an environment that encourages interactive exploration of algorithms from different packages (e.g., SPM, FSL), eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages. Nipype is creating a collaborative platform for neuroimaging software development in a high-level language and addressing limitations of existing pipeline systems.
Proper citation: Nipype (RRID:SCR_002502) Copy
Software tool as a cross-platform NIfTI format image viewer. Used for viewing and exporting of brain images. MRIcroGL is a variant of MRIcron.
Proper citation: MRIcron (RRID:SCR_002403) Copy
Web application search tool intended to help users find MRI data shared publicly on the Web, particularly from projects organized under the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI). Users can perform queries visually to select a cohort of participants with brain imaging data based on their demographics and phenotypic information and then link out to imaging measures.
Proper citation: MetaSearch (RRID:SCR_014751) Copy
http://www.nitrc.org/projects/striatalvoimap/
An atlas intended to provide accurate data in terms of specific uptake location to make the BP quantitation. The VOIs were manually drawn with software Analyze 9.0 (Mayo Clinic) in 18F-DOPA brain image after spatial normalization with a 18F-DOPA Template. Each striatum was divided into 6 sub-regions: ventral caudate, anterior dorsal caudate, posterior dorsal caudate, ventral putamen, anterior dorsal putamen and posterior dorsal putamen.
Proper citation: Striatal Subregional VOImap (RRID:SCR_014173) Copy
http://www.nitrc.org/projects/cmap/
The Brain Coactivation Map describes all the coactivation networks in the human brain based on the meta-analysis of more than 5,400 neuroimaging articles (from NeuroSynth) containing more than 16,000 individual experiments. The map can be browsed interactively (CoactivationMap.app on GitHub) or queried from a shell using a command line tool (cmtool on GitHub).
Proper citation: Brain Coactivation Map (RRID:SCR_014172) Copy
http://www.nitrc.org/projects/pcp/
A project which systematically preprocess the data from the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) and openly share the results. Data is currently hosted in an Amazon Web Services Public S3 Bucket and at NITRC.
Proper citation: Preprocessed Connectomes Project (RRID:SCR_014162) Copy
http://www.nitrc.org/projects/pd3/
THIS RESOURCE IS NO LONGER IN SERVICE, documented Jan. 5, 2016. Tools will be available for biomedical data mining and visualization as well as linkages to Google Maps and other online resources.
Proper citation: Parkinsons Disease Discovery Database (RRID:SCR_014160) Copy
http://www.nitrc.org/projects/asdb/
Database as an open science framework with a scientific data extracted from scientific literature about various altered states of consciousness assessed with questionnaires. Used to compare what experiences are elicited by different drugs and non-pharmacological methods that induce altered states to help to understand human consciousness functions. Is listed by Neuroimaging Informatics Tools.
Proper citation: Altered States Database (RRID:SCR_016350) Copy
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
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
A C++ software framework to develop, simulate and run magnetic resonance sequences on different platforms.
Proper citation: Object-Oriented Development Interface for NMR (RRID:SCR_005974) Copy
http://www.biological-networks.org/p/outliers/
Software that performs a morphology-based approach for the automatic identification of outlier neurons based on neuronal tree structures. This tool was used by Zawadzki et al. (2012), who reported on and its application to the NeuroMorpho database. For the analysis, each neuron is represented by a feature vector composed of 20 measurements, which are projected into lower dimensional space with PCA. Bivariate kernel density estimation is then used to obtain a probability distribution for cells. Cells with high probabilities are understood as archetypes, while those with the small probabilities are classified as outliers. Further details about the method and its application in other domains can be found in Costa et al. (2009) and Echtermeyer et al. (2011). This version requires Matlab (Mathworks Inc, Natick, USA) and allows the user to apply the workflow using a graphical user interface.
Proper citation: DONE: Detection of Outlier NEurons (RRID:SCR_005299) Copy
A Monte Carlo (MC) solver for photon migration in 3D turbid media. Different from existing MC software designed for layered (such as MCML) or voxel-based media (such as MMC or tMCimg), MMC can represent a complex domain using a tetrahedral mesh. This not only greatly improves the accuracy of the solutions when modeling objects with smooth/complex boundaries, but also gives an efficient way to sample the problem domain to use less memory. The current version of MMC support multi-threaded programming and can give a almost proportional speed-up when using multiple CPU cores.
Proper citation: Mesh-based Monte Carlo (MMC) (RRID:SCR_006950) Copy
http://cmic.cs.ucl.ac.uk/mig/index.php?n=Tutorial.NODDImatlab
This MATLAB toolbox implements a data fitting routine for Neurite Orientation Dispersion and Density Imaging (NODDI). NODDI is a new diffusion MRI technique for imaging brain tissue microstructure. Compared to DTI, it has the advantage of providing measures of tissue microstructure that are much more direct and hence more specific. It achieves this by adopting the model-based strategy which relates the signals from diffusion MRI to geometric models of tissue microstructure. In contrast to typical model-based techniques, NODDI is much more clinically feasible and can be acquired on standard MR scanners with an imaging time comparable to DTI.
Proper citation: NODDI Matlab Toolbox (RRID:SCR_006826) Copy
http://www.nitrc.org/projects/minc_ex/
A reference MINC set of files that currently includes human head images only of standard modalities. The goal is to build a well curated collection of files that demonstrate the capabilities of MINC
Proper citation: MINC Example files (RRID:SCR_000859) Copy
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