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

shapeAnalysisMANCOVA offers statistical shape analysis based on a parametric boundary description (SPHARM) as the point-based model computing method. The point-based models will be analyzed with the methods here proposed using multivariate analysis of covariance (MANCOVA). Here, the number of variates being tested is the dimensionality of our observations. Each point of these observations is a three dimensional displacement vector from the mean. The number of contrasts is the number of equations involved in the null-hypothesis. In order to encompass varying numbers of variates and contrasts, and to account for independent variables, a matrix computation is performed. This matrix represents the multidimensional aspects of the correlation significance and it can be transformed into a scalar measure by manipulation of its eigenvalues. Details of the methods can be found in its Insight Journal publication: http://hdl.handle.net/10380/3124

Proper citation: shapeAnalysisMANCOVA - SPHARM tools (RRID:SCR_002578) Copy   

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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   

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http://www.egi.com/research-division-geodesic-system-components/eeg-software

A complete software package for working with electroencephalography (EEG) and event-related potential (ERP) data. You can acquire, review, analyze, and now ?see? your participant with synchronized video. Net Station also offers specialized tools and workflow options for both clinical and research applications, allows you to save different combinations of view settings (called workspaces) and helps with your reporting requirements by letting you set up and print custom cover pages. For more specialized work, Net Station also provides an optional electrical source estimation module (GeoSource) and an optional sensor location digitizer (Geodesic Photogrammetry System).

Proper citation: Net Station EEG Software (RRID:SCR_002453) Copy   

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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   

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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   

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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   

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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   

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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   

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http://nipy.org/nipype/

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   

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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   

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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   

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

An effort to provide a set of quasi-probabilistic atlases for established functional ROIs in the human neuroimaging literature. Many atlases exist for various anatomical parcellation schemes, such as the Brodmann areas, the structural atlases, tissue segmentation atlases, etc. To date, however, there is no atlas for so-called functional ROIs. Such fROIs are typically associated with an anatomical label of some kind (e.g. the _fusiform_ face area), but these labels are only approximate and can be misleading inasmuch as fROIs are not constrained by anatomical landmarks, whether cytoarchitectonic or based on sulcal and gyral landmarks. The goal of this project is to provide quasi-probabilistic atlases for fROIs that are based on published coordinates in the neuroimaging literature. This is an open-ended enterprise and the atlas can grow as needed. Members of the neuroscience and neuroimaging community interested in contributing to the project are encouraged to do so.

Proper citation: Functional ROI Atlas (RRID:SCR_009481) Copy   

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

This atlas takes advantage of ultra-high resolution 7T MRI to provide unprecedented levels of detail on structures of the basal ganglia in-vivo. The atlas includes probability maps of the Subthalamic Nucleus (STh) using T2*-imaging. For now it has been created on 13 young healthy participants with a mean age of 24.38 (range: 22-28, SD: 2.36). We recently also created atlas STh probability maps from 8 middle-aged participants with a mean age of 50.67 (range: 40-59, SD: 6.63), and 9 elderly participants with a mean age of 72.33 (range: 67-77, SD: 2.87). You can find more details about the creation of these maps in the following papers: Young: http://www.ncbi.nlm.nih.gov/pubmed/22227131 Middle-aged & Elderly: http://www.ncbi.nlm.nih.gov/pubmed/23486960 Participating institutions are the Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, and the Cognitive Science Center Amsterdam, University of Amsterdam, the Netherlands.

Proper citation: Atlasing of the basal ganglia (RRID:SCR_009431) Copy   

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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   

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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   

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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   

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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   

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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   

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

Project hosting binary packaged distributions, scripts, example datasets, and corresponding results of analysis using their UNC/Utah NAMIC DTI Fiber Analysis Framework. This project can be seens as a master project encompassing several current NITRC projects into a coherent set. Their workflow utilizes tools already available on NITRC including: * DTIPrep * DTIAtlasBuilder * FiberViewerLight * DTIAtlasFiberAnalyzer * FADTTS

Proper citation: UNC/Utah NAMIC DTI Fiber Analysis Framework (RRID:SCR_009615) Copy   

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http://www.brainvoyager.com/products/brainvoyagerqx.html

Commercial neuroimaging software package for multi-modal data analysis and management. It has been programmed in C++ with efficient statistical, numerical, and image processing routines. It supports parallelized basic math routines on all platforms and uses modern multi-core, multi-processor hardware for demanding computational routines.

Proper citation: BrainVoyager (RRID:SCR_013057) Copy   

•   Source: SciCrunch Registry